Data mining involves analyzing data to look for patterns, correlations, trends, and anomalies that might be significant for a particular business.
Organizations can use data mining techniques to analyze a particular customer’s previous purchase and predict what a customer might be likely to purchase in the future. It can also highlight purchases that are out of the ordinary for a customer and might indicate fraud.
For more information, also see: What is Big Data Analysis
How Data Mining Works
Data mining often starts with data collection, as most companies collect records, logs, website visitors’ data, application data, sales data, and more. By collecting this data, a company can understand what limits there are and what can be done.
The cross-industry standard process for data mining (CRISP-DM) is a guide to help start the data mining process. There are six phases for data mining: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
The 6 CRISP-DM phases
The objectives and requirements of the project are the focus of this phase. Four tasks in this phase help with many project management activities:
- Determine business objectives: Decide what a company should accomplish with the help of customer needs and define business success criteria.
- Assess the situation: Determine resources, requirements, assess risks, and conduct a cost-benefit analysis.
- Determine goals: A company must analyze what success may look like from a data mining perspective.
- Create project plan: A company should evaluate and select technologies, and tools, and create detailed plans for all phases.
Establishing business understanding is essential to data mining.
The next phase is working to understand the data, which adds to business understanding as well. It controls the focus to identify, collect, and analyze the data sets to help achieve the project goals. This phase also has four tasks:
- Collect necessary data: Gather all possible data that relates to the issues in question.
- Describe data: Notate the data’s various parameters, which helps describe the depth of the research.
- Learn more about the data: Use related and semi-related data for comparison to put the mined data set in better context.
- Verify data quality: Examine the data quality – where it came from, when it was gathered – to better understand the later results.
Data preparation is one of the most vital phases of the six. This phase prepares the final data sets for modeling. This phase has five tasks:
- Select data: Choose which data sets will be used, and document why it is necessary.
- Clean data: This task is meant to correct or remove unneeded values.
- Construct data: See what new attributes will be helpful.
- Integrate data: Combine data from multiple sources to create new data sets.
- Format data: Re-format data as needed or if it is necessary.
Modeling is one of the shortest phases in the process. It usually consists of building and accessing models based on different modeling techniques. This phase has four tasks:
- Select modeling techniques: Determine which modeling algorithms to use and estimate how they might affect the project.
- Generate test design by splitting: A company should then split the data into training, test, and validation sets.
- Build model: Building a model can usually be executed through a few lines of code.
- Assess model: To ensure a data scientist decides on the correct model, the model needs to be interpreted based on domain knowledge, defined success criteria, and the test design.
Practice teams should continue repeating the process until they find a good model, and then later improve the models.
The Evaluation phase looks at data more broadly than the access model. The optimal model must meet the business needs and lay out what to do next.
This phase has three tasks:
- Evaluate results: Did the results confirm your hypothesis, or suggest new possible data mining models?
- Review process: Look at the various steps you took to complete this data mining – were all practices optimal?
- Determine next steps: Based on your results, what data mining query do you want to perform next?
The deployment phase might be as simple as generating a report or might be as complex as using a repeatable data mining process across the company.
A model is not useful unless the customer can access the results. The difficulty of this phase varies. This final phase has four tasks:
- Plan deployment: Create and document a plan for deploying the model.
- Plan monitoring and maintenance: A company should develop a thorough monitoring and maintenance plan for data scientists to avoid problems during the operational phase.
- Produce final report: The project team constructs a summary of the project containing data mining results.
- Review project: See what phases went well and how to improve in the future.
As a project framework, CRISP-DM does not define what to do when the project is completed. If the model is going to production, be sure the model is maintained in production.
See more: The Data Mining Market
Types of Data Mining
Data scientists and analysts use many different data mining techniques to accomplish their goals. Some of the most common include the following:
- Clustering involves finding groups with similar characteristics. For example, marketers often use clustering to identify groups and subgroups within their target markets. Clustering is helpful when you don’t know what similarities might exist within your data.
- Classification sorts items (or individuals) into categories based on a previously learned model. Classification often comes after clustering (although you can also train a system to classify data based on categories that the data scientist or analyst defines). Clustering identifies the potential groups in an existing data set, and classification puts new data into the appropriate group. Computer vision systems also use classification systems to identify objects in images.
