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Making a Risk Management Plan for Your Business
It’s impossible to eliminate all business risk. Therefore, it’s essential for having a plan for its management. You’ll be developing one covering compliance, environmental, financial, operational and reputation risk management. These guidelines are for making a risk management plan for your business.
Developing Your Executive Summary
When you start the risk management plan with an executive summary, you’re breaking apart what it will be compromised of into easy to understand chunks. Even though this summary is the project’s high-level overview, the goal is describing the risk management plan’s approach and scope. In doing so, you’re informing all stakeholders regarding what to expect when they’re reviewing these plans so that they can set their expectations appropriately.
Who Are the Stakeholders and What Potential Problems Need Identifying?
During this phase of making the risk management plan, you’re going to need to have a team meeting. Every member of the team must be vocal regarding what they believe could be potential problems or risks. Stakeholders should also be involved in this meeting as well to help you collect ideas regarding what could become a potential risk. All who are participating should look at past projects, what went wrong, what is going wrong in current projects and what everyone hopes to achieve from what they learned from these experiences. During this session, you’ll be creating a sample risk management plan that begins to outline risk management standards and risk management strategies.
Evaluate the Potential Risks Identified
A myriad of internal and external sources can pose as risks including commercial, management and technical, for example. When you’re identifying what these potential risks are and have your list complete, the next step is organizing it according to importance and likelihood. Categorize each risk according to how it could impact your project. For example, does the risk threaten to throw off timelines or budgets? Using a risk breakdown structure is an effective way to help ensure all potential risks are effectively categorized and considered. Use of this risk management plan template keeps everything organized and paints a clear picture of everything you’re identifying.
Assign Ownership and Create Responses
It’s essential to ensure a team member is overseeing each potential risk. That way, they can jump into action should an issue occur. Those who are assigned a risk, as well as the project manager, should work as a team to develop responses before problems arise. That way, if there are issues, the person overseeing the risk can refer to the response that was predetermined.
Have a System for Monitoring
Having effective risk management companies plans includes having a system for monitoring. It’s not wise to develop a security risk management or compliance risk management plan, for example, without having a system for monitoring. What this means is there’s a system for monitoring in place to ensure risk doesn’t occur until the project is finished. In doing so, you’re ensuring no new risks will potentially surface. If one does, like during the IT risk management process, for example, your team will know how to react.
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A Sample Big Data Mining and Analytics Business Plan Template
Are you about starting a data mining & analytics business? If YES, here is a complete sample data mining & analytics business plan template & feasibility report you can use for FREE . Okay, so we have considered all the requirements for starting a data mining & analytics business. We also took it further by analyzing and drafting a sample data mining & analytics marketing plan template backed up by actionable guerrilla marketing ideas for data mining & analytics businesses. So let’s proceed to the business planning section.
Why Start a Big Data Mining & Analytics Business?
If you are a data enthusiast and you are looking for a research based consulting business to start, then one of your options is to start a data mining and analytics business.
As a data mining and analytics company, your responsibility is to develop software for data mining. Data mining is the process of extracting patterns from large data sets. The truth is that this industry is highly profitable because every business would want to increase sales and make profit. Your level of profitability is dependent on your ability to come up with useful data that will help your clients experience growth in their business.
If you are sure this type of business is what you want to do after you must have conducted your feasibility studies and market research, then the next step to follow is to write a good business plan; a detailed blue print of how you intend raising your seed capital, setting up the business, managing the flow of the business, sorting out tax and marketing your products amongst others.
The truth is that it is one thing to have a fantastic idea cum business plan; it is entirely another thing for the business plan to translate to money (profits) that is why it is important to assemble a team of dedicated workers to work with if you want to be successful with your business.
Below is a sample data mining and analytics company business plan template that can help you to successfully write your own with little or no difficulty.
A Sample Big Data Mining & Analytics Business Plan Template
1. industry overview.
The data mining and analytics industry is made up of organizations that systematically gather, record, tabulate and present relevant data for the purpose of finding anomalies, patterns and correlations within large data sets to predict outcomes. With the aid of the right data, an organization can make use of a broad range of techniques to increase revenue, cut cost, improve customer relationship, and reduce risk amongst others.
The usefulness of data mining has increase in recent time. As a matter of fact, the recent explosion in large-scale high-resolution data enables managers to ask and answer questions regarding businesses and consumers at a whole new level. Business owners are faced with data about businesses and consumers that are growing faster than they can be utilized.
Data mining enables businesses to extract useful consumer behavior and preferences from seemingly tremendous and unorganized data, which then can be utilized for data-driven decision-making and Competitive Advantage. Data mining and analytics are useful in e-commerce, sales, marketing, finance, operations, education et al.
If you keep a close watch of happening in the data and analytics industry, you will notice that the industry has experienced slow growth over the past five years. As corporate profit began to rise, businesses emerged from their cost-conscious slump and began to develop new software and increase investment.
The Data Mining and Analytics industry is indeed a fast growing industry in Germany, United Kingdom, Switzerland, China, Japan, India and the united states of America. IBM, HP, SAS and StatSoft are the leaders in this industry and they can boast of having the lion share of the available market in the United States.
Every year, HP conducts approximately 2.5 billion interactions via customer calls, website visits, emails and chat sessions, and has even more touchpoints through retail partners. The result is a 900TB data warehouse with 360 million customer records, growing by millions each month. HP’s goal was clear: find meaningful value in all that data, and achieve a 360-degree view of its customers in order to be more responsive and competitive.
It is fact that data mining is a productive business strategy and with powerful data mining analytics, HP was able to accurately score more than 100 million customers in seconds to target its marketing and service efforts. As a result, HP has seen a 20 percent incremental ROI across campaigns.
Orders shipped have increased by 50 percent in three years, and the overall operating profit of the HPDirect.com store has increased by more than 50 percent. Over and above, a data mining and analytics company is one good business to start if you have the expertise and capacity to reach out to your target market and of course you know how to produce results for your clients.
2. Executive Summary
Binary Solutions® Data Mining & Analytics Company, Inc. is a licensed data mining and analytics company that will be located in the heart of New York City – New York. We have been able to acquire a standard office facility that is highly suitable for the kind of business we want to operate.
Binary Solutions® Data Mining & Analytics Company, Inc. is in the data mining and analytics industry to provide several services to clients in different sectors of the economy. We will develop products such as predictive analytics software, statistical analysis software and data mining software with the aim of finding anomalies, patterns and correlations within large data sets to predict outcomes.
We are geared towards helping our clients sift through all the chaotic and repetitive noise in their data, help them understand what is relevant and then make good use of that information to assess likely outcomes and also assist them in accelerating the pace of making informed decisions. We are well trained and equipped to service the market segments that require the services we will be offering.
We are in the data mining and analytics line of business to deliver excellent result oriented services to all those who will patronize our services. We will also ensure that in the line of carrying out our duty, we comply with the laws and regulations governing businesses in New York City and the United States of America. Our employees are well trained and qualified to handle the wide range of marketing research services.
At Binary Solutions® Data Mining & Analytics Company, Inc., our clients’ overall best interest would always come first, and everything we do is guided by our values and professional ethics. We will ensure that we hire professional and certified data miners and analysts with various skills set who are well experienced and passionate in helping our clients achieve their business goals within record time.
Binary Solutions® Data Mining & Analytics Company, Inc. will at all times demonstrate her commitment to sustainability, by actively participating in our communities and integrating sustainable business practices wherever possible. We will ensure that we hold ourselves accountable to the highest standards by meeting our clients’ needs precisely and completely.
Binary Solutions® Data Mining & Analytics Company, Inc. is a family business that is owned and managed by Nelson Borough and his immediate family members. Nelson Borough is going to be the Chief Researcher of the organization.
He is a qualified and well trained Data Miner and Analyst with a Degree in Math and Statistics and with over 10 years’ experience working for some of the leading brands in the data mining and analytics industry in the United States of America.
3. Our Products and Services
Binary Solutions® Data Mining & Analytics Company, Inc. is in the data mining and analytics line of business to offer varieties of services within the scope of the data mining and analytics industry in the United States of America.
Our intention of starting our data mining and analytics company is to become one of the leading brands in the industry and of course to also make profits and we will do all that is permitted by the law in the US to achieve our aim and business goal.
These are the services that Binary Solutions® Data Mining & Analytics Company, Inc. will be offering;
- Develop predictive analytics software
- Develop statistical analysis software
- Develop data mining software
- Use data to help our clients increase revenue, cut cost, improve customer relationship, reduce risk and more.
- Other data mining and analytics related research services
4. Our Mission and Vision Statement
- Our vision is to establish a standard data mining and analytics company whose services and brand will not only be accepted in the United States of America, but also in other parts of the world.
- Our mission is to provide result oriented data mining and analytics services and other related services that will assist businesses, individuals and non-profit organizations in developing the models that can uncover connections within millions or billions of records.
- We want to build a data mining and analytics company that can favorably compete with other leading brands in the data mining and analytics industry.
Our Business Structure
From the outset, we have decided to recruit only qualified professionals (data miners and analysts) to man various job positions in our organization. We are quite aware of the rules and regulations governing the data mining and analytics industry which is why we decided to recruit only qualified employees as foundation staff of the organization.
We hope to leverage on their expertise to build our brand. When hiring, we will look out for applicants that are not just qualified and experienced, but honest, customer centric and are ready to work to help us build a prosperous business that will benefit all the stake holders.
As a matter of fact, profit-sharing arrangement will be made available to all our management staff and it will be based on their performance for a period of five years or more. In view of the above, we have decided to hire qualified and competent hands to occupy the following positions;
- Chief Executive Officer
Data Miner/Data Analyst
Software Developer
Office Administrator
- Marketing Executives
Client Service Executive
5. Job Roles and Responsibilities
Chief Executive Officer:
- Increases management’s effectiveness by recruiting, selecting, orienting, training, coaching, counseling, and disciplining managers; communicating values, strategies, and objectives; assigning accountabilities; planning, monitoring, and appraising job results; developing incentives; developing a climate for offering information and opinions; providing educational opportunities.
- Generates, interconnects, and implements the organization’s vision, mission, and overall direction – i.e. leading the development and implementation of the overall organization’s strategy.
- Accountable for fixing prices and signing business deals
- Responsible for providing direction for the business
- Responsible for signing checks and documents on behalf of the company
- Evaluates the success of the organization
- Develops records, management processes and policies
- Identifies areas to increase efficiency and automation of processes
- Sets up and maintain automated data processes
- Identifies, evaluates and implements external services and tools to support data validation and cleansing
- Produces and tracks key performance indicators
- Develops and supports reporting processes
- Monitors and audits data quality
- Liaises with internal and external clients to fully understand data content
- Gathers, understands and documents detailed business requirements using appropriate tools and techniques
- Designs and carries out surveys and analyze survey data
- Prepares reports for internal and external audience using business analytics reporting tools
- Creates data dashboards, graphs and visualizations
- Provides sector and competitor benchmarking
- Mines and analyzes large datasets, draw valid inferences and present them successfully to management using a reporting tool.
- Responsible for developing predictive analytics software, statistical analysis software and data mining software et al
- Keeps up with the latest software and happenings in the industry
- Handles any other duty as assigned by the CEO
- Responsible for overseeing the smooth running of HR and administrative tasks for the organization
- Designs job descriptions with KPI to drive performance management for psychologists, social workers and marriage counselors
- Regularly holds meetings with key stakeholders to review the effectiveness of the organizations’ Policies, Procedures and Processes
- Maintains office supplies by checking stocks; placing and expediting orders; evaluating new products.
- Ensures operation of equipment by completing preventive maintenance requirements; calling for repairs.
- Defines job positions for recruitment and managing interviewing process
- Carries out staff induction for new team members
- Responsible for training, evaluation and assessment of employees
- Responsible for arranging travel, meetings and appointments
- Oversees the smooth running of the daily activities for the organization.
Marketing Executive
- Recognizes, arranges, and reaches out to new clients, and business opportunities et al
- Identifies development opportunities; follows up on development leads and contacts; participates in the structuring and financing of projects; assures the completion of development projects.
- Writes winning proposal documents, negotiate fees and rates in line with organizations’ policy
- Responsible for handling business research, market surveys and feasibility studies for clients
- Responsible for supervising implementation, advocate for the customer’s needs, and communicate with clients
- Develops, executes and evaluates new plans for expanding sales
- Documents all customer contact and information
- Helps to increase sales and growth for the organization
- Responsible for preparing financial reports, budgets, and financial statements for the organization
- Provides managements with financial analyses, development budgets, and accounting reports; analyzes financial feasibility for the most complex proposed projects; conducts market research to forecast trends and business conditions.
- Responsible for financial forecasting and risks analysis.
- Performs cash management, general ledger accounting, and financial reporting for one or more properties.
- Responsible for developing and managing financial systems and policies
- Responsible for administering payrolls
- Ensures compliance with taxation legislation
- Handles all financial transactions for the organization
- Serves as internal auditor for the organization.
- Welcomes clients and visitors by greeting them in person or on the telephone; answering or directing inquiries.
