Creating an enterprise data strategy is not for the faint of heart.  It first requires a commitment from the top and an acknowledgement that data is a corporate asset that must be managed and protected like any other asset.  For insurance organisations this should not be a difficult concept as data is more important to them than perhaps many other industries.

Given the difficulty of getting executive buy-in, it’s not surprising that only one in 10 organisations have an enterprise data strategy.  To harness data as a corporate asset requires a mix of “soft” skills, required to build sustainable strategies and manage change, and “hard” stuff, which applies a portfolio of data management tools and techniques to ensure the delivery of consistent, high-quality data that’s aligned with business strategies and initiatives.

 

One of the key elements in managing data is reconciling enterprise and local requirements for data.  Most organisations see-saw between these two extremes, but astute managers foster a dynamic interplay between the two polarities, embracing both at the same time without getting stuck at either end (or somewhere in the middle.)  Organizations with highly centralized approaches to data management need to distribute development back to the departments (as scary as that might be) while still maintaining high standards for data integrity and quality; organisations with fragmented data and lots of analytic silos need to implement  processes and controls to standardise shared data across functional areas.

A key element in any data strategy is to design and implement a data governance program.  The fundamental premise of a data governance program is that the business designs and runs the program and the IT department (or data management team) executes the policies and procedures defined by the data governance team.  In addition, a good data governance program incorporates change management practices that accelerate user adoption and ensure long-term sustainability.

Finally, the data management team needs to implement a broad portfolio of data integration tools to accelerate and automate data management tasks.  As the type and volume of data that users want to analyze expands, data management teams also need to expand on the tools they can use to manage and manipulate this data.