Querying or mining

As a data developer, you will almost always be in the habit of querying data. Indeed, a data scientist will query data as well. So, what is data mining? Well, when one queries data, one expects to ask a specific question. For example, you might ask, What was the total number of daffodils sold in April? expecting to receive back a known, relevant answer such as in April, daffodil sales totaled 269 plants.

With data mining, one is usually more absorbed in the data relationships (or the potential relationships between points of data, sometimes referred to as variables) and cognitive analysis. A simple example might be: how does the average daily temperature during the month affect the total number of daffodils sold in April?

Another important distinction between data querying and data mining is that queries are typically historic in nature in that they are used to report past results (total sales in April), while data mining techniques can be forward thinking in that through the use of appropriate statistical methods, they can infer a future result or provide the probability that a result or event will occur. For example, using our earlier example, we might predict higher daffodil sales when the average temperature rises within the selling area.