- Ensures that all contacts with clients (e-mail, walk-In center, SMS or phone) provides the client with a personalized customer service experience of the highest level
- Through interaction with clients on the phone, uses every opportunity to build client’s interest in the company’s products and services
- Manages administrative duties assigned by the principal partners in an effective and timely manner
- Consistently stays abreast of any new information on the organizations’ services, promotional campaigns etc. to ensure accurate and helpful information is supplied to clients when they make enquiries
- Receives parcels/documents for the organization.
6. SWOT Analysis
Binary Solutions® Data Mining & Analytics Company, Inc. is set to become one of the leading data mining and analytics companies in New York City – New York which is why we are willing to take our time to cross every ‘Ts’ and dot every ‘Is’ as it relates to our business.
We know that if we are going to achieve the goals that we have set for our business, then we must ensure that we build our business on a solid foundation. We must ensure that we follow due process in setting up the business.
Even though our Chief Executive Officer has a robust experience in the data mining and analytics industry, we still went ahead to hire the services of business consultants that are specialized in setting up new businesses to help our organization conduct a detailed SWOT analysis.
This is the summary of the SWOT analysis that was conducted for Binary Solutions® Data Mining & Analytics Company, Inc.;
Our core strength lies in the ability to quickly adopt new technology, access to skilled and flexible workforce and provision of relevant results. Also, we have a team of experts in the industry, a team with excellent qualifications and experience in data mining and analytics.
Aside from the synergy that exists in our carefully selected team members and our strong online presence, Binary Solutions® Data Mining & Analytics Company, Inc. is well positioned in a business community with the right demography and we know we will attract loads of clients from the first day we open our doors for business.
As a new data mining and analytics company in New York, it might take some time for our organization to break into the market and gain acceptance especially from top profile clients in the fast – growing data mining and analytics industry; that is perhaps our major weakness.
- Opportunities:
The opportunities that are available to data mining and analytics companies are unlimited considering the fact that the data mining and analytics industry is a dynamic industry. Every organization that has products and services to sell would always look for useful data and strategies that will help them reach out to their target market hence the need for the services of data mining and analytics companies.
Just like any other business, one of the major threats that we are likely going to face is economic downturn. It is a fact that economic downturn affects purchasing/spending power. Another threat that may likely confront us is the arrival of a data mining and analytics company in same location where our target market exists and who may want to adopt same Business model like us.
7. MARKET ANALYSIS
- Market Trends
The trends in the data mining and analytics industry shows that over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights.
Retailers, banks, manufacturers, telecommunications providers and insurers, among others, are using data mining to discover relationships among everything from pricing, promotions and demographics to how the economy, risk, competition and social media are affecting their business models, revenues, operations and customer relationships.
Also, the process of mining data to discover hidden connections and predict future trends has a long history. The term “data mining” wasn’t coined until the 1990s.
But its foundation comprises three intertwined scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and/or machines) and machine learning (algorithms that can learn from data to make predictions).
What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power. Lastly, the data mining and analytics industry will continue to evolve due to the advancement of computer technology and software application designs.
8. Our Target Market
Binary Solutions® Data Mining & Analytics Company, Inc. is in business to service a wide range of customers in New York City and all across the United States of America. We will ensure that we target both private companies and government organizations.
The fact that we are going to open our doors to a wide range of customers does not in any way stop us from abiding by the rules and regulations governing the data mining and analytics industry in the United States.
In view of that, we have created strategies that will enable us reach out to various corporate organizations and individuals who we know can’t afford to do without our services. We have conducted our market research and survey and we will ensure that we meet and surpass the expectations of our clients. Below is a list of the people and organizations that we have specifically market our services to;
- Banks, Insurance Companies and other related Financial Institutions
- Blue Chips Companies
- Corporate Organizations
- Retail Companies
- Manufacturers and Distributors
- Real Estate Owners, Developers, and Contractors
- Research and Development Companies
- The Government (Public Sector)
- Schools (High Schools, Colleges and Universities)
- Sport Organizations
- Religious Organizations
- Political Parties
- Television Stations
- Printing Press (Publishing Houses) and Authors
- Branding and Advertising agencies
- Entrepreneurs and Startups
Our Competitive Advantage
We are aware that to be highly competitive in the data mining and analytics industry means that we should be able to develop models that can uncover connections within millions or billions of records; increase revenue, cut costs, improve customer relationships, reduce risks and more.
Binary Solutions® Data Mining & Analytics Company, Inc. is coming into the market well prepared to favorably compete in the industry. We will ensure our pricing policy is appropriate, plus the fact that we having a good reputation and also economies of scale.
Our staff are well groomed in all aspects of data mining and analytics and all our employees are trained to provide customized customer service to all our clients. Our services will be carried out by highly trained professionals who know what it takes to give our highly esteemed customers value for their money.
Lastly, all our employees will be well taken care of, and their welfare package will be among the best within our category in the industry. It will enable them to be more than willing to build the business with us and help deliver our set goals and achieve all our business aims and objectives.
9. SALES AND MARKETING STRATEGY
- Sources of Income
Binary Solutions® Data Mining & Analytics Company, Inc. will ensure that we do all we can to maximize the business by generating income from every legal means within the scope of our industry. Below are the sources we intend exploring to generate income for Binary Solutions® Data Mining & Analytics Company, Inc.;
- Use data to help our clients increase revenue, cut costs, improve customer relationships, reduce risks and more.
10. Sales Forecast
One thing is certain, there would always be organizations that would need the services of data mining and analytics companies to help them increase sales or generate more revenue.
We are well positioned to take on the available market in the data mining and analytics industry and we are quite optimistic that we will meet our set target of generating enough profits from our first six months of operation and grow our data mining and analytics company to enviable heights.
We have been able to critically examine the data mining and analytics space, we have analyzed our chances in the industry and we have been able to come up with the following sales forecast. Below is the sales projection for Binary Solutions® Data Mining & Analytics Company, Inc.; it is based on the location of our business and of course the wide range of our data mining and analytics services and target market;
- First Year: $250,000
- Second Year: $500,000
- Third Year: $950,000
N.B : This projection was done based on what is obtainable in the industry and with the assumption that there won’t be any major economic meltdown and there won’t be any major competitor offering same services as we do within same location. Please note that the above projection might be lower and at the same time it might be higher.
- Marketing Strategy and Sales Strategy
The marketing and sales strategy adopted by Binary Solutions® Data Mining & Analytics Company, Inc. will be based on generating long-term personalized relationships with customers. In order to achieve that, we will ensure that we offer all – round data mining and analytics services at affordable prices compare to what is obtainable in and around New York City.
All our employees will be well trained and equipped to provide excellent and knowledgeable data mining and analytics services and customer service. We know that if we are consistent with offering high quality and result oriented data mining and analytics, we will increase the number of our customers by more than 25 percent for the first year and then more than 40 percent subsequently.
Before choosing a location for our data mining and analytics company, we conducted a thorough market survey and feasibility studies in order for us to penetrate the available market and become the preferred choice for organizations in New York City – New York and of course in other cities all across the United States of America.
We have detailed information and data that we were able to utilize to structure our business to attract the number of customers we want to attract per time. In summary, Binary Solutions® Data Mining & Analytics Company, Inc. will adopt the following sales and marketing approach to win customers over;
- Introduce our data mining and analytics company by sending introductory letters alongside our brochure to school, corporate organizations, and other key stake holders all across the United States of America.
- Promptness in bidding for data mining and analytics contracts from the government and other cooperate organizations.
- Advertise our business in relevant business magazines, newspapers, TV and radio stations.
- List our business on yellow pages’ ads (local directories).
- Attend relevant international and local expos, seminars, and business fairs et al
- Create different packages for different category of clients in order to work with their budgets and still deliver excellent and result oriented data mining and analytics services.
- Leverage on the internet to promote our business.
- Engage direct marketing approach.
- Encourage word of mouth marketing from loyal and satisfied clients.
11. Publicity and Advertising Strategy
We are in the data mining and analytics business to become one of the market leaders and also to maximize profit, hence we are going to explore all available means to promote our company.
Binary Solutions® Data Mining & Analytics Company, Inc. has a long – term plan of working for clients all across the United States and other parts of the world which is why we will deliberately build our brand to be well accepted in New York City before venturing out.
As a matter of fact, our publicity and advertising strategy is not solely for winning customers over but to effectively communicate our brand to the general public. Here are the platforms we intend leveraging on to promote and advertise Binary Solutions® Data Mining & Analytics Company, Inc.;
- Place adverts on both print (community based newspapers and magazines) and electronic media platforms
- Sponsor relevant community research programs
- Leverage on the internet and social media platforms like Instagram, Facebook, twitter, YouTube, Google + et al to promote our brand
- Install our billboards in strategic locations all around New York City – New York
- Distribute our fliers and handbills in target areas
- Ensure that all our workers wear our branded shirts and all our vehicles are branded with our company’s logo
12. Our Pricing Strategy
Generally, data mining and analytics services are billed on the results produced and flat fees on a weekly or monthly basis as it applies. As a result of this, Binary Solutions® Data Mining & Analytics Company, Inc. will charge our clients flat fees except for few occasions where there will be need for us to charge special clients on hourly basis.
At Binary Solutions® Data Mining & Analytics Company, Inc. we will keep our fees below the average market rate by keeping our overhead low and by collecting payment in advance. In addition, we will also offer special discounted rates to all our clients at regular intervals.
- Payment Options
The payment policy adopted by Binary Solutions® Data Mining & Analytics Company, Inc. is all inclusive because we are quite aware that different customers prefer different payment options as it suits them but at the same time, we will ensure that we abide by the financial rules and regulation of the United States of America.
Here are the payment options that Binary Solutions® Data Mining & Analytics Company, Inc. will make available to her clients;
- Payment via bank transfer
- Payment with cash
- Payment via credit cards
- Payment via online bank transfer
- Payment via check
- Payment via mobile money transfer
- Payment via bank draft
In view of the above, we have chosen banking platforms that will enable our client make payment for our services without any stress on their part. Our bank account numbers will be made available on our website and promotional materials.
13. Startup Expenditure (Budget)
If you are looking towards starting a data mining and analytics company, then you should be ready to ensure that you raise enough capital to cover some of the basic expenditure that you are going to incur. You would need money to secure a standard facility, acquire relevant software apps, pay your workforce and pay bills for a while until the revenue you generate from the business becomes enough to pay them.
The items listed below are the basics that we would need when starting our data mining and analytics company in the United States.
- The total fee for registering the Business in the United States – $750.
- Legal expenses for obtaining licenses and permits – $1,500.
- Marketing promotion expenses for the grand opening of Binary Solutions® Data Mining & Analytics Company, Inc. in the amount of $3,500 and as well as flyer printing (2,000 flyers at $0.04 per copy) for the total amount of – $3,580.
- Cost for hiring Business Consultant – $2,500.
- Cost for computer software (Accounting Software, Payroll Software, CRM Software, Microsoft Office, and QuickBooks Pro et al) – $7,000
- Insurance (general liability, workers’ compensation and property casualty) coverage at a total premium – $3,400.
- Cost for payment of rent for 12 months at $1.76 per square feet in the total amount of – $105,600.
- Cost for facility remodeling (construction of racks and shelves) – $20,000.
- Other start-up expenses including stationery ( $500 ) and phone and utility deposits – ( $2,500 ).
- Operational cost for the first 3 months (salaries of employees, payments of bills et al) – $500,000
- The cost for the purchase of furniture and gadgets (Computers, Printers, Telephone, TVs, tables and chairs et al) – $4,000.
- The cost of launching a website – $700
- Miscellaneous – $10,000
We would need an estimate of two hundred thousand dollars ( $200,000 ) to successfully set up our data mining and analytics business in New York City – New York.
Generating Startup Capital for Binary Solutions® Data Mining & Analytics Company, Inc.
Binary Solutions® Data Mining & Analytics Company, Inc. is a family business that will be owned and managed by Nelson Borough and his immediate family members. They are the financiers of the business, but may likely welcome partners later which is why they decided to restrict the sourcing of the startup capital for the business to just three major sources.
These are the areas we intend generating our startup capital;
- Generate part of the startp capital from personal savings
- Source for soft loans from family members and friends
- Apply for loan from the Bank
N.B: We have been able to generate about $50,000 ( Personal savings $30,000 and soft loan from family members $20,000 ) and we are at the final stages of obtaining a loan facility of $150,000 from our bank. All the papers and documents have been duly signed and submitted, the loan has been approved and any moment from now our account will be credited.
14. Sustainability and Expansion Strategy
The future of a business lies in the number of loyal customers that they have, the capacity and competence of the employees, their investment strategy and the business structure. If all of these factors are missing from a business, then it won’t be too long before the business close shop.
One of our major goals of starting Binary Solutions® Data Mining & Analytics Company, Inc. is to build a business that will survive off its own cash flow without the need for injecting finance from external sources once the business is officially running.
We know that one of the ways of gaining approval and winning customers over is to offer our data mining and analytics services work a little bit cheaper than what is obtainable in the market and we are well prepared to survive on lower profit margin for a while.
Binary Solutions® Data Mining & Analytics Company, Inc. will make sure that the right foundation, structures and processes are put in place to ensure that our staff welfare are well taken of. Our organizations’ corporate culture is designed to drive our business to greater heights and training and retraining of our workforce is at the top burner.
As a matter of fact, profit-sharing arrangement will be made available to all our management staff and it will be based on their performance for a period of three years or more. We know that if that is put in place, we will be able to successfully hire and retain the best hands we can get in the industry; they will be more committed to help us build the business of our dreams.
