“We’ll know that data has finally made it as the second most valuable asset in our company when all senior executives understand what 'metadata' means."

This was an absolutely wonderful quote from a senior leader at a large insurance company with accountability for an enterprise data management initiative. (Even better, the first most valuable asset for this insurer was its employees.)

A few short years ago, data management had not made the top list of projects at many insurance companies, but now we’re seeing insurers begin to make strategic and organizational investments in their data management initiatives.

But this executive’s comment is well put – data management applies to all data from capture to presentation. Data management does not start in a data warehouse – it starts with the point of entry, which makes data quality everybody’s responsibility. This same executive continued with the following statement: “Data management needs to be a high priority for everyone from the claims department to the sales and marketing offices, all the way from the bottom to the top. Nobody can ever think that data quality is somebody else’s job.” And he recognized the challenge that “people are focused on getting their job done and assume that the data will get fixed later.”

Which reminds me of an interesting anecdote I heard about an insurer that got into insuring race horses: Their existing policