2 posts tagged with "data mining"

Welcome to my second of 3 posts around data terminology. In the first post, I focused on terminology that happens before analysis even starts. In this post I will discuss some of the terminology around data analysis and communication approaches.

Data Analysis Approaches​

First, let me explain what I mean by data analysis approaches because this can also get somewhat confusing. When those in the industry think of data analysis, they probably think of the types of data analysis (descriptive, explanatory, predictive and prescriptive). Or, they might think of specific techniques like regression or cluster analysis. This is NOT what I will be discussing in this post. (However, if you are interested in learning more about types of analysis, I do cover them in considerable detail in my course Building a Data Mindset).

No, what I am referring to are overarching approaches because this is really where there is a lack of clarity in terminology and people often use the terms interchangeably.

I always think of the gold rush in the 19th century when I think of data mining. Just as these miners were looking to strike it rich in gold, data miners are hoping to strike it rich in insights. Data miners are trying to find that "golden nugget" of data or information that will help boost their business profits or take them from a struggling start up to a million or billion dollar going concern.

Data mining has been around for a long time but with the improvements in computing power and in the era of big data, it is a key component within data science. It is also used interchangeably with the term Knowledge Discovery. There are really two big areas that data mining focuses on: descriptive modeling and predictive modeling.