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How CFO’s Mine Financial Opportunities from Big Data


To do your job well, you need access to the right information at the right time. Unfortunately, that’s not always an easy task.But it can be. By designing effective dashboards you can view what matters most, so you’re able to make faster, smarter decisions.

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Today’s chief financial officers are well aware that data analytics and Big Data are no longer just nice to have if budgets permit, but a must have in order to compete and stay ahead of the curve. Responsible for managing, reviewing and analyzing all reports and data, CFO’s are often inundated with information.  

So how do Chief Financial Officers handle it all when they also have to juggle several other responsibilities?

According to a CFO.com article, Analyzing Big Data, before CFO’s come up with a solution to all the information, they first need an understanding of Big Data. With a comfortable understanding of Big Data, CFO’s are able to begin a process to discover and extract new patterns in large data sets, known as data mining. 

A great example of an organization that utilized data mining practices to successfully predict buying habits of customers is the well-known retailer, Target which brings us to the first, of two, common and practical applications employed in data mining for finance:

  1. Clustering Analysis is the task of dividing data into groups (clusters) to identify patterns. Advanced data mining software can automate the clustering process--grouping similar data, detecting repetitions, and making sense of the information. The software will group relevant data into similar “clusters” simplifying the process of deep analysis, pattern recognition, and having a full-picture of the information. 

    Going back to our example, Target reviewed clusters of customers who were going through a major life event and discovered that their buying habits changed during big life events. Information collected helped Target identify about 25 products, from vitamin supplements to lotion and establish a “pregnancy prediction” score. Target used their insight to then send light advertisements with baby-related products to women based on their prediction score. As a result, sales of mom and baby products increased. 

  2. Association Rules: is another widely utilized data mining technique that discovers useful patterns and relationships within data sets. The rule applies a condition clause similar to an “If-then” rule. For example, “If a customer buys a product 3 times a month THEN they are 50% more likely to join a company membership club.” 

cfo-insightsCFO’s do not use one technique over the other; instead they use both in conjunction to help yield fast and insightful information. With busy schedules, quality human analysis is not realistic anymore for most CFO’s. There is not enough time in the day for them to review all information, compare, and find meaningful discovery. 

The experts at DIO-soft help organizations find value from big data through tools that account for customer, employee and competitor behavior and risk. To learn more, ask the people who know best.