Check List/Milestone
- Business Name Availability Check: Completed
- Business Registration: Completed
- Opening of Corporate Bank Accounts: Completed
- Securing Point of Sales (POS) Machines: Completed
- Opening Mobile Money Accounts: Completed
- Opening Online Payment Platforms: Completed
- Application and Obtaining Tax Payer’s ID: In Progress
- Application for business license and permit: Completed
- Purchase of Insurance for the Business: Completed
- Leasing of facility and remodeling the facility: In Progress
- Conducting Feasibility Studies: Completed
- Generating capital from family members: Completed
- Applications for loan from the bank: In Progress
- Writing of Business Plan: Completed
- Drafting of Employee’s Handbook: Completed
- Drafting of Contract Documents and other relevant Legal Documents: In Progress
- Design of The Company’s Logo: Completed
- Printing of Promotional Materials: In Progress
- Recruitment of employees: In Progress
- Purchase of the needed furniture, racks, shelves, computers, electronic appliances, office appliances and CCTV: In progress
- Creating Official Website for the Company: In Progress
- Creating Awareness for the business both online and around the community: In Progress
- Health and Safety and Fire Safety Arrangement (License): Secured
- Launching party planning: In Progress
- Compilation of our list of data mining and analytics services areas that will be our strong emphasis: Completed
- Establishing business relationship with corporate organizations, NGOs and other key stakeholders in the United States of America: In Progress.
More on Technology Sector
Data Mining Planning: Successful Data Mining Business Planning
“The most important step in successful data mining is setting your objectives,” says Rob Gerritsen, president of Philadelphia- based data mining consultancy Exclusive Ore, Inc. In particular, objectives must be actionable, and, ultimately, profitable. According to Gerritsen, mined data should “lead you to being able to change the way you do business.”
Gerritsen and Exclusive Ore vice-president Estelle Brand share their secrets on how to prepare for successful data mining projects:
- Make it a team project. In almost every successful data mining project, the end-users, those who will ultimately be using the data, are the impetus for the project. But, others must be involved. “Convince the appropriate people in the organization that it needs to be done,” Gerritsen says, and point out that involvement from groups other than end-users will be profitable for the organization. Keep in mind that the call for a data mining project will come from whichever group in a company has an agenda for using the data, but successful projects are always team efforts between the business users and I.T. department.
- Pick your team based on your goals. Both Gerritsen and Brand stress to “look at what you’re going to do at the deployment stage, since this indicates what people need to be on board the project.” This is completely dependent upon how the data will be used – if the data you mine is going to be used by the sales and engineering departments, involve those people in the early stages of the planning process.
- Don’t forget Team Marketing Department. Having input early on from the marketing department is vital. They view data mining as an important way of being able to target customers, says Gerritsen, hence the marketing department “is the early major adopter of this technology,” and probably will even be the instigators.
- Involving the whole organization prepares it for change. Since successful data mining usually results in changing the way your company does business, well-received and widely used data mining projects involve as many departments as possible. “People who aren’t involved are going to feel completely blindsided,” Brand warns, and probably end up resisting the process at crucial moments.
- Create a game plan. Have the business users and the technology department create a “strategy and tactics” breakdown of the project, Brand says. The overall strategy should explain how the mined data will help the company’s performance – this is attained from the end users. Creating the framework for the tactics, or exactly how the data mining will be done, comes from those within the information technology department.
- Inventory your data. Create and organize an inventory of your preexisting data. Figure out where it is – if it’s already in a warehouse, you’re ahead. This will help you determine what is missing and what isn’t, where it your data lies, who’s responsible for its maintenance, how much of it there really is, and give you an indication if you need to add external data.
- Choose your plays based on your goals. Select your hardware and software based on the organization’s goals and end-usage requirements that are firmly in place ahead of time. In other words, don’t let technology drive you, make technology fit your needs.
Rob Gerritsen and Estelle Brand will be presenting a seminar on this topic June 26th as part of the Database and Client Server World Boston event, and will present the seminar July 26th at the Data Warehouse World Conference in New York.
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A Complete Guide to Data Mining and How to Use It
Published: February 09, 2022
Data mining is one of the most effective ways organizations can make sense of their data. This technique can be extremely valuable to streamline operations, build accurate sales forecasts, increase marketing ROI, provide valuable customer insights, and much more.
Let's talk about what data mining is, some key definitions to keep in mind, common challenges, and how your business can harness its potential safely and ethically.
What is data mining?
Data mining is the process of analyzing big amounts of data to find trends and patterns. It allows you to turn raw, unstructured data into comprehensible insights about various areas of the business. These areas may include sales, marketing, operations, finance, and more.
Any data that has to do with your business can be mined. This data includes but is not limited to:
- Raw number of sales
- Raw number of customers
- Raw number of customers who’ve churned
- Raw number of customers in a certain geographical area
- Marketing spend
- And much, much more
Feeling overwhelmed? That’s understandable. Most businesses wish they could take better advantage of their data to make better, more informed decisions — but that is much easier said than done.
Big data is a veritable gold mine in what it has to offer, but managing, analyzing, and deriving insights from it presents a lot of challenges , too. And when you start learning about data management , you come across all this technical jargon and complex definitions that seem to make it all the more complicated.
That’s where data mining comes in. It takes everything that’s overwhelming about analyzing and managing big data and makes it much more accessible and easier to understand.
How Data Mining Works
Data mining can give you important insights that solve problems, reduce risks and costs, identify market opportunities, improve customer experience, and predict customer behaviors and preferences.
Before we dive into the more tactical aspects of data mining, let’s take a look at the benefits.
Benefits of Data Mining
- It allows you to easily find the most important data.
- It results in faster, automated decision-making.
- It helps your team work more efficiently.
- It helps you gather accurate data about your customers.
- It helps you increase revenue.
When done well, data mining can bring a significant advantage by providing business intelligence you wouldn't otherwise have access to. It also gives you insights in a much more relevant and timely manner. Some of the benefits of data mining include:
1. It allows you to easily find the most important data.
Big data has some really useful information in it, but there's also a lot you don't need and that would hinder analyses rather than help. Data mining allows you to automatically tell the valuable information apart and construe it into actionable reports.
If you’re using a tool such as Operations Hub to track your data, you often don’t have to look at the raw numbers at all or create reports from scratch each time. Instead, you can find your most pertinent data each time you access the tool, negating the need to export and compile spreadsheet after spreadsheet of raw numbers.
2. It results in faster, automated decision-making.
Instead of needing a person to review everything and decide on a course of action, you can automate certain decisions. For example, banks can use software to identify data trends that look like fraudulent behavior and automatically block accounts within seconds, notify a responsible individual, or request additional verification from users.
Even if you have a person manually reviewing the data, you can speed up the decision-making process by having data mining processes in place that turn the big data into more digestible fragments.
3. It helps your team work more efficiently.
Imagine having your sales team review a 100-tab spreadsheet every time they want to find the number of customers in a certain industry. Data mining takes all of this manual work out of the equation by providing a way for salespeople to find this information without wading through rows and rows of big data.
There are hundreds of use cases where data mining will serve both managers and individual contributors in a team. If your job is to find patterns and trends in a data set, data mining will help you do that effortlessly.
4. It helps you gather accurate data about your customers.
Data mining can help you gather customer data from multiple sources and collate it to form informative and thorough profiles. This can give you valuable knowledge about customer trends, preferences, behaviors, similarities, and differences. That's the type of information that helps you deliver a better customer experience overall and improve communication across all touchpoints.
5. It helps you increase revenue.
With the knowledge you get from data mining, you can build much more personalized sales pitches, create better campaigns, and tailor content and product recommendations based on known customer preferences and behaviors.
You can also predict trends in how consumers purchase or navigate your website, figure out what stops them from buying or what leads them to churn, create accurate audience segments, and offer tailored promotions. It goes without saying that these data-driven changes yield a significantly higher ROI, increasing revenue.
Now that you know the benefits of data mining, let’s take a look at some techniques you can use to get started.
Data Mining Techniques
You can get started data mining without needing a data analyst on your staff roster. We’ll start with some basic techniques, then move on to more specialized processes.
- Data Warehousing : Data warehousing refers to the systems you use to store all of your business’s data. This can include spreadsheet tools, servers, and dedicated dataset software . Data warehousing is the backbone of a strong data mining process.
- Data Cleansing and Preparation: This is the next most important data mining technique. The information stored in your data warehouse must be duplicate-free and error-free, and must also be adaptable to different formats. Keeping your data quality high is essential in data mining, or you risk finding false trends and patterns.
- Association : Association refers to the process of finding correlations, and even causality, between different types of data. For instance, if your customers in a certain industry almost always buy a certain product, associating the two could help you create stronger pitches later.
- Classification : Classification is the straightforward process of putting your data in buckets based on specific shared qualities and characteristics. The most challenging aspect of classification is determining which categories you should place your data into.
- Regression : Regression is a data mining technique used to predict a number — for example, the price of an item — based on certain factors, characteristics, or data points. For instance, if you wanted to predict the price of a house, you might take into account the neighborhood, plot size, and more.
- Data Analytics : In data mining, data analytics refers to the process of turning raw data into insights that can help you make better business decisions. While you can use a wide variety of tools for data analytics, the most common ones include dashboard software and business intelligence reporting tools .
- Clustering : Similar to classification, clustering is the process of loosely putting data in buckets based on similarities. The difference between classification and clustering is that classification requires you to create categories, while clustering is more about finding similarities regardless of category.
An often overlooked step when implementing data processes — including data mining — is data integration . In a nutshell, data integration means combining data from several disparate sources into a unified database for a more consistent view of the data. It’s one of the most important steps in data lifecycle management (DLM) .
Advanced Data Mining Techniques
For the following techniques, you might need a data analyst who knows how to use AI and machine learning tools to further refine the data mining processes at your business.
- Artificial Intelligence : More of a tool and less of a technique, artificial intelligence systems can help you use speech recognition and natural language processing to glean insights from large datasets and help you classify and associate them.
- Machine Learning : In data mining, machine learning refers to the process of programming a software or computer to predict future patterns and behaviors, without being explicitly programmed to do so. A data analyst can use the Python and R programming languages to use machine learning in a data mining context.
- Association Rule Learning : Association rule learning mixes basic association, which we covered in the previous section, and machine learning to find patterns within your dataset. If the patterns keep occurring, this is called an “association rule.”
How to Data Mine
Data mining may sound like something only an enterprise firm can do, but any company can do it, so long as you approach it in stages. For that, we recommend using CRISP-DM (Cross Industry Standard Process for Data Mining) . It’s comprised of six stages:
- Business Understanding
- Data Understanding
- Data Preparation
We break these down below.
Stage 1: Business Understanding
In this stage, your job is to figure out what your company is trying to get out of this data mining project. Is it to increase revenue? Find better prospects? Attract top talent? Create more profitable marketing campaigns? It can truly be anything, so long as you can arrive to an answer by analyzing data.
Stage 2: Data Understanding
Next up, it’s time to identify the datasets you need to answer your question. For instance, if your goal is to increase revenue, you might need the current number of customers, the number who has churned, and the average deal size.
Gather your high-quality data and store it in a format that you can easily access. If you’re just getting started with data mining, you might use something as simple as Google Sheets. If your business is growing, consider HubSpot’s data sync tool . If you’re experienced, you might opt for a tool such as Tableau .
Stage 3: Data Preparation
Clean up the data, remove duplicates, and ensure it represents your business accurately. To avoid errors, you might employ the help of a tool such as Operations Hub and appoint this task to one person. Allowing multiple people to collaborate on one dataset at the same time may lead to duplicates and redundancies.
Check out our guides on data quality and data lifecycle management to ensure you do everything you need to do in this stage.
Stage 4: Modeling
In the modeling stage, you use algorithms, artificial intelligence, and machine learning to associate, categorize, regress, and cluster your data. If you have a data analyst on staff, they might use the R and Python programming languages to carry out these data mining techniques. They might also use data mining software .
If you’re just getting started, you might use the pivot table, filtering, and data visualization tools in your spreadsheet software.
Stage 5: Evaluation
Next, it’s time to look at the results. Do your findings help you answer the business question you established in stage one? If not, then it’s time to try stage four again — it’s totally normal to have to model the data various times before gleaning the right insights.
Stage 6: Deployment
Last, you compile all of your results in a presentation or dashboard and present it to key stakeholders. You’ll all convene and figure out what to do based on what you found in your data.
Data mining has its benefits, but it can sound like a lot to tackle for a beginner in the subject. One common point of confusion is in regards to the differences between data mining and data harvesting.
Data Mining vs Data Harvesting
Data mining is the analysis of large sets of data in order to derive trends, and data harvesting is the process of extracting data from online sources to then build analyses. While data mining focuses more on the analysis of data, data harvesting focuses on the collection of data.
The two processes can be complementary if done properly. Data harvesting involves crawling a website to extract its data. You can then use data mining to organize it into intelligible information.
While it is possible to do this safely and ethically, there are plenty of malicious actors who use data harvesting methods to collect information online — such as email addresses, contact lists, photos, videos, text, or code — without users' consent or knowledge.