- Association identifies pieces of data that are commonly found near each other. This is the technique that drives most recommendation engines, such as when Amazon suggests that if you purchased one item, you might also like another item.
- Anomaly detection looks for pieces of data that don’t fit the usual pattern. These techniques are very useful for fraud detection.
- Regression is a more advanced statistical tool that is common in predictive analytics. It can help social media and mobile app developers increase engagement, and it can also help forecast future sales and minimize risk. Regression and classification can also be used together in a tree model that is useful in many different situations.
- Text mining analyzes how often people use certain words. It can be useful for sentiment or personality analysis, as well as for analyzing social media posts for marketing purposes or to spot potential data leaks from employees.
- Summarization puts a group of data into a more compact, easier-to-understand form. For example, you might use summarization to create graphs or calculate averages from a given set of data. This is one of the most familiar and accessible forms of data mining.
For more information, also see: Top Data Analytics Tools
Data Mining Benefits
Data mining can bring many benefits to companies by providing business intelligence that companies have access to. It gives insights in a relevant manner.
Some of the benefits of data mining include:
Organize reliable information
Companies rarely look at the raw numbers and are not required to create reports from scratch. Instead, a company can see their most important data each time the tool accesses the tool, erasing the need to export and compile spreadsheets from raw numbers.
Make informed decisions
Instead of an employee reviewing data and deciding on the course of action, data mining can help by automating some decisions. The decision-making process can be sped up by having data mining processes in place.
Improve customer relationships
Data mining can help gather customer data from multiple sources. This gives companies knowledge about customer trends, preferences, behaviors, similarities, and differences. That can help a company deliver a positive customer relationship by improving communication across the touchpoints.
See more on data mining: Top Data Mining Certifications
Data Mining Examples
Nearly every company on the planet uses data mining, so the examples are nearly endless. One very familiar way that retailers use data mining is to analyze customer purchases and then send customers coupons for items that they might want to purchase in the future.
In one well-publicized example, Target began sending a teenage girl coupons for baby products, such as diapers, baby food, formula, etc. Her irate father called the company to complain, and the firm apologized.
However, several weeks later, the teenager discovered that she was, in fact, pregnant. In this case, Target knew her condition before she did, based solely on changes in her purchasing habits for items not explicitly related to baby care.
Users also encounter the results of data mining every time they watch a show on a streaming service like Netflix or Hulu. These services not only use viewer data to recommend shows and movies users might like to watch, but they have also analyzed their databases to discover the characteristics of programs that are particularly popular and then produce more content with those attributes.
Some industry watchers argue that Netflix – due to its astute data mining – has become more successful than Hollywood studios at identifying and creating the kinds of content that viewers want.
Companies like Facebook and Google also use data mining to help their advertisers reach consumers with targeted content. This process is most obvious when you shop for something on a retail site and then see ads for the same item on Facebook.
However, advertisers are also using data mining in much more subtle ways that might not always be obvious to site visitors. For example, Facebook has come under intense criticism for the way advertisers have been able to target voters with messages related to elections. These scandals have resulted in greater concerns over data mining privacy issues.
For more examples of data mining: How Data Mining is Used by Nasdaq, DHL, Cerner, PBS, and The Pegasus Group: Case Studies
Data Mining Tools
Organizations have a wide variety of proprietary and open-source data mining tools available to them. These tools include data warehouses, ELT tools, data cleansing tools, dashboards, analytics tools, text analysis tools, business intelligence tools, and others. Here are some of the best data mining tools on the market:
- Zoho Analytics
- IBM Cognos Analytics
- Microsoft Power BI
- Oracle Business Intelligence
- Salesforce Einstein Analytics Cloud
- SAP Business Objects
For more information, also see: Data Management Platforms
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Bottom Line: Data Mining
With data mining, a company can gather accurate and reliable insights from data, which can be done safely. Data mining gives users privacy and protection.
By using six CRISP-DM phases, a company can garner many benefits, from making better decisions to improving customer satisfaction. When used correctly, data mining can greatly benefit any company.
For more: Data Mining Trends