Insurance-Canada.ca - Where Insurance & Technology Meet

Data Analytics and Understanding: Strategies for Balanced Growth

There’s an old story that goes something like this:  The CEO has commanded that all decisions have to be backed by solid market information.  This causes a parade of people to form outside the IT VP’s door looking for data and information.  After getting tired of providing the same answer, the overworked exec puts up a sign which reads, “If it’s data you want, come on in; if it’s information you’re looking for, you’ll have to find it elsewhere.”

We are living in a world where data is growing geometrically.  The recent Insurance-Canada.ca Technology Conference had one-third of its sessions devoted to data and analytics.  We need to make sure that understanding (information) grows at the same pace.  There are some techniques that can help.  Most important of which is sharing information.  We’d like to know what you do to balance data analytics and understanding.

If you ask me anything I don’t know, I’m not going to answer (Yogi Berra)

We have written about the importance of a new breed of employee – the data scientist – to use analytics to pull information out of increasing large amounts of data, and to use sophisticated tools to provide dashboards and other visualization tools to help put big data in an understandable context.  But there is still one thing missing:  human interaction.

If the significance of analytics depends on the ability of business users to understand and use them, then there are two responsibilities:. The users must be prepared to strive to understand more sophisticated constructs and and the analytics folk must be prepared to facilitate the understanding.  Some techniques are emerging to address this.

Every Picture Tells a Story, Don’t It? (Rod Stewart)

In a recent Tech Trends report, Deloitte devotes a chapter to ‘Finding the Face of Your Data’, in which the authors define the problem succinctly.  After noting that some businesses are rushing to implement Big Data solutions, the authors note: “Big data alone, however, creates no new value if it doesn’t lead to insights – about questions you haven’t answered before, or perhaps more importantly, about questions you didn’t know you could ask.”

This is the classic I-don’t-know-squared problem: I don’t know what I don’t know.  In a recent post on Insurance & Technology, Rachel Alt-Simmons, senior industry consultant for Insurance at  SAS, suggests that training analysts to use soft skills such as story telling is an important element in facilitating understanding.

Alt-Simmons give an example of a meeting during which she was presenting a new decision tree model for use in field offices that made use of some sophisticated mathematics.   She made the mistake of showing the model in detail.  The result:  “The meeting was almost derailed when the executives began to ponder why the split happened on a specific dollar amount and we lost sight of the true objective: the ability to predict which of our key producers were likely to stop selling our products.”

Don’t know about you, but I’ve been in some of those meetings.

The solution, Alt-Simmons suggests, is to ensure that presenters are prepared to use artistry in explaining the complex concepts.  Noting that this is not generally part of an analyst’s formal training, she notes examples of how some insurers are doing this on the job.  Some are providing communications training, others are less formal (but more fun).  Alt-Simmons writes:

One insurer holds a monthly contest for the “best visual.” Analysts submit their graphs and analysis in a competition that is judged by a group of non-analyst peers. In addition to getting valuable and objective feedback, participants can win a variety of prizes – the fun of the competition encourages participation and raises the collective understanding of the entire organization.

What Can Users Do?
Executives and managers are not off the hook.  We cannot expect to just pay the bills and be spoon -fed the information we need.  We have a number of responsibilities that the Deloitte report notes, including:
  • Hiring the right talent for the business.
  • Understanding the data that we have internally, those that we have access to externally, and (most significantly) the data we don’t have, but need.
  • Setting up governance structures and processes – such as Master Data Management – to support initiatives.
  • Driving projects which are properly scoped and managed to provide immediate benefits while contributing to a data-driven future.
What Do You Think?
Have you embarked on new initiatives that bring data scientists and users together to achieve goals around data use and understanding?  What worked?  What didn’t?  Please share and contribute to our common knowledge.

 

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