Let’s take a look at one real-life example and two hypothetical examples to illustrate how harmful this practice can be.
Data Harvesting Examples
Harvesting data from facebook.
One famous example of data harvesting you might have heard of was the Cambridge Analytica and Facebook scandal. As reported by The New York Times , the British political consulting firm started harvesting data of millions of Facebook users in order to build psychological profiles of voters and try to sell them to political campaigns.
Though the Cambridge Analytica scandal was large-scale and had huge repercussions, unethical data harvesting practices can be conducted by any type of company, regardless of size.
Acquiring Data Without Users’ Consent
Let's say a small media startup is hoping to build more personalized content recommendations for their audience, which is mainly composed of women aged 18-24. So, in order to get more data to build these campaigns, this company decides to crawl similar websites that are often visited by the same target audience.
It finds out what type of content they consume there and builds tailored content recommendations from that. However, this data was acquired without users' consent, which already constitutes a data harvesting malpractice.
Buying Email Lists
Another unethical data harvesting example is when a company is seeking to broaden the reach of their email newsletters, but doesn't have a huge number of subscribers yet. So this company decides to buy a contact list from a third-party provider to reach more people. However, buying and selling contact lists may be prohibited under several data protection laws, as well as sending unsolicited emails when users didn't explicitly provide their personal data or consent to receive emails.
The scenarios described above are perfect examples of what not to do when deploying data mining and harvesting. In the Facebook-Cambridge Analytica case, for instance, data was extracted without users' consent or knowledge. Facebook also failed to safeguard user data against external actors, and the data was then used for purposes that the users didn't explicitly agree with — or even necessarily knew about.
That's why it's paramount to be aware of the potential pitfalls with data mining and data harvesting and ensure that you carry out these practices ethically and transparently.
When Data Mining, Ensuring Data Protection and Privacy Is Key
Like any process that deals with sensitive data — including personal data — your number one concern should be to ensure that all data you're collecting and using has been provided with explicit consent and in full compliance with any applicable privacy laws. This also includes making sure the data is secure throughout all stages of the process, including collection, storage, analysis, all the way to data deletion.
Organizations also need to implement internal rules to specify what the data can be used for and how it can be analyzed and implemented – and make sure that the insights taken from data mining themselves don't infringe on privacy policies. As a rule of thumb, being transparent, honest, and ethical with data should be your top priority.
Some companies may want to hire staff specialized in data science and security to oversee all data management and analysis procedures, which can be a big help to ensure data protection and user privacy throughout the entire process. They can also deploy specialized tools to achieve the best results.
However, all these special know-how and tools can end up getting quite expensive, which could make data mining cost-prohibitive to smaller or more budget-conscious businesses. This cost may also scale as your company grows and the complexity of your data increases.
Integrating Your Data Before Mining
Integrating your data can make data mining even more effective and accurate. Since your data would be unified, enriched, and up-to-date after integration, it would be much easier and faster to identify trends and patterns, allowing for more agile decision-making based on current and accurate results.
If you use a syncing solution like Operations Hub to integrate your data, your customer databases are also updated in real time, so any analysis you gather from this data will be based on real-time insights and enable you to build more accurate profiles and compile reliable reports.
This type of integration can also sync customers' communication preferences between your apps, making it much easier for you to visualize customers' opt-ins and opt-outs in all apps to comply with data protection and privacy laws.
With that, you can not only gather accurate, reliable, and relevant insights from your data, but you can do so safely and legitimately — putting users' privacy and protection front and center.
Editor's note: This post was originally published in October 2020 and has been updated for comprehensiveness.
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What Is Data Mining?
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What Is Data Mining? How It Works, Benefits, Techniques, and Examples
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Investopedia / Julie Bang
Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection , warehousing , and computer processing.
Key Takeaways
- Data mining is the process of analyzing a large batch of information to discern trends and patterns.
- Data mining can be used by corporations for everything from learning about what customers are interested in or want to buy to fraud detection and spam filtering.
- Data mining programs break down patterns and connections in data based on what information users request or provide.
- Social media companies use data mining techniques to commodify their users in order to generate profit.
- This use of data mining has come under criticism lately as users are often unaware of the data mining happening with their personal information, especially when it is used to influence preferences.
Watch Now: How Does Data Mining Work?
Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can be used in a variety of ways, such as database marketing, credit risk management, fraud detection , spam Email filtering, or even to discern the sentiment or opinion of users.
The data mining process breaks down into five steps. First, organizations collect data and load it into their data warehouses. Next, they store and manage the data, either on in-house servers or the cloud. Business analysts, management teams, and information technology professionals access the data and determine how they want to organize it. Then, application software sorts the data based on the user's results, and finally, the end-user presents the data in an easy-to-share format, such as a graph or table.
Data Warehousing and Mining Software
Data mining programs analyze relationships and patterns in data based on what users request. For example, a company can use data mining software to create classes of information. To illustrate, imagine a restaurant wants to use data mining to determine when it should offer certain specials. It looks at the information it has collected and creates classes based on when customers visit and what they order.
In other cases, data miners find clusters of information based on logical relationships or look at associations and sequential patterns to draw conclusions about trends in consumer behavior.
Warehousing is an important aspect of data mining. Warehousing is when companies centralize their data into one database or program. With a data warehouse, an organization may spin off segments of the data for specific users to analyze and use. However, in other cases, analysts may start with the data they want and create a data warehouse based on those specs.
Cloud data warehouse solutions use space and power of a cloud provider to store data from data sources. This allows smaller companies to leverage digital solutions for storage, security, and analytics.
Data Mining Techniques
Data mining uses algorithms and various techniques to convert large collections of data into useful output. The most popular types of data mining techniques include:
- Association rules , also referred to as market basket analysis, searches for relationships between variables. This relationship in itself creates additional value within the data set as it strives to link pieces of data. For example, association rules would search a company's sales history to see which products are most commonly purchased together; with this information, stores can plan, promote, and forecast accordingly.
- Classification uses predefined classes to assign to objects. These classes describe characteristics of items or represent what the data points have in common with each. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines.
- Clustering is similar to classification. However, clustering identified similarities between objects, then groups those items based on what makes them different from other items. While classification may result in groups such as "shampoo", "conditioner", "soap", and "toothpaste", clustering may identify groups such as "hair care" and "dental health".
- Decision trees are used to classify or predict an outcome based on a set list of criteria or decisions. A decision tree is used to ask for input of a series of cascading questions that sort the dataset based on responses given. Sometimes depicted as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
- K-Nearest Neighbor (KNN) is an algorithm that classifies data based on its proximity to other data. The basis for KNN is rooted in the assumption that data points that are close to each are more similar to each other than other bits of data. This non-parametric, supervised technique is used to predict features of a group based on individual data points.
- Neural networks process data through the use of nodes. These nodes is comprised of inputs, weights, and an output. Data is mapped through supervised learning (similar to how the human brain is interconnected). This model can be fit to give threshold values to determine a model's accuracy.
- Predictive analysis strives to leverage historical information to build graphical or mathematical models to forecast future outcomes. Overlapping with regression analysis , this data mining technique aims at supporting an unknown figure in the future based on current data on hand.
The Data Mining Process
To be most effective, data analysts generally follow a certain flow of tasks along the data mining process. Without this structure, an analyst may encounter an issue in the middle of their analysis that could have easily been prevented had they prepared for it earlier. The data mining process is usually broken into the following steps.
Step 1: Understand the Business
Before any data is touched, extracted, cleaned, or analyzed, it is important to understand the underlying entity and the project at hand. What are the goals the company is trying to achieve by mining data? What is their current business situation? What are the findings of a SWOT analysis ? Before looking at any data, the mining process starts by understanding what will define success at the end of the process.
Step 2: Understand the Data
Once the business problem has been clearly defined, it's time to start thinking about data. This includes what sources are available, how it will be secured stored, how information will be gathered, and what the final outcome or analysis may look like. This step also critically thinks about what limits their are to data, storage, security, and collection and assesses how these constraints will impact the data mining process.
Step 3: Prepare the Data
It's now time to get our hands on information. Data is gathered, uploaded, extracted, or calculated. It is then cleaned, standardized, scrubbed for outliers, assessed for mistakes, and checked for reasonableness. During this stage of data mining, the data may also be checked for size as an overbearing collection of information may unnecessarily slow computations and analysis.
Step 4: Build the Model
With our clean data set in hand, it's time to crunch the numbers. Data scientists use the types of data mining above to search for relationships, trends, associations, or sequential patterns. The data may also be fed into predictive models to assess how previous bits of information may translate into future outcomes.
Step 5: Evaluate the Results
The data-centered aspect of data mining concludes by assessing the findings of the data model(s). The outcomes from the analysis may be aggregated, interpreted, and presented to decision-makers that have largely be excluded from the data mining process to this point. In this step, organizations can choose to make decisions based on the findings.
Step 6: Implement Change and Monitor
The data mining process concludes with management taking steps in response to the findings of the analysis. The company may decide the information was not strong enough or the findings were not relevant to change course. Alternatively, the company may strategically pivot based on findings. In either case, management reviews the ultimate impacts of the business and re-creates future data mining loops by identifying new business problems or opportunities.
Different data mining processing models will have different steps, though the general process is usually pretty similar. For example, the Knowledge Discovery Databases model has nine steps, the CRISP-DM model has six steps, and the SEMMA process model has five steps.
Applications of Data Mining
In today's age of information, it seems like almost every department, industry, sector , and company can make use of data mining. Data mining is a vague process that has many different applications as long as there is a body of data to analyze.
The ultimate goal of a company is to make money, and data mining encourages smarter, more efficient use of capital to drive revenue growth. Consider the point-of-sale register at your favorite local coffee shop. For every sale, that coffeehouse collects the time a purchase was made, what products were sold together, and what baked goods are most popular. Using this information, the shop can strategically craft its product line.
Once the coffeehouse above knows its ideal line-up, it's time to implement the changes. However, to make its marketing efforts more effective, the store can use data mining to understand where its clients see ads, what demographics to target, where to place digital ads, and what marketing strategies most resonate with customers. This includes aligning marketing campaigns , promotional offers, cross-sell offers, and programs to findings of data mining.
Manufacturing
For companies that produce their own goods, data mining plays an integral part in analyzing how much each raw material costs, what materials are being used most efficiently, how time is spent along the manufacturing process, and what bottlenecks negatively impact the process. Data mining helps ensure the flow of goods is uninterrupted and least costly.
Fraud Detection
The heart of data mining is finding patterns, trends, and correlations that link data points together. Therefore, a company can use data mining to identify outliers or correlations that should not exist. For example, a company may analyze its cash flow and find a reoccurring transaction to an unknown account. If this is unexpected, the company may wish to investigate should funds be potentially mismanaged.
Human Resources
Human resources often has a wide range of data available for processing including data on retention, promotions, salary ranges, company benefits and utilization of those benefits, and employee satisfaction surveys. Data mining can correlate this data to get a better understanding of why employees leave and what entices recruits to join.
Customer Service
Customer satisfaction may be caused (or destroyed) for a variety of reasons. Imagine a company that ships goods. A customer may become unhappy with ship time, shipping quality, or communication on shipment expectations. That same customer may become frustrated with long telephone wait times or slow e-mail responses. Data mining gathers operational information about customer interactions and summarizes findings to determine weak points as well as highlights of what the company is doing right.
Benefits of Data Mining
Data mining ensures a company is collecting and analyzing reliable data. It is often a more rigid, structured process that formally identifies a problem, gathers data related to the problem, and strives to formulate a solution. Therefore, data mining helps a business become more profitable , efficient, or operationally stronger.
Data mining can look very different across applications, but the overall process can be used with almost any new or legacy application. Essentially any type of data can be gathered and analyzed, and almost every business problem that relies on qualifiable evidence can be tackled using data mining.
The end goal of data mining is to take raw bits of information and determine if there is cohesion or correlation among the data. This benefit of data mining allows a company to create value with the information they have on hand that would otherwise not be overly apparent. Though data models can be complex, they can also yield fascinating results, unearth hidden trends, and suggest unique strategies.
Limitations of Data Mining
This complexity of data mining is one of the largest disadvantages to the process. Data analytics often requires technical skillsets and certain software tools. Some smaller companies may find this to be a barrier of entry too difficult to overcome.
Data mining doesn't always guarantee results. A company may perform statistical analysis, make conclusions based on strong data, implement changes, and not reap any benefits. Through inaccurate findings, market changes, model errors, or inappropriate data populations , data mining can only guide decisions and not ensure outcomes.
There is also a cost component to data mining. Data tools may require ongoing costly subscriptions, and some bits of data may be expensive to obtain. Security and privacy concerns can be pacified, though additional IT infrastructure may be costly as well. Data mining may also be most effective when using huge data sets; however, these data sets must be stored and require heavy computational power to analyze.
Even large companies or government agencies have challenges with data mining. Consider the FDA's white paper on data mining that outlines the challenges of bad information, duplicate data, underreporting, or overreporting.
One of the most lucrative applications of data mining has been that of social media. Platforms like Facebook (owned by Meta), TikTok, Instagram, and Twitter gather reams of data about individual users to make inferences about their preferences in order to send targeted marketing ads. This data is also used to try to influence user behavior and change their preferences, whether it be for a consumer product or who they will vote for in an election.
Data mining on social media has become a big point of contention, with several investigative reports and exposes showing just how nefarious mining users' data can be. At the heart of the issue, users may agree to the terms and conditions of the sites not realizing how their personal information is being collected or to whom their information is being sold to.
Examples of Data Mining
Data mining can be used for good, or it can be used illicitly. Here is an example of both.
eBay and e-Commerce
eBay collects countless bits of information every day, ranging from listings, sales, buyers , and sellers . eBay uses data mining to attribute relationships between products, assess desired price ranges, analyze prior purchase patterns, and forms product categories. eBay outlines the recommendation process as:
- Raw item metadata and user historical data is aggregated.
- Scrips are run on a trained model to generate and predict the item and user.
- A KNN search is performed.
- The results are written to a database.
- The real-time recommendation takes the user ID, calls the database results, and displays them to the user.
Facebook-Cambridge Analytica Scandal
Another cautionary example of data mining includes the Facebook-Cambridge Analytica data scandal. During the 2010s, the British consulting firm Cambridge Analytical collected personal data belong to millions of Facebook users. This information was later analyzed to assist the 2016 presidential campaigns of Ted Cruz and Donald Trump. It is also suspected that Cambridge Analytica interfered with other notable events such as the Brexit referendum.
In slight of inappropriate data mining and misuse of user data, Facebook agreed to pay $100 million for misleading investors about the use of consumer data. The Securities and Exchange Commission claimed Facebook discovered the misuse in 2015 but did not correct disclosures for more than two years.
What Are the Types of Data Mining?
Data mining is broken into two basic aspects: predictive data mining and descriptive data mining. Predictive data mining is a type of analysis that extracts data that may be helpful in determining an outcome. Description data mining is a type of analysis that informs users of that data of a given outcome.
How Is Data Mining Done?
Data mining relies on big data and advanced computing processes including machine learning and other forms of artificial intelligence (AI). The goal is to find patterns that can lead to inferences or predictions from otherwise unstructured or large data sets.
What Is Another Term for Data Mining?
Data mining also goes by the less-used term knowledge discover in data, or KDD.
Where Is Data Mining Used?
Data mining applications range from the financial sector to look for patterns in the markets to governments trying to identify potential security threats. Corporations, and especially online and social media companies, use data mining on their users to create profitable advertising and marketing campaigns that target specific sets of users.
Modern businesses have the ability to gather information on customers, products, manufacturing lines, employees, and storefronts. These random pieces of information may not tell a story, but the use of data mining techniques, applications, and tools helps pieces together information to drive value. The ultimate goal of the data mining process is to compile data, analyze the results, and execute operational strategies based on data mining results.
International Journal of Innovation and Scientific Research. " A Comparative Study of Data Mining Process Models. "
Food and Drug Administration. " Data Mining at FDA -- White Paper ."
eBay. " Building a Deep Learning Based Retrieval System for Personalized Recommendations ."
Federal Trade Commission. " FTC Issues Opinion and Order Against Cambridge Analytica For Deceiving Consumers About Collection of Facebook Data, Compliance with EU-U.S. Privacy Shield ."
Security and Exchange Commission. " Facebook to Pay $100 Million for Misleading Investors About the Risks It Faced From Misuse of User Data. "
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10 Key Data Mining Techniques and How Businesses Use Them
Businesses collect and store an unimaginable amount of data, but how do they turn all that data into insights that help them build a better business? Data mining, the process of sifting through massive amounts of data to identify hidden business trends or patterns, makes these transformational business insights possible.
Data mining is not a new technology. Its roots have been traced to the 1930s, according to Hacker Bits , but the term became more widely used in the 1990s as businesses attempted to grapple with the ever-increasing amount of data our society was producing to derive value from it.
The advent of modern computers and application of data mining techniques meant businesses could finally analyze exponential amounts of data and extract non-intuitive, valuable insights; forecasting likely business outcomes, mitigating risks, and taking advantage of newly identified opportunities.
Due to its usefulness across many industries, and its critical role in business success, data mining is a promising career path. Companies need data scientists skilled in mining techniques who can present their findings in understandable ways. According to the U.S. Bureau of Labor Statistics , employment for computer and information research scientists is expected to climb by 15 percent through 2029.
Interested in a career in data science? Consider Georgia Tech Data Science and Analytics Boot Camp to learn the necessary foundational skills in just 24 weeks.
So what are the key techniques that aspiring data miners should know? Here are 10 data mining techniques that we will explore in detail:
- Association
- Data Cleaning
- Data Visualization
- Classification
- Machine Learning
- Neural Networks
- Outlier Detection
- Data Warehousing
If you’re interested in pursuing a data science career, read on to learn more about these data mining methods and how they can lead to success in different industries.
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10 Data Mining Techniques
1. Clustering
Clustering is a technique used to represent data visually — such as in graphs that show buying trends or sales demographics for a particular product.
What Is Clustering in Data Mining?
Clustering refers to the process of grouping a series of different data points based on their characteristics. By doing so, data miners can seamlessly divide the data into subsets, allowing for more informed decisions in terms of broad demographics (such as consumers or users) and their respective behaviors.
Methods for Data Clustering
- Partitioning method: This involves dividing a data set into a group of specific clusters for evaluation based on the criteria of each individual cluster. In this method, data points belong to just one group or cluster.
- Hierarchical method: With the hierarchical method, data points are a single cluster, which are grouped based on similarities. These newly created clusters can then be analyzed separately from each other.
- Density-based method: A machine learning method where data points plotted together are further analyzed, but data points by themselves are labeled “noise” and discarded.
- Grid-based method: This involves dividing data into cells on a grid, which then can be clustered by individual cells rather than by the entire database. As a result, grid-based clustering hase a fast processing time.
- Model-based method: In this method, models are created for each data cluster to locate the best data to fit that particular model.
Examples of Clustering in Business
Clustering helps businesses manage their data more effectively. For example, retailers can use clustering models to determine which customers buy particular products, on which days, and with what frequency. This can help retailers target products and services to customers in a specific demographic or region.
Clustering can help grocery stores group products by a variety of characteristics (brand, size, cost, flavor, etc.) and better understand their sales tendencies. It can also help car insurance companies that want to identify a set of customers who typically have high annual claims in order to price policies more effectively. In addition, banks and financial institutions might use clustering to better understand how customers use in-person versus virtual services to better plan branch hours and staffing.
2. Association
Association rules are used to find correlations, or associations, between points in a data set.
What Is Association in Data Mining?
Data miners use association to discover unique or interesting relationships between variables in databases. Association is often employed to help companies determine marketing research and strategy.
Methods for Data Mining Association
Two primary approaches using association in data mining are the single-dimensional and multi-dimensional methods.
- Single-dimensional association: This involves looking for one repeating instance of a data point or attribute. For instance, a retailer might search its database for the instances a particular product was purchased.
- Multi-dimensional association: This involves looking for more than one data point in a data set. That same retailer might want to know more information than what a customer purchased — such as their age, method of purchase (cash or credit card), or age.
Examples of Association in Business
The analysis of impromptu shopping behavior is an example of association — that is, retailers notice in data studies that parents shopping for childcare supplies are more likely to purchase specialty food or beverage items for themselves during the same trip. These purchases can be analyzed through statistical association.
Association analysis carries many other uses in business. For retailers, it’s particularly helpful in making purchasing suggestions. For example, if a customer buys a smartphone, tablet, or video game device, association analysis can recommend related items like cables, applicable software, and protective cases.
Additionally, association is used by the government to employ census data and plan for public services; it is also used by doctors to diagnose various illnesses and conditions more effectively.
3. Data Cleaning
Data cleaning is the process of preparing data to be mined.
What Is Data Cleaning in Data Mining?
Data cleaning involves organizing data, eliminating duplicate or corrupted data, and filling in any null values. When this process is complete, the most useful information can be harvested for analysis.
Methods for Data Cleaning
- Verifying the data: This involves checking that each data point in the data set is in the proper format (e.g, telephone numbers, social security numbers).
- Converting data types : This ensures data is uniform across the data set. For instance, numeric variables only contain numbers, while string variables can contain letters, numbers, and characters.
- Removing irrelevant data: This clears useless or inapplicable data so full emphasis can be placed on necessary data points.
- Eliminating duplicate data points: This helps speed up the mining process by boosting efficiency and reducing errors.
- Removing errors: This eliminates typing mistakes, spelling errors, and input errors that could negatively affect analysis outcomes.
- Completing missing values: This provides an estimated value for all data and reduces missing values, which can lead to skewed or incorrect results.
Examples of Data Cleaning in Business
According to Experian, 95 percent of businesses say they have been impacted by poor data quality . Working with incorrect data wastes time and resources, increases analysis costs (because models need to be repeated), and often leads to faulty analytics.
Ultimately, no matter how great their models or algorithms are, businesses suffer when their data is incorrect, incomplete, or corrupted.
4. Data Visualization
Data visualization is the translation of data into graphic form to illustrate its meaning to business stakeholders.
What Is Data Visualization in Data Mining?
Data can be presented in visual ways through charts, graphs, maps, diagrams, and more. This is a primary way in which data scientists display their findings.
Methods for Data Visualization
Many methods exist for representing data visually. Here are a few:
- Comparison charts: Charts and tables express relationships in the data, such as monthly product sales over a one-year period.
- Maps: Data maps are used to visualize data pertaining to specific geographic locations. Through maps, data can be used to show population density and changes; compare populations of neighboring states, counties, and countries; detect how populations are spread over geographic regions; and compare characteristics in one region to those in other regions.
- Heat maps: This is a popular visualization technique that represents data through different colors and shading to indicate patterns and ranges in the data. It can be used to track everything from a region’s temperature changes to its food and pop culture trends.
- Density plots: These visualizations track data over a period of time, creating what can look like a mountain range. Density plots make it easy to represent occurrences of single events over time (e.g., month, year, decade).
- Histograms: These are similar to density plots but are represented by bars on a graph instead of a linear form.
- Network diagrams: These diagrams show how data points relate to each other by using a series of lines (or links) to connect objects together.
- Scatter plots: These graphs represent data point relationships on a two-variable axis. Scatter plots can be used to compare unique variables such as a country’s life expectancy or the amount of money spent on healthcare annually.
- Word clouds: These graphics are used to highlight specific word or phrase instances appearing in a body of text; the larger the word’s size in the cloud, the more frequent its use.
Examples of Data Visualization in Business
Representing data visually is an important skill because it makes data readily understandable to executives, clients, and customers. According to Markets and Markets , the market size for global data visualization tools is expected to nearly double (to $10.2 billion) by 2026.
Companies can make faster, more informed decisions when presented with data that is easy to understand and interpret. Today, this is typically accomplished through effective, visually accessible mediums such as graphs, 3D models, and even augmented reality. As a result, it’s a good idea for aspiring data professionals to consider learning such skills through a data science and visualization bootcamp .
5. Classification
Classification is a fundamental technique in data mining and can be applied to nearly every industry. It is a process in which data points from large data sets are assigned to categories based on how they’re being used.
What Is Classification in Data Mining?
In data mining, classification is considered to be a form of clustering — that is, it is useful for extracting comparable points of data for comparative analysis. Classification is also used to designate broad groups within a demographic, target audience, or user base through which businesses can gain stronger insights.
Methods for Data Mining Classification
- Logistic regression: This algorithm attempts to show the probability of a specific outcome within two possible results. For example, an email service can use logistic regression to predict whether or not an email is spam.
- Decision trees: Once data is classified, follow-up questions can be asked, and the results diagrammed into a chart called a decision tree. For example, if a computer company wants to predict the likelihood of laptop purchases, it may ask, Is the potential buyer a student? The data is classified into “Yes” and “No” decision trees, with other questions to be asked afterward in a similar fashion.
- K-nearest neighbors (KNN): This is an algorithm that tries to identify an unknown object by comparing it to others. For instance, grocery chains might use the K-nearest neighbors algorithm to decide whether to include a sushi or hot meals station in their new store layout based on consumer habits in the local marketplace.
- Naive Bayes: Based on the Bayes Theorem of Probability, this algorithm uses historical data to predict whether similar events will occur based on a different set of data.
- Support Vector Machine (SVM): This machine learning algorithm is often used to define the line that best divides a data set into two classes. An SVM can help classify images and is used in facial and handwriting recognition software.
Examples of Classification in Business
Financial institutions classify consumers based on many variables to market new loans or project credit card risks. Meanwhile, weather apps classify data to project snowfall totals and other similar figures. Grocery stores also use classification to group products by the consumers who buy them, helping forecast buying patterns.
6. Machine Learning
Machine learning is the process by which computers use algorithms to learn on their own. An increasingly relevant part of modern technology, machine learning makes computers “smarter” by teaching them how to perform tasks based on the data they have gathered.
What Is Machine Learning in Data Mining?
In data mining, machine learning’s applications are vast. Machine learning and data mining fall under the umbrella of data science but aren’t interchangeable terms. For instance, computers perform data mining as part of their machine learning functions.
Methods for Machine Learning
- Supervised learning: In this method, algorithms train machines to learn using pre-labeled data with correct values, which the machines then classify on their own. It’s called supervised because the process trains (or “supervises”) computers to classify data and predict outcomes. Supervised machine learning is used in data mining classification.
