Data Mining: Key Element in Business Analytics

“Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful.” - Chip & Dan Heath

True to the quote, there is always a story behind data, and data mining is all about joining the dots and understanding the pattern to bring those stories upfront. Let’s look at a use case where data mining has already proved its mettle.

Brick and Mortar Supermarket Retail 

Target, the supermarket chain in the US, went on to present a very unique case study. The Target Loyalty program issues cards with a unique ID for each customer. Now each of these Ids is liked to the email ids and credit cards thereby creating a bucket for storing all the purchase history of the customer. The very famous Andrew Pole, the master statistician of Target is famed to have predicted pregnancies, almost accurate due dates. As the customer data was run through various predictive models in Cole's computer, he could recognize certain buying patterns signaling an impending pregnancy. Right from the purchase of a certain lotion to a bag similar to a diaper bag and zinc along with magnesium supplements was enough to tell what the customer was expecting. Once this insight was revealed through a predictive score, Target started sending coupons for maternity wear and baby strollers. This is a glaring example of how data mining plays a crucial role in business analytics.

Five Point Value that Data Mining Drives in Business Analytics 

The process of mining the data from the depths of dungeons of ever-increasing data involves five key processes. These are:

• Extraction, Transformation, and Loading of Data - This is the very first stage of data mining. This involves culling data from already functional systems and then parsing it through a standardized format, storing it into the data warehouses.
• Storage and Management of Data - These centrally placed data are further fed into different database systems in the likes of OLAP. 
• Access to Data - As the name suggests each of these data stored in different databases is made accessible to relevant stakeholders. This enables them to determine the organization of data. 
• Analysis of the Data - This stage revolves around the analysis and sorting of data basis queries.
• Presentation of Data - The analyzed data then is further represented through charts and graphs to deliver the relevant business insight.

So, it is evident that data simply doesn’t make sense, it has to be translated into information. This particular translation is carried out during the data mining process. Through data mining, organizations can achieve the following such as:

• classify the data
• cluster it into appropriate data sets,
• derive associations between two data points
• detect datasets that don’t match, indicating a danger
• predict future events 

Maneuvering data calls for a specific skill set, and this needs academic intervention. IIM Calcutta is currently offering a Executive Programme on Business Analytics (EPBA) designed to enable new-age professionals to become competent in analytics along with the ability to handle bigdata.