Skip to Content

Data Analytics have transformed the business world over the past decade.  The ability to store, organize and mine data has grown exponentially.  Many carriers have adopted some form of analytics to help drive their business.  However, with the rapid increase in the volume and complexity of information in today’s business world, an analytics approach created a decade ago may not be providing the return on investment that it once did.

 In Competing on Analytics: The New Science of Winning by Thomas Davenport and Jeanne Harris, the authors observe, “Regardless of the approach, for companies to sustain a competitive advantage, analytics must be applied judiciously, executed well, and continually renewed.”

 

 Retrospective Analysis

Traditional analytics or “Business Intelligence” applications leverage database tools to mine data and summarize what has already happened within a business or business segment.  Many companies have created executive dashboards, reports and scorecards to enable employees to view the data and help understand its meaning. 

 While it can provide valuable insight into a company’s performance, the essential problem with retrospective analysis is that the company is spending its time looking in the rear view mirror at what has already happened.  The belief that past performance is indicative of future events was greatly challenged by the turmoil in the real estate and banking industries and the resultant economic malaise of the past few years.  While historical data provides valuable insight into a businesses health, it should not be relied on as the sole source of truth.

 

 Predictive Analytics

What happens when a company mines data for key performance indicators (KPI’s) and applies mathematical scoring models against those KPIs?  When successfully executed, a company can predict customer or employee behavior based on past experience.  This is an effective mechanism for identifying sales patterns and establishing marketing campaigns. 

For example, some companies have traditionally depended on credit scoring as one key performance indicator in the underwriting process.  However, some emerging studies show that a customer with a weakened credit score, related exclusively to mortgage related debt, may still be a good underwriting risk.  According to TransUnion in its study “Life After Foreclosure”, “ The ability to identify ‘life event’ mortgage defaulters versus chronic defaulters can open up profitable, low-competition target segments.”

 This is just one example of a significantly changed market resulting from the housing meltdown over the past five years.  If a company relies solely on retrospective analytics, it would likely miss these changing market conditions.

To report this post you need to login first.

1 Comment

You must be Logged on to comment or reply to a post.

  1. Deepak K Gupta
    Tony,

    It is good to see the views of yours on data analysis.

    Can you share more insights on predictive and retrospective problems which can arise with some data organization as far as master data is concern.

    (0) 

Leave a Reply