- Unsupervised learning: When computers handle unlabeled data, they engage in unsupervised learning. In this case, the computer classifies the data itself and then looks for patterns on its own. Unsupervised models are used to perform clustering and association.
- Semi-supervised learning: Semi-supervised learning uses a combination of labeled and unlabeled data, making it a hybrid of the above models.
- Reinforcement learning: This is a more layered process in which computers learn to make decisions based on examining data in a specific environment. For example, a computer might learn to play chess by examining data from thousands of games played online.
Examples of Machine Learning in Business
With machine learning, companies can use computers to quickly identify all sorts of data patterns (in sales, product usage, buying habits, etc.) and develop business plans using those insights. This is a growing need in many industries.
According to a MicroStrategy survey , 18 percent of analytics professionals said machine learning and AI will have the most significant impact on their strategies over the next five years. Learning more advanced topics like machine learning is thus becoming imperative for data scientists.
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7. Neural Networks
Computers process large amounts of data much faster than human brains but don’t yet have the capacity to apply common sense and imagination in working with the data. Neural networks are one way to help computers reason more like humans.
What Are Neural Networks in Data Mining?
Artificial neural networks attempt to digitally mimic the way the human brain operates. Neural networks combine many computer processors (similar to the way the brain uses neurons) to process data, make decisions, and learn as a human would — or at least as closely as possible.
Neural Network Methods
Neural networks consist of three main layers: input, “hidden,” and output. Data enters through the input layer, is processed in the hidden layer, and is resolved in the output layer where any relevant action based on the data is then taken. The hidden layer can consist of many processing layers, depending on the amount of data being used and learning taking place.
Supervised and unsupervised learning also apply to neural networks; neural networks use these types of algorithms to “train” themselves to function in ways similar to the human brain.
Examples of Neural Networks in Business
Neural networks have a wide range of applications. They can help businesses predict consumer buying patterns and focus marketing campaigns on specific demographics. They can also help retailers make accurate sales forecasts and understand how to use dynamic pricing. Furthermore, they help to improve diagnostic and treatment methods in healthcare, improving care and performance.
8. Outlier Detection
Outlier detection is a key component of maintaining safe databases. Companies use it to test for fraudulent transactions, such as abnormal credit card usage that might suggest theft.
What Is Outlier Detection in Data Mining?
While other data mining methods seek to identify patterns and trends, outlier detection looks for the unique: the data point or points that differ from the rest or diverge from the overall sample. Outlier detection finds errors, such as data that was input incorrectly or extracted from the wrong sample. Natural data deviations can be instructive as well.
Methods for Outlier Detection
- Numeric outlier: Outliers are detected based on the Interquartile Range, or the middle 50 percent of values. Data points outside that range are considered outliers.
- Z-score: The Z-Score denotes how many standard deviations a data point is from the sample’s mean. This is also known as extreme value analysis.
- DBSCAN: This stands for “density-based spatial clustering of applications with noise” and is a method that defines data as core points, border points, and noise points, which are the outliers.
- Isolation forest: This method isolates anomalies in large sets of data (the forest) with an algorithm that searches for those anomalies instead of profiling normal data points.
Examples of Outlier Detection in Business
Almost every business can benefit from understanding anomalies in their production or distribution lines and how to fix them. Retailers can use outlier detection to learn why their stores witness an odd increase in purchases, such as snow shovels being bought in the summer, and how to respond to such findings.
Generally, outlier detection is employed to enhance logistics, instill a culture of preemptive damage control, and create a smoother environment for customers, users, and other key groups.
9. Prediction
Predictive modeling seeks to turn data into a projection of future action or behavior. These models examine data sets to find patterns and trends, then calculate the probabilities of a future outcome.
What Is Prediction in Data Mining?
Predictive modeling is among the most common uses of data mining and works best with large data sets that represent a broad sample size.
Methods for Prediction
Predictive modeling uses some of the same techniques and terminology as other data mining processes. Here are four examples:
- Forecast modeling: This is a common technique in which the computer answers a question (for instance, How much milk should a store have in stock on Monday? ) by analyzing historical data.
- Classification modeling: Classification places data into groups where it can be used to answer direct questions.
- Cluster modeling: By clustering data into groups with shared characteristics, a predictive model can be used to study those data sets and make decisions.
- Time series modeling: This model analyzes data based on when the data was input. A study of sales trends over a year is an example of time series modeling.
Examples of Prediction in Business
Predictive modeling is a business imperative that impacts nearly every corner of the public and private sectors. According to MicroStrategy , 52 percent of global businesses consider advanced and predictive modeling their top priority in analytics.
10. Data Warehousing
Data warehousing is the process by which data is collected and stored before it is evaluated.
What Is Data Warehousing in Data Mining?
Data miners collect data from multiple sources into a common archive before it can be used in business analysis. This process, called data warehousing, typically occurs before the data mining process.
Methods for Data Warehousing
Data goes through a three-stage process known as ETL before being loaded into a data warehouse. ETL stands for extract, transform, and load:
- Extract: Data is copied and moved from its source to a warehouse staging area. Data can be structured (names, dates, credit card numbers, etc.) or unstructured (photos, videos, audio files, social media posts).
- Transform: In this step, the data is filtered and cleaned — errors are removed and the data is validated. The data is also formatted to fit the warehouse.
- Load: In the final step, the transformed data is uploaded to the data warehouse. These steps can be repeated as data is updated.
Examples of Data Warehousing in Business
Data warehouses make working with big data easier — particularly for businesses that deal with large customer bases, sales and billing reports, and resource plans. Through data warehousing, businesses can segment and target customers from vast collections of sales orders, product searches, or loyalty program registrations. They also can store and analyze a wide variety of data points, even social media posts about products and businesses.
Data warehousing also consolidates various data sources into one place, making mining and decision-making more efficient and saving businesses time and money.
Businesses looking for a competitive advantage often find data to be among their best resources, and data mining techniques are vital in bringing this resource to fruition. Mining allows businesses to harness the power of data, gain insight, detect patterns and anomalies, and find ways to be more productive.
As we continue to produce a growing amount of diverse data, the ability to mine that data for insights will become increasingly important. Organizations generally want faster, more efficient ways to work with their data, more methods to visualize data, and computing systems that can make more human-like decisions.
As a result, many companies expect to increase their investment in analytics initiatives, which includes data mining. According to MicroStrategy’s 2018 Global State of Enterprise Analytics Report , 71 percent of global companies say they plan to spend more money on analytics (with 73 percent of U.S. companies intending to increase their analytics budgets).
As a result, data science and visualization is a promising career path, and a data science and analytics bootcamp is a great way to learn the technical skills needed to solve complex data problems and visualize solutions. Bootcamps cover necessary skills such as statistical modeling, database programming languages, and business intelligence software. They also afford the opportunity to gain practical experience through real-world projects.
Contact us to learn more about how Georgia Tech Data Science and Analytics Boot Camp can help you realize your data science career aspirations.
Data Mining Techniques FAQ
Aspiring data miners may still have a few lingering questions as they enter the field. Here are a few key points of summary:
What data mining techniques should I learn?
There are numerous crucial data mining techniques to consider when entering the data field, but some of the most prevalent methods include clustering, data cleaning, association, data warehousing, machine learning, data visualization, classification, neural networks, and prediction. Each of these techniques comprises an important aspect of data mining.
What is data mining?
Broadly speaking, data mining is the computer-driven process of exploring data sets, pinpointing key trends and anomalies, and subsequently analyzing these findings to form conclusions and make better decisions. Data mining is used in countless industries as a means of improving efficiency, developing crucial consumer insights, and innovating on existing business models.
What are data mining techniques used for?
There are many common data mining techniques, and each addresses a different aspect of data collection and analysis. For instance, outlier detection is used to identify critical abnormalities in data that could be indicative of a deeper issue. Meanwhile, predictive modeling is instrumental in developing more informed future plans based on existing findings.
What are the different types of data mining?
Categorically, data mining methods can range from pattern-based (clustering, classification, association) and anomaly-focused (outlier detection) to automated (neural networks, machine learning). In most cases, the type of data mining will depend on the entity using it and the data in question.
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Data mining in business analytics.
Data mining. You may read that and see scary images about hackers getting access to your information or people spying on you. But the truth is, data mining has a very important and positive role in our everyday lives. Data mining helps professionals and researchers learn about how to help with humanitarian work in many countries. They can learn about the spread of diseases, climate change, discrimination, and more. Without data mining it would take months or years to get the data we need to make predictions and solve problems around the world. Organizations around the globe use data mining for projects with all kinds of applications and meaning for the business world.
Data mining is an important role for IT professionals, and a degree in data analytics can help you be qualified to have a career in data mining. But everyone in business also needs to understand data mining—it is vital to how many business process are done and how information is gleaned, so current and aspiring business professionals need to understand how this process works as well.
This guide will help you learn more about what data mining is, how it’s done, and what it means for businesses.
What is data mining?
Simply put, data mining is the process that companies use to turn raw data into useful information. They utilize software to look for patterns in large batches of data so they can learn more about customers. It pulls out information from data sets and compares it to help the business make decisions. This eventually helps them to develop strategies, increase sales, market effectively, and more.
Data mining sometimes gets confused with machine learning and data analysis, but these terms are all very different and unique.
While both data mining and machine learning use patterns and analytics, data mining looks for patterns that already exist in data, while machine learning goes beyond to predict future outcomes based on the data. In data mining, the “rules” or patterns aren’t known from the start. In many cases of machine learning, the machine is given a rule or variable to understand the data. Additionally data mining relies on human intervention and decisions, but machine learning is meant to be started by a human and then learn on its own. There is quite a bit of overlap between data mining and machine learning, machine learning processes are often utilized in data mining in order to automate those processes.
Similarly data analysis and data mining aren’t interchangeable terms. Data mining is used in data analytics, but they aren’t the same. Data mining is the process of getting the information from large data sets, and data analytics is when companies take this information and dive into it to learn more. Data analysis involves inspecting, cleaning, transforming, and modeling data. The ultimate goal of analysis is discovering useful information, informing conclusions, and making decisions.
Data mining, data analysis, artificial intelligence, machine learning, and many other terms are all combined in business intelligence processes that help a company or organization make decisions and learn more about their customers and potential outcomes.
Overview of the data mining process.
Almost all businesses use data mining, and it’s important to understand the data mining process and how it can help a business make decisions.
Business understanding. The first step to successful data mining is to understand the overall objectives of the business, then be able to convert this into a data mining problem and a plan. Without an understanding of the ultimate goal of the business, you won’t be able to design a good data mining algorithm. For example, a supermarket may want to use data mining to learn more about their customers. The business understanding is that a supermarket is looking to find out what their customers are buying the most.
Data understanding. After you know what the business is looking for, it’s time to collect data. There are many complex ways that data can be obtained from an organization, organized, stored, and managed. Data mining involves getting familiar with the data, identifying any issues, getting insights, or observing subsets. For example, the supermarket may use a rewards program where customers can input their phone number when they purchase, giving the supermarket access to their shopping data.
Data Preparation. Data preparation involves getting the information production ready. This is the biggest part of data mining. It is taking the computer-language data, and converting it into a form that people can understand and quantify. Transforming and cleaning the data for modeling is key for this step.
Modeling. In the modeling phase, mathematical models are used to search for patterns in the data. There are usually several techniques that can be used for the same set of data. There is a lot of trial and error involved in modeling.
Evaluation. When the model is complete, it needs to be carefully evaluated and the steps to make the model need to be reviewed, to ensure it meets the business objectives. At the end of this phase, a decision about the data mining results will be made. In the supermarket example, the data mining results will provide a list of what the customer has purchased, which is what the business was looking for.
Deployment. This can be a simple or complex part of data mining, depending on the output of the process. It can be as simple as generating a report, or as complex as creating a repeatable data mining process to happen regularly.
After the data mining process has been completed, a business will be able to make their decisions and implement changes based on what they have learned.
How does data mining inform business analytics?
So why is data mining important for businesses? Businesses that utilize data mining are able to have a competitive advantage, better understanding of their customers, good oversight of business operations, improved customer acquisition, and new business opportunities. Different industries will have different benefits from their data analytics. Some industries are looking for the best ways to get new customers, others are looking for new marketing techniques, and others are working to improve their systems. The data mining process is what gives businesses the opportunities and understanding for how to make their decisions, analyze their information, and move forward.
Data mining techniques in business analytics.
Now that you understand why data mining is important, it’s beneficial to see how data mining works specifically in business settings.
Classification. This data mining technique is more complex, using attributes of data to move them into discernable categories, helping you draw further conclusions. Supermarket data mining may use classification to group the types of groceries customers are buying, like produce, meat, bakery items, etc. These classifications help the store learn even more about customers, outputs, etc.
Clustering. This technique is very similar to classification, chunking data together based on their similarities. Cluster groups are less structured than classification groups, making it a more simple option for data mining. In the supermarket example, a simple cluster group could be food and non-food items instead of the specific classes.
Association rules. Association in data mining is all about tracking patterns, specifically based on linked variables. In the supermarket example, this may mean that many customers who buy a specific item may also buy a second, related item. This is how stores may know how to group certain food items together, or in online shopping they may show “people also bought this” section.
