Business Intelligence Vs. Data Mining: What's The Difference?

With customers connecting to businesses from numerous access points (websites, social platforms, email, chat, etc,) the sheer quantity of information at a company’s fingertips is staggering. The trick to gaining useful insights from that information is applying the right data tactics. That’s where concepts like business intelligence (BI), data mining, and integration come into play. Business Intelligence and data mining are two different techniques that can work together to inform strategic business decisions. But before getting started with either, we must answer a few questions: What’s the difference between the two? How do they relate? How do they tie into larger projects like data integration? Let’s discuss.

Data Mining Defined

Just as Snow White’s small friends excavated for diamonds, data mining is like excavating for insights. It helps businesses uncover the “diamonds” in their datasets by searching through the massive pool of information and making sense of it. Data mining looks for patterns, anomalies, and correlations between small, specific datasets so you can understand what is relevant for deeper analysis. Through data mining, you are able to extract important data, analyze it from various perspectives, and then summarize that information in a useful format.

There are many different data mining techniques, and they’re all used for different purposes. But our real question today is how data mining relates to Business Intelligence (BI).

Here’s the simple answer: Data mining tells you “what” is important so you can use BI to learn “why” it’s important and “how” it should be applied. 

Business Intelligence Defined

Business Intelligence analyzes data for the purpose of understanding a company’s historical trends and predicting new ones. Unlike data mining, which looks at smaller segments of data, BI focuses on larger volumes of data – enterprise levels of data, if you will. It presents the data that was patterned, interpreted, and formatted during data mining in a format that’s easy for executives to pull insight from.

Business Intelligence data is shown through visual representations like reports, charts, graphs, and dashboards. These visualizations of data allow stakeholders to quickly understand the data’s significance to business operations. From there, they can make some strategic decisions.

Business Intelligence and Data Mining Together

Data in its raw form is unstructured and difficult to draw conclusions from. The act of data mining simplifies those complex datasets so business intelligence tools can draw insights from them. As we mentioned earlier in this post, data mining detangles data to reveal “what” we’re looking at so the BI tools can answer “why” and “how” to use that information for better business. Together, data mining and BI provide a foundation for streamlined processes and smarter business initiatives.

How Integration Overlaps

Data mining and BI have a lot in common with integration. For example, data cleaning is an important aspect of both data mining and data integration. The idea is that, if you want to draw meaningful conclusions from your data, you must first make sure the data itself isn’t junk. Data cleaning during data mining helps prepare the best data for BI initiatives. For integration, connecting two solutions whose information is accurate means that the resulting analytics will be a true reflection of your business.

Both data mining and data integration use ETL (or ELT) strategies. ETL stands for Extract-Transform-Load and is used during data preparation for projects like data mining and integration. Whether you use ETL or ELT, you are pulling out the data (extracting) and then either transforming it to a usable state and loading it into a destination or loading it where it needs to go and transforming it afterwards. Transforming the data updates the format to make the information more easily understandable—an attribute that’s crucial in both effective data mining and integration.

To successfully implement BI and uncover the most comprehensive analytics, organizations require integration between multiple sources. Well-defined integrations use best practices to standardize data for BI initiatives and give IT teams the flexibility they need to implement custom processes that support their BI goals. BI relies on massive quantities of information sourced from different places to make intelligent predictions and accurate historical findings. Integration enables the data transparency necessary to analyze information for these purposes.