Expert Guide: Data Mining (2024)

In daily operations, a business collects data about sales, customers, production, employees,marketing activities and more. Data mining can help businesses extract more value from thatcritical company asset. The knowledge gained through data mining can become actionableinformation a business can use to improve marketing, predict buying trends, detect fraud,filter emails, manage risk, increase sales and improve customer relations.

Because data mining techniques require large data sets to generate reliable results, theyhave been used in the past mostly by big businesses. But the advent of large publiclyavailable data sets — think social media posts, weather forecasts and trends, trafficpatterns — can make data mining useful for many small businesses that can combine suchexternal data with their own information and mine them together for valuable insights. Atthe same time, data mining tools are becoming less expensive and easier to use, making themmore accessible to smaller businesses.

What Is Data Mining?

Data mining is a collection of technologies, processes and analytical approaches broughttogether to discover insights in business data that can be used to make better decisions. Itcombines statistics, artificial intelligence and machine learning to find patterns,relationships and anomalies in large data sets.

With data mining, a business can discover patterns in current customer behaviors that may notbe apparent to a human analyst. It also can predict future trends. For example, applied to anew dataset of prospects, a model based on current customers could predict which prospectsare most likely to become future customers.

Key Takeaways

• Data mining combines statistics, artificial intelligence and machine learning to findpatterns, relationships and anomalies in large data sets.
• An organization can mine its data to improve many aspects of its business, though thetechnique is particularly useful for improving sales and customer relations.
• Data mining can be used to find relationships and patterns in current data and thenapply those to new data to predict future trends or detect anomalies, such as fraud.

Data Mining Defined

In a multistep, iterative process, data mining produces models that automatically look forpatterns and relationships within large data sets, then use that information to describerelationships within the data or predict future trends. For this reason, data mining is alsosometimes called knowledge discovery in data, or KDD. Often, the analysis is performed by adata scientist, but new software tools make it possible for others to perform some datamining techniques.

How Data Mining Works

Data mining works through the concept of predictive modeling.Suppose an organization wants to achieve a particular result. By analyzing a dataset wherethat result is known, data mining techniques can, for example, build a software model thatanalyzes new data to predict the likelihood of similar results. Here’s an overview:

1. Start with historical data

   Let’s say a company wants to know the best customer prospects in a new marketingdatabase. It starts by examining its own customers.

2. Analyze the historical data

   Software scans the collected data using a combination of algorithms from statistics,artificial intelligence and machine learning, looking for patterns and relationshipsin the data.

3. Write rules

   Once the patterns and relationships are uncovered, the software expresses them asrules. A rule might be that most customers ages 51 to 65 shop twice a week and filltheir baskets with fresh foods, while customers ages 21 to 50 tend to shop once aweek and buy more packaged food.

4. Apply the rules

   Here, the data mining model is applied to a new marketing database. If the company isa packaged food provider, it will be looking for 21- to 50-year-olds.

What Can Data Mining Do?

Data mining finds hidden relationships and patterns in data that human analysts and otheranalysis techniques are likely to miss. The insights it reveals can help a business makebetter decisions, increasing revenue or making marketing more efficient, for example. Butit’s important to understand that data mining finds patterns, not causal relationships. Itdoesn’t reduce an organization’s need for analysts who know the business, understand thedata and are knowledgeable about data mining techniques and processes. Only such experts canassess the value of the patterns that data mining discovers and put them to good use onbehalf of a business.

Why Is Data Mining Important?

More products are becoming digital, as are more payment transactions and customerinteractions. As this happens, more companies are finding that their data, often alreadystored in a data warehouse waiting to be analyzed, is just as valuable as their products andservices. In this context, data mining gives companies a competitive edge by helping torapidly find business insights hidden in all the data from all those digital businesstransactions. The benefits are almost endless. Understanding customer behaviors can lead tonew product, service or marketing ideas. Detecting intrusions can prevent a devastatingtheft of customer data.

Who Uses Data Mining?

Any company can use data mining, but those with large data sets will get more reliableresults. The patterns and relationships discovered with thousands of customers are morelikely to accurately predict future customer behavior than those discovered with onlyhundreds or dozens. But the market is also broadening as large data sets become publiclyavailable and data mining technologies become less expensive and more accessible to eventhose without a background in data analysis.

So, while data mining has traditionally been used in industries that generate a lot of data,such as in the credit card industry, health care or oil and gas exploration, it’s alsogaining ground in education, customer relationship management andmarketing, among many others.

Key Data Mining Concepts

As in many fields, data mining uses its own vocabulary as shortcuts to identify importantconcepts. Knowing these concepts is important to master data mining and understand what itcan do for a business.

• Data cleansing: Also called data scrubbing. The process ofcorrecting errors and omissions in data before analyzing it.

  See Also

  How Data Mining Works: A GuideWhat Is Data Mining? How It Works, Benefits, Techniques, and Examples

• Model: The knowledge discovery of relationships among data, oftenexpressed as rules.

• Target: The goal of data mining, for example, identifying high-valuecustomers.

• Predictors: The related data that leads to the target.