Regression analysis. Regression is used to plan and model, identifying the likelihood of a specific variable. The supermarket may be able to project price points based on availability, consumer demand, and their competition. Regression helps data mining by identifying the relationship between variables in a set.
Anomaly/outlier detection. For many data mining cases, just seeing the overarching pattern might not be all you need. Data needs to be able to identify and understand the outliers in your data as well. For example, in the supermarket if most of the shoppers are female, but one week in February is mostly men, you’ll want to investigate that outlier and understand what is behind it.
These data mining techniques are key for businesses to be able to understand the information they have and better their practices.
Free data mining tools for businesses.
DataMelt. DataMelt performs mathematics, statistics, calculations, data analysis, and visualization. Many scripting languages and Java packages are available in this system.
ELKI Data Mining Framework. ELKI focuses on algorithms with a specific emphasis on unsupervised cluster and outlier systems. ELKI is designed to be easy for researchers, students, and business organizations to use
Orange Data Mining. Orange data mining helps organizations do simple data analysis and use top visualization and graphics. Heatmaps, hierarchical clustering, decision trees, and more are used in this process.
The R Project for Statistical Computing. The R Project is used in statistical modeling and graphics and is utilized on many operating systems and programs
Rattle GUI. Rattle GUI presents statistical and visual summaries of data, helps prepare it to be modeled, and utilizes supervised and unsupervised machine learning to present the information.
Weka 3. Weka is a great machine learning software that is used for teaching, research, and industrial applications.
There is a steep learning curve with data mining tools, and it’s important to study and research so you’re prepared for all the data mining techniques and options that are available. A degree program in data analytics could be the perfect key to helping you learn the skills, scripting, languages, operating systems, and more to make sure you’re prepared for a data mining career.
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Table of Contents
We are living in an information-rich, data-driven world. While it’s comforting to know there’s a plethora of readily available knowledge, the sheer volume creates challenges. The more information available, the longer it can find the useful insights you need.
That’s why today we’re discussing data mining. We’ll be exploring all aspects of data mining, including what it means, its stages, data mining techniques, the benefits it offers, data mining tools , and more. Let’s kick things off with a data mining definition, then tackle data mining concepts and techniques.
We will now begin by understanding what is data mining.
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What is Data Mining?
Typically, when someone talks about “mining,” it involves people wearing helmets with lamps attached to them, digging underground for natural resources. And while it could be funny picturing guys in tunnels mining for batches of zeroes and ones, that doesn't exactly answer “what is data mining.”
Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements.
Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process that includes many different components, some of which are even confused for data mining itself. For instance, statistics is a portion of the overall data mining process, as explained in this data mining vs. statistics article.
Additionally, both data mining and machine learning fall under the general heading of data science , and though they have some similarities, each process works with data in a different way. If you want to know more about their relationship, read up on data mining vs. machine learning .
Data mining is sometimes called Knowledge Discovery in Data, or KDD.
Data Mining History
For millennia, people have excavated places to find hidden mysteries. "Knowledge discovery in databases" refers to the act of sifting through data to uncover hidden relationships and forecast future trends. In the 1990s, the phrase "data mining" was invented. Data mining emerged from the convergence of three scientific disciplines: artificial intelligence, machine learning, and statistics.
Artificial intelligence is the human-like intelligence demonstrated by software and machines, machine learning is the term used to describe algorithms that can learn from data to create predictions, and statistics is the numerical study of data correlations.
Data mining takes advantage of big data's infinite possibilities and inexpensive processing power. Processing power and speed have grown significantly in the recent decade, allowing the globe to undertake rapid, easy, and automated data analysis.
Data Mining Steps
When asking “what is data mining,” let’s break it down into the steps data scientists and analysts take when tackling a data mining project.
1. Understand Business
What is the company’s current situation, the project’s objectives, and what defines success?
2. Understand the Data
Figure out what kind of data is needed to solve the issue, and then collect it from the proper sources.
3. Prepare the Data
Resolve data quality problems like duplicate, missing, or corrupted data, then prepare the data in a format suitable to resolve the business problem.
4. Model the Data
Employ algorithms to ascertain data patterns. Data scientists create, test, and evaluate the model.
Also Read: Top 6 Data Scientist Skills You Need in 2022
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5. Evaluate the Data
Decide whether and how effective the results delivered by a particular model will help meet the business goal or remedy the problem. Sometimes there’s an iterative phase for finding the best algorithm , especially if the data scientists don’t get it quite right the first time. There may be some data mining algorithms shopping around.
6. Deploy the Solution
Give the results of the project to the people in charge of making decisions.
To extend our learning on what data mining is, we will next look at the benefits.
Examples of Data Mining
The following are a few real-world examples of data:
Shopping Market Analysis
In the shopping market, there is a big quantity of data, and the user must manage enormous amounts of data using various patterns. To do the study, market basket analysis is a modeling approach.
Market basket analysis is basically a modeling approach that is based on the notion that if you purchase one set of products, you're more likely to purchase another set of items. This strategy may help a retailer understand a buyer's purchasing habits. Using differential analysis, data from different businesses and consumers from different demographic groups may be compared.
Weather Forecasting Analysis
For prediction, weather forecasting systems rely on massive amounts of historical data. Because massive amounts of data are being processed, the appropriate data mining approach must be used.
Stock Market Analysis
In the stock market, there is a massive amount of data to be analyzed. As a result, data mining techniques are utilized to model such data in order to do the analysis.
Intrusion Detection
Well, data mining can assist to enhance intrusion detection by focusing on anomaly detection. It assists an analyst in distinguishing between unusual network activity and normal network activity.
Fraud Detection
Traditional techniques of fraud detection are time-consuming and difficult due to the amount of data. Data mining aids in the discovery of relevant patterns and the transformation of data into information.
Surveillance
Well, video surveillance is utilized practically everywhere in everyday life for security perception. Because we must deal with a huge volume of acquired data, data mining is employed in video surveillance.
Financial Banking
With each new transaction in computerized banking, a massive amount of data is expected to be created. By identifying patterns, causalities, and correlations in corporate data, data mining may help solve business challenges in banking and finance.
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What Are the Benefits of Data Mining?
Since we live and work in a data-centric world, it’s essential to get as many advantages as possible. Data mining provides us with the means of resolving problems and issues in this challenging information age. Data mining benefits include:
- It helps companies gather reliable information
- It’s an efficient, cost-effective solution compared to other data applications
- It helps businesses make profitable production and operational adjustments
- Data mining uses both new and legacy systems
- It helps businesses make informed decisions
- It helps detect credit risks and fraud
- It helps data scientists easily analyze enormous amounts of data quickly
- Data scientists can use the information to detect fraud, build risk models, and improve product safety
- It helps data scientists quickly initiate automated predictions of behaviors and trends and discover hidden patterns
Challenges of Implementation in Data Mining
Because data handling technology is always improving, leaders confront additional obstacles in addition to scalability and automation, as mentioned below:
Distributed Data
Real-world data saved on several platforms, such as databases, individual systems, or the Internet, cannot be transferred to a centralized repository. Regional offices may have their own servers to store data, but storing data from all offices centrally will be impossible. As a result, tools and algorithms for mining dispersed data must be created for data mining.
Complex Data
It takes a long time and money to process big amounts of complicated data. Data in the real world is structured, unstructured,semi-structured, and heterogeneous forms, including multimedia such as photos, music, video, natural language text, time series, natural, and so on, making it challenging to extract essential information from many sources in LAN and WAN.
Domain Knowledge
It is simpler to dig some information with domain expertise, without which collecting useful information from data might be tough.
Data Visualization
The first interaction that presents the result correctly to the client is data visualization. The information is conveyed with unique relevance based on its intended use. However, it is difficult to accurately address the information to the end-user. To make the information relevant, effective output information, input data, and complicated data perception methods must be used.
Incomplete Data
Large data amounts might be imprecise or unreliable owing to measurement equipment problems. Customers that refuse to disclose their personal information may result in incomplete data, which may be updated owing to system failures, resulting in noisy data, making the data mining procedure difficult.
Security and Privacy
Decision-making techniques necessitate security through data exchange for people, organizations, and the government. Private and sensitive information about individuals is gathered for customer profiles in order to better understand user activity trends. Illegal access and the confidentiality of the information are significant issues here.
Higher Costs
The expenses linked with purchasing and maintaining strong servers, software, and hardware for handling massive amounts of data might be too expensive.
Performance Issues
The performance of a data mining system is determined by the methods and techniques utilized, which might have an impact on data mining performance. Large database volumes, data flow, and data mining challenges can all contribute to the development of parallel and distributed data mining methods.
User Interface
If the knowledge uncovered via data mining technologies is engaging and clear to the user, it will be beneficial. Mining findings from appropriate visualisation data interpretation may assist comprehend customer requirements. Users can utilize the data mining process to discover trends and present and optimize data mining requests depending on the results.
Data Mining Prerequisites
Data mining necessitates an understanding of arithmetic and statistics, programming, business principles, and communication. To begin studying data analysis, you must have knowledge in the following areas:
- Linear Algebra
Artificial Intelligence
Machine learning.
- Statistical Analysis
- Data Structures and Algorithms
- Data Retrieval and Database
- Problem-solving Ability
Learn how to use tools such as RapidMiner, Apache Spark, and SAS. These are suggested for beginning your data analysis training.
R and Python are well-known programming languages in this field. In the sober analysis, the R language has great backing and can function effectively with Java and C.
Python is also commonly used in data mining and machine learning. Because of its various libraries and frameworks, it is popular among programmers in this sector. Python is also appropriate for large projects, and if you are familiar with object-oriented programming, you will find it easier to learn Python.
The Future of Data Mining
The future of data mining is bright, as data volumes continue to grow. Mining techniques have changed as a result of technological advancements, as have systems that extract useful information from data. Previously, only companies such as NASA could utilize their supercomputers to examine data since the expense of storing and calculating data was prohibitively expensive.
Companies are now experimenting with machine learning, artificial intelligence, and deep learning on cloud-based data lakes.
The Internet of Things and wearable technologies have transformed people and gadgets into data-generating machines capable of producing infinite knowledge about individuals and organizations. This is how businesses can gather, store, and analyze massive amounts of data.
Cloud-based analytics solutions will make it easier and more cost-effective for businesses to access huge amounts of data and processing power. Cloud computing enables businesses to swiftly receive and act on data from sales, marketing, Internet, manufacturing, and inventory systems, among other sources, in order to enhance their bottom line.
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Are There Any Drawbacks to Data Mining?
Nothing’s perfect, including data mining. These are the major issues in data mining:
- Many data analytics tools are complex and challenging to use. Data scientists need the right training to use the tools effectively.
- Speaking of the tools, different ones work with varying types of data mining, depending on the algorithms they employ. Thus, data analysts must be sure to choose the correct tools.
- Data mining techniques are not infallible, so there’s always the risk that the information isn’t entirely accurate. This obstacle is especially relevant if there’s a lack of diversity in the dataset.
- Companies can potentially sell the customer data they have gleaned to other businesses and organizations, raising privacy concerns.
- Data mining requires large databases, making the process hard to manage.
After going through what is data mining, let us look into the various kinds.
Also Read: How to Become a Data Analyst in 2022?
What Kinds of Data Mining Tools Are Out There?
As engineers are fond of saying, “Use the right tool for the right job.” Here is a selection of tools and techniques that provide data analysts with diverse data mining functionalities.
Association Rule Learning
Classification, data analytics, data cleansing and preparation, data warehousing.
Two specific tools need mentioning.
- R. This language is an open-source tool used for graphics and statistical computing. It provides analysts with a wide selection of statistical tests, classification and graphical techniques, and time-series analysis.
- Oracle Data Mining (ODM). This tool is a module of the Oracle Advanced Analytics Database. It helps data analysts make predictions and generate detailed insights. Analysts use ODM to predict customer behavior, develop customer profiles, and identify cross-selling opportunities.
In our learning about what is data mining, let us now look into the applications.
Data Mining Applications
Data mining is a useful and versatile tool for today’s competitive businesses. Here are some data mining examples, showing a broad range of applications.
Data mining helps banks work with credit ratings and anti-fraud systems, analyzing customer financial data, purchasing transactions, and card transactions. Data mining also helps banks better understand their customers’ online habits and preferences, which helps when designing a new marketing campaign.
Data mining helps doctors create more accurate diagnoses by bringing together every patient’s medical history, physical examination results, medications, and treatment patterns. Mining also helps fight fraud and waste and bring about a more cost-effective health resource management strategy.
If there was ever an application that benefitted from data mining, it’s marketing! After all, marketing’s heart and soul is all about targeting customers effectively for maximum results. Of course, the best way to target your audience is to know as much about them as possible. Data mining helps bring together data on age, gender, tastes, income level, location, and spending habits to create more effective personalized loyalty campaigns. Data marketing can even predict which customers will more likely unsubscribe to a mailing list or other related service. Armed with that information, companies can take steps to retain those customers before they get the chance to leave!
The world of retail and marketing go hand-in-hand, but the former still warrants its separate listing. Retail stores and supermarkets can use purchasing patterns to narrow down product associations and determine which items should be stocked in the store and where they should go. Data mining also pinpoints which campaigns get the most response.
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There’s a lot of data generated every day, and consequently, there is a correspondingly great demand for professionals to analyze that information using techniques like data mining. Simplilearn’s Caltech Post Graduate Program in Data Science is the perfect data analytics certification course for anyone on a data scientist career path.
