What is data mining and how can it benefit your business? In this FAQ, we’ll answer some of the most commonly asked questions about data mining, data-driven methods, and how data can be used to boost organizational performance.
What Is Data Mining?
Data mining refers to the extraction of information from data.
It is a common practice in business and can be used for business intelligence, competitive intelligence, and other business projects.
Insights gained from data mining, for instance, can offer insight into:
- The customer experience
- Competitors’ activities
- Business processes
- The organization’s performance
- The employee experience
The applications of data mining are virtually limitless, and this is perhaps why data-driven methods have become so central in the modern business world.
What Are the Benefits of Data Mining?
Like other data-driven practices, data mining is focused on extracting reliable information from the real world and applying that information in business.
Using data to make decisions is beneficial for several reasons:
- Employees can use objective data to make decisions, rather than emotions or subjective opinions
- Advances in digital technology allow businesses to comb enormous data sets – i.e., big data – to gain insights
- The more data a company has access to, the more it can use that data to gain a competitive edge
Data, in short, is knowledge, and the right knowledge can vastly improve the accuracy and efficiency of business operations, no matter where or how it is applied.
Big Data vs. Data Mining – What’s the Difference?
Data mining is just one practice within the field of data science.
Here are a few other terms and concepts that can help offer some context:
- Data science is the field and all the techniques dedicated to extracting and using data in fields such as business, science, and computer science
- Big data refers to massive data sets that must be analyzed with the help of computers
- Analytics refers to the analysis and extraction of meaningful patterns from data
- Statistics is the branch of science dedicated to collecting, analyzing, and using data
- Machine learning is a type of artificial intelligence (AI) that is frequently used for data mining and other data science applications
Many of these terms are clearly interrelated and, in some cases, their definitions overlap. Those who want to use data in their business, however, should consider diving deeper into these terms to better understand the differences between them.
What Steps Are Involved in Data Mining?
Here are the key steps that data teams will follow when they embark on a data mining project:
- Define the business problem and scope. Data mining is designed to solve business problems. During this step, stakeholders should define this problem clearly, in the form of a question, since that question will guide the rest of the data mining process.
- Understand the available data. There are different types of data that can be drawn from a variety of sources. During this stage, the data team will assess available data and choose which data set can be used for this process.
- Prepare data. Preparing data can involve data cleaning, data transformation, and otherwise improving the quality of the data for use in the next steps.
- Modeling. A model refers to the technique – that is, the statistical technique – used to gain an understanding of the data.
- Evaluate. Once models have been created, they can be tested to assess their viability, then further refined if needed.
- Apply the model. Models are used in a real-world context.
Finally, after applying the model in the real world, they can be further refined over time to improve their accuracy and usefulness.
What Are the Limitations of Data Mining?
As with any other business process, data mining has its use cases and its deficiencies.
Data mining can, as we saw, be very useful for gaining insight into real-world events, extracting patterns from existing data sources, driving revenue growth, and much more.
However, data mining does have limitations.
For example:
- Data-driven methods depend on having a certain amount of data
- Data quality can also impact the outcomes of data-driven processes
- The costs associated with data mining can be prohibitive for some companies
- Overreliance on data can result in oversights or missteps
It is important to take the results of data mining projects with a grain of salt. Data models are just models and they are, by definition, incomplete. Human judgment should always play a role when analyzing data and using it to make decisions.
Where Can I Learn More About Data Mining?
There are countless resources available online for those interested in learning more about data science.
Here are just a few:
- Tableau’s blog has some excellent introductory articles on data science topics
- Towards Data Science offers a wide range of articles on data science, many of which are geared towards professionals or advanced practitioners
- Udemy has a number of courses that cover data science topics
- IBM has several good introductions to data science, data mining, and related topics
Finally, our very own digital adoption blog includes posts on data, big data adoption, and other data science topics.