• Case: A specific instance of data, such as a particular customer’sinformation, that is plugged into the model to determine its relationship with thetarget. For example, is this customer likely to return for repeat sales?

• Market basket analysis: Discovering buying behaviors of customersbased on past buying patterns, often using data collected from company loyaltyprograms.

• Machine learning: Algorithms that use known cases to discover othersimilar or identical cases in large data sets.

Data Mining Techniques

Depending on the company’s goals for data mining, different techniques are used to producemodels that fit the desired outcomes. The models can be used to describe current data,predict future trends or aid in finding data anomalies.

1. Descriptive model: Descriptive analytics finds patterns andrelationships in current data.

2. Predictive model: Used to predict future outcomes, such as whether aloan applicant is a good risk, or to make financial forecasts, suchas upcoming sales.

3. Outlier Analysis: Used to find anomalies, that is, data that doesn’tfit neatly into patterns. Outlier analysis is especially useful in fraud detection,network intrusion detection and criminal investigations.

Advantages of Data Mining

Data mining can deliver big benefits to companies by discovering patterns and relationshipsin data the company already collects and by combining that data with external sources. Hereare just a few of the potential advantages data mining can bring to a business. The resultsof data mining are often demonstrated in dashboards within business software, whichaggregates metrics and key performance indicators anddisplays them with simple-to-understand visuals.

• Optimal product/service pricing: Using data mining to analyze theinterplay of pricing variables, such as demand, elasticity, distribution and brandperception, can help a business set prices that maximize profit.

• Better marketing: Data mining can help a company get more value outof their marketing campaigns by segmenting customers with different behaviors,optimizing engagement by segment or providing insight to aid development ofpersonalized ad creative. The results of ad campaigns can often be demonstrated insales dashboards.

• Heightened employee productivity: Analyzing employee behaviorpatterns and viewing KPIs in HR dashboards can lead to strategies for boostingemployee engagement and productivity.

• Improved customer retention: Understanding customer behavior canimprove customer relations, reducing churn.

• Increased cost efficiency: Manufacturing costs, for example, couldbe lowered through many different data mining analyses, from insights into supplierpricing behavior to better understanding customer buying patterns.

• Higher product/service quality: Finding and fixing areas wherequality falters can decrease product returns.

Privacy Concerns

No organization should begin a data mining initiative involving customer and employeeinformation without careful consideration of the potential privacy issues involved and theethical questions that may arise. Data mining algorithms can find patterns and relationshipsthat may lead to identifying people even when care is taken during the data collectionprocess to protect their privacy. Therefore, any organization planning to use data miningwhere people are involved should include privacy and ethics experts to help guide their workfrom the very beginning of the project.

Data Mining Process

Data mining is an iterative process that normally begins with a stated business goal, such asimproving sales, customer retention or marketing efficiency. The process works by gatheringdata, developing a goal and applying data mining techniques. The selected tactics may varydepending on the goal, but the empirical process for data mining is the same.

1. Define goal: Do you want to learn more about your customers? Do youwant to cut manufacturing costs? Do you want to increase revenue? Do you want todetect fraud? Clearly identify the desired outcome of data mining implementation toget started.

2. Gather the data: Data mining can answer all those questions, buteach one requires a different set of data. Often the data comes from multipledatabases, for example, customers and orders.

3. Cleanse the data: Once selected, the data usually needs to becleansed, reformatted and validated.

4. Get to know the data: Become familiar with the data by running basicstatistical analyses and building visual graphs and charts. This is where analysts identify variablesthey believe to be most important to the goal and begin to form hypotheses that leadto a model.

5. Build a model: Model building is where the data mining process ismost iterative. Analysts choose one or more of the technology approaches discussedin the next section and apply one or more to the data being mined. The possibleapproaches are better suited to different questions. The outcome of this step is tofind the data mining technology approach that produces the most useful results. Thismay require a reiteration of step three because some models require data to beformatted in specific ways.

6. Validate the results: Whichever techniques are used, examine theresults to validate that the findings are accurate. If not, go back to step No. 5 —rebuild the model.

7. Implement the model: Use the discoveries to fulfill your originalbusiness goal.

Data Mining Technology

Much of data mining uses well-known algorithms that cluster, segment, associate and classifydata. Each technique builds a model which is then used to describe current data or predictoutcomes for new data cases.

• Classification: Assigns data to multiple categories or classes. Forexample, a loan applicant can be assigned to a low, medium or high-risk category.Usually, the categories for the model are predefined based on previous analysis ofthe data.

• Anomaly detection: A form of classification that uses machinelearning to detect data that does not fit a class. For example, anomaly detection isused to find fraudulent credit card charges.

• Clustering: Identifies groups of similar data. For example,clustering can be used to find customers with similar buying habits.

• Association: Generates a probability of multiple events occurringtogether. One application is “market basket analysis,” which discovers when two ormore items are frequently bought together.

• Regression: Using a data set where values are known, regressiontechniques attempt to predict a value based on multiple attributes. For example,regression could predict sales based on the advertising dollars, month, websitevisits and other financial attributes.

• Neural networks: A form of artificial intelligence that mimics thehuman brain to find relationships in data. Neural networks have multipleapplications, for example, in predicting customer behavior.