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1. Why use data mining?
Data mining uses span from the finance industry searching for market patterns to governments attempting to uncover potential security risks. Corporations, particularly internet and social media businesses, mine user data to build successful advertising and marketing campaigns targeting certain consumer groups.
Data mining assists marketers in better understanding client behavior and preferences, allowing them to design focused marketing and advertising campaigns. Similarly, sales teams may leverage data mining results to enhance lead conversion rates and sell new items and services to current clients.
2. Why is data mining so popular?
The reason is simple: it creates several commercial prospects because to its predictive and descriptive capabilities; hence, it is the technology that can forecast the future and make it lucrative. Businesses may learn more about their consumers by utilizing software to search for patterns in enormous amounts of data. This allows them to design more successful marketing campaigns, improve sales, and save expenses.
3. What are the key advantages of data mining?
It assists firms in making informed judgments. It aids in the detection of credit risks and fraud. It enables data scientists to swiftly evaluate massive volumes of data. The information may be used by data scientists to detect fraud, construct risk models, and improve product safety.
4. What are the disadvantages of Data Mining?
Data mining makes extensive use of technology in the data collecting process. Every piece of data created needs its own storage space as well as upkeep. This can significantly raise the cost of deployment. When employing data mining, identity theft is a major concern. If proper security is not given, it may expose security vulnerabilities. Many privacy issues have been highlighted while employing data mining. The information gathered for data mining can be utilized for reasons other than those for which it was gathered, despite the fact that data mining has opened the road for easy data acquisition in its own ways. It still has limits in terms of accuracy. The information obtained may be incorrect, producing issues with decision-making.
5. What Are the Types of Data Mining?
Each of the data mining approaches listed below serves multiple different business challenges and gives a unique perspective on each of them. Understanding the sort of business problem you need to address, on the other hand, can assist you in determining which strategy to apply and which will produce the greatest outcomes. The Data Mining kinds are classified into two categories, which are as follows:
Predictive Data Mining Analysis
Descriptive Data Mining Analysis
6. What are the advantages and disadvantages of Data Mining?
It aids in the detection of hazards and fraud.
It aids in the understanding of behaviors, trends and the discovery of hidden patterns.
Aids in the rapid analysis of vast amounts of data
Disadvantages
Data mining necessitates vast datasets and is costly.
7. How Is Data Mining Done?
Projects such as data cleansing and exploratory analysis are part of the data mining process, but they are not the only ones. Data mining professionals clean and prepare data, develop models, test models against hypotheses, and publish models for analytics or business intelligence initiatives.
8. What Is Another Term for Data Mining?
Knowledge Discovery in Data(KDD) is another name for data mining.
9. Where Is Data Mining Used?
Market risks can be easily and definitely better assessed by all the banks using the methodology of data mining. It is often used to analyze transactions, card transactions, purchasing trends, and client financial data in credit ratings and intelligent anti-fraud systems. The retail industry is another example of Data Mining and Business Intelligence. Retailers divide their clients into 'Recency, Frequency, and Monetary (RFM) groupings and focus marketing and promotions on each category.
10. What is the difference between machine learning and data mining?
Data mining is intended to extract rules from massive amounts of data, whereas machine learning teaches a computer how to understand and interpret the parameters provided. To put it another way, data mining is essentially a means of doing research to discover a certain conclusion based on the sum of the data collected.
11. What is the most common application of data mining?
In order to better assess market risks, banks use data mining. It is often used to analyze transactions, card transactions, purchasing trends, and client financial data in credit ratings and intelligent anti-fraud systems.
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The data mining business case: here's what to include.
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A solid business case maximizes your chance of getting funding for data mining initiatives. (Image: ... [+] Shutterstock)
You believe your organization can benefit from data mining, or from expanding on the data mining program you’ve already got in place. You want to make a little investment, but you need management approval to do it. You need a business case for data mining.
But a lot of people who’ve been trained in statistics, programming, data management or similar technical skills haven’t been trained to prepare a business case. If that’s your situation, here’s how to get started.
The business case has four sections:
- An explanation of the business problem to be addressed.
- Potential solutions for the problem.
- What the business will gain from a solution.
- What the solution will cost.
Simple, right? To make your business case convincing, you’ll need concrete evidence for the things you state.
For starters, nobody spends money to solve a problem without believing that the problem exists, so the first thing to do is gather together the evidence that a problem exists. You may have financial statements, internal email, customer complaints or other documentation.
Using this material, outline the situation. Begin with a background section, which explains exactly what and who is involved. Name the organization and business unit. Describe the business purpose and current status. Then go to the problem statement section, which explains what’s going wrong, and how the problem affects the business. Provide some history, such as when the problem began, how it has evolved, whether the problem is common or unusual, whether it is getting worse. Provide your best estimate of what the problem is costing your business.
Lay out the alternative actions for solving the problem. Include several options, even if only one seems reasonable. Evaluating several alternatives builds the credibility of your business case. Get suggestions for action through input from staff, industry literature, and input from competent vendors.
For each alternative, you’ll need to state whether it is actually feasible, plus advantages and disadvantages of that approach. Do this with care. The idea is not to force management to make a choice, but to provide enough information to clearly explain why you are proposing one specific course of action.
Clearly indicate your preferred choice and the reasons for that preference, and end this section with an explanation of how your recommended action aligns with business goals.
Benefits should be stated in financial terms. Sometimes this is easy, and sometimes not. If, for example, your plan calls for bringing work in house that you now send to a vendor, and you know exactly what you’re paying the vendor, then it’s easy to say how much money you will save. But if your object is to gain new information, address a security concern or comply with a change in the law, it’s hard to put a value on that. Still, do your best. Consider what additional sales you could get with the right information, what losses the security problem may cause, and so on.
Explain how the recommended action will bring about the benefits you’re claiming. It may be obvious to you, but remember that your executive manager doesn’t spend all day pondering the benefits of analytics.
Provide metrics, ways to measure actual benefits. Will it be new sales, fewer customer service calls, reduced fraud rates? Give management confidence that there will be a way to check results later.
Stating the cost of your proposed solution is often the easiest part of the business case. Provide a detailed budget, including both cash and noncash (which may include some labor and other resources that are not explicitly charged) expenses. Include a timeline, as cash-flow may be important for gaining the approvals you need.
Finally, strengthen your case by including a section on the costs of inaction. It’s harder to say that money’s not available for a solution when you’re reminded that the business is losing money each day that you delay.
What if you don’t know each of the things you need to put in the business case? Use your business knowledge and analysis skill, and figure it out. Look for existing information sources to provide as much information as possible. Estimate any missing elements. For example, if you don’t know exactly how much the labor cost will be, make an educated guess at the range of time that may be needed, the range of cost per hour and so on. Put together a convincing case built out of pieces you can explain and defend.
The mere fact that you’ve prepared a business case for data mining will help to put your proposal ahead of many others competing for the same resources. Using the proper structure, documented evidence and a compelling financial story builds the strongest possible argument in favor of your data mining proposal.
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Data Mining in Business Analytics 101 – The Ultimate Guide
Vaishali Advani • May 31st, 2022
In this day and age, businesses are generating and storing a large volume of data to analyze and generate insights to optimize processes, reduce costs, and engage better with customers, among others. What makes these business insights possible is data mining, which is essentially the process of sifting and sorting through data to identify underlying trends and patterns.
Table of Contents
Data mining in business analytics has been around for some time now, with the oldest references going back to the 1930s. But it became popular only in the 90s as businesses were trying to make sense of all the data they have been generating.
With data mining, businesses can extract valuable insights, forecast business outcomes, mitigate risks, and identify new opportunities. In this article, we will discuss what is data mining and its importance in business analytics. We also have a list of top data mining tools for your perusal.
Table of Contents:
What is data mining, process overview of data mining in business analytics, association, data cleaning, data visualization, classification, outlier detection.
- Free Tools For Data Mining in Business Analytics
Data Mining in business is the process of turning raw data into useful information by identifying hidden patterns and trends. Various tools help businesses in parsing large data volumes in batches to pull out important information. This information then helps businesses to fine-tune strategies, increase revenue, reduce cost, effective marketing, enhance customer relationships, mitigate risks, and much more.
As more and more businesses consider big data and analytics to be their prime digital drivers, let’s take a glance at the importance of data mining in business. Data mining helps businesses:
- To gain a competitive advantage
- A better understanding of customers and prospects
- Have a good oversight of business operations
- Identifying new business opportunities
Different organizations from different industries will benefit in different ways by using data mining, depending on their respective priorities. In each scenario, the data mining process helps businesses in understanding how to make better decisions by analyzing their information and then proceeding forward.
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Most of the businesses using data mining would know and follow a set process that is described below. Here’s a step by step guide to using data mining in business analytics:
Understand Business Objective: The first step is to identify and understand the business objective and the purpose that needs to be solved with Data Mining. Next is to convert the objective into a data mining problem statement and devise a plan. This is important to design an accurate data mining algorithm that fuels the business objective and provides insights and information that are actually required.
Familiarizing with Data: Once the objective is clear, the next step is to collect the data and get familiar with it. Businesses would need to set the process for collecting, organizing, storing, and managing the data. Here it is important to identify any existing issues in the above-mentioned stages of data collection to storage, along with getting readily available insights and observing the subsets.
Preparing the Data: The information alone is not enough for implementing the whole data mining process. It needs to be in a form that is production-ready. It means transforming the computer language data into a form that is understandable and quantifiable by the stakeholders. Along with transformation, data preparation also involves data cleaning.
Data Modeling: This step involves mathematical models that would search for the hidden patterns in the data. As technology is advancing, machine learning techniques are being actively used by businesses to create models.
Evaluating the Model: Once the data model is complete, there is a need to evaluate it on certain important parameters. The steps used in data modeling need to be reviewed to ensure that the result from the model aligns with the business objective.
Deploying the Data Model: Once the model is created, reviewed, and optimized if required, it is deployed to generate the output of the data mining process. Depending on the type of output that is being generated, deployment could be a simple or a complex part of the whole data mining process.
Data Mining Techniques in Business Analytics
When a series of different data points are grouped together based on their characteristics, it is called clustering. The data is divided into subsets enabling informed decision-making. Business analysts can have information about broad demographics and their behaviors. A simple use case of clustering could be when a retail business segregates and clusters customers based on the product they have purchased. This helps them to run targeted ads for customers in each cluster.
Finding correlation or association between data points in a data set is known as an association in data mining. It helps discover unique relationships between variables in a database. This type of data mining technique is mostly used to determine marketing strategies. A good use case of association is how the government employs census data to plan for public services, or how doctors use association to diagnose medical conditions more effectively.
Data cleaning in data mining is to prepare the data before it is mined. It involves the elimination of duplicate data, removal of corrupted data, data organization, and filling up null values. The information drawn from the data after it is cleaned can be harvested for analysis. Working with data that is incorrect nullifies the whole purpose of data mining in business analytics. No matter how sophisticated a data mining model is, businesses will ultimately suffer if the data being used is not cleaned.
A graphical representation of data better illustrates its meaning, especially to the business stakeholders who are not data engineers or analysts. Information drawn out of data could be represented through charts, graphs, diagrams, and more. Data visualization is extensively used in business reporting because of how well they communicate the findings from the data mining process. When the data is presented in such an easy-to-understand manner, businesses are able to make informed decisions faster.
Classification is one such data mining technique that can be applied to any business in any industry. It is also considered a type of clustering but mostly for comparative analysis. It is used to categorize broad groups of target audiences within demographic or other factors. By categorizing, businesses can get comprehensive insights. A common use case of categorization in data mining could be how financial institutions classify consumer profiles depending on various variables to project credit card risks or provide new loans.
It is a data mining technique that detects anomalies in patterns identified in the data set. Outlier detection in data mining is the key to maintaining safe databases. Businesses use it for use cases such as fraud detection, or abnormal account activity that might suggest theft. It flags any unique data points that diverge from the overall data set. Sometimes outliers can be of an instructive nature as well. Another use case could be understanding anomalies in production or distribution lines to identify any blocker or bottleneck so they can be fixed.
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Check out what makes Hevo amazing:
- Fully Managed : Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation : Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation : Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making.
- Schema Management : Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure : Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support : Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Free Tools for Data Mining in Business Analytics
Here we have listed some of the free data mining tools for your perusal:
DataMelt is used for numeric computation, statistics, mathematics, data analysis, visualization, and more. The platform combines multiple scripting languages such as Ruby, Python, and Java.
ELKI Data Mining Framework
It is an open-source data mining software in Java that focuses on unsupervised methods in cluster analysis and outlier detection.
Orange Data Mining
Orange Data Mining is a diverse toolbox that has the capability to build data analysis workflows visually. It also supports open-source machine learning and data visualization.
Rattle GUI is a free and open-source software package providing a graphical user interface (GUI) for data mining using the R statistical programming language.
In this article, we understood various aspects of data mining in business analytics. We went through the importance of data mining in business analytics, the techniques, and even some free tools which could help you effectively mine the large volumes of data that businesses generate.
Hevo is another great tool to establish a data pipeline and transform data from a variety of sources to a destination in an analytics-ready format. Hevo Data can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ sources to Cloud-based Data Warehouses like Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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