Data Mining Use Cases and Examples

As individual organizations collect larger volumes of data, more public data sets are madeavailable and data mining technologies become easier to use and less expensive, thepotential applications of data mining are expanding. Examples of data mining improvingprocesses and delivering benefits can be found in multiple business segments. And it’s easyto extrapolate from these uses to imagine how your organization could deploy data mining.Here are only a few of the countless ways data mining is already in use.

• Banking: Data mining is used to predict successful loan applicantsas well as to detect fraud in credit cards.

• Retail: Create effective advertisem*nts based on past responses.

• Insurance: Predict probability and costs for future disasters, basedon past hurricanes or tornadoes.

• Grocery stores: Analyze market baskets to find products usuallybought together. Running a sales promotion on one item can improve sales of theother item at its normal price.

• Manufacturing: Implement just-in-timefulfillment by predicting when new supplies should be ordered or whenequipment is likely to fail.

• Customer relationship management: Identify characteristics ofcustomers who move to competitors, then offer special deals to retain othercustomers with those same characteristics.

• Security: Intrusion detection techniques use data mining to identifyanomalies that could be network break-ins.

History and Evolution of Data Mining

People have been manually analyzing data to find patterns for centuries. The rise of digitalinformation technology and databases beginning in the 1950s was, of course, a game changerfor such analyses. The term “data mining” came into use around 1990 as research into thetechnologies and techniques described above was put to practical use in the computerdatabase community. Data mining has grown in popularity, mainly because of its demonstratedvalue to companies.

Today, large data warehouses with information collected from multiple sources in varyingformats, combined with larger storage capacities and faster computers, allow even smallcompanies to reap the benefits of data mining. Data mining algorithms have also grown insophistication. For example, relatively new machine learning techniques can inferrelationships not found by previous algorithms.

Future of Data Mining

The fundamental technologies underlying data mining — computing, databases, data warehouses,neural networks, machine learning and artificial intelligence — continue to become morepowerful, less expensive and easier to use. Therefore, they are becoming more accessible tomany more — and smaller — businesses. So, the overall arc of data mining’s future is that itwill be put to increasing use by many more, and more diverse, kinds of businesses.

Meanwhile, more data about the world we live in is becoming available, opening up thepotential for future data mining techniques to evolve specifically for analysis of what wenow consider nontraditional data. This includes video, audio and images; geographical andspatial data; and mobile phone data, and it’s often stored in what’s known as a data lake.Similar to a data warehouse, data lakes are repositories for information, but the data doesnot have to be structured and is stored in its natural or raw format.

The foreseeable future for data mining includes its potential use in everything from themundane — think finding the best airfares at the moment or the best prices for portablegenerators in Long Island, N.Y. — to the profound, like new medical treatments ordiscoveries about the nature of the universe.

Data Mining Software & Tools

In the past, data scientists had to use programming languages such as R and Python in datamining applications. However, there are now tools that facilitate data mining and softwarecan perform many of the necessary tasks and help identify rules and other insights from yourdata. Graphics capabilities are usually included in these tools for visualizing the resultsin pre-configured and customizable business intelligence dashboards.

More recently, cloud-based data warehouse software has become available for companies thatwouldn’t otherwise be able to afford data mining or have the IT infrastructure necessary tosupport it. These tools represent a significant simplification of what it takes for anorganization to pursue data mining. They can house a business’s own data in the samerepository as external data and can include structured as well as semi-structured data. Theyalso represent a step up in computational power, which means that data mining analyses canoccur faster than before.

By combining all of an organization’s data in a single warehouse, a business can get a morecomprehensive and holistic view of its operations. And by including externally acquired dataand mining it together with internal data, a business can discover new opportunities.

Conclusion

Data mining opens opportunities for companies to improve their bottom lines by findingpatterns and relationships in data they already collect. It has proven benefits in everyindustry. Meanwhile, the technologies required to perform data mining are becoming moreautomated, easier to use and less expensive, making them more broadly available to smallerorganizations. The future opportunities for data mining are limited only by a company’simagination.

Data Mining FAQs

What do you mean by data mining?

Data mining combines statistics, artificial intelligence and machine learning to findpatterns, relationships and anomalies in large data sets. From this knowledge, a businesscan discover current behavior and predict future trends.

What is data mining used for?

The knowledge gained through data mining can be used in almost unlimited ways — limited onlyby the availability of data and the imagination of an organization to use it. A few waysdata mining is used today include to improve marketing, predict buying trends, detect fraud,filter emails, manage risk, increase sales and improve customer relations.

What skills are required for data mining?

Data scientists have developed complex data mining algorithms that are now implemented insoftware, enabling companies without special knowledge to mine their data. But data miningstill requires analysts who understand the nature of the business, as well as the data thebusiness generates or acquires from external sources.

What is data mining and its types?

Data mining can be used to describe current patterns and relationships in data, predictfuture trends or detect anomalies or outlier data. It does this using three primary models,or types: the descriptive model, which finds patterns and relationships in current data; thepredictive model, which is used to predict future outcomes; and outlier analysis, whichfinds anomalies — data that doesn’t fit neatly into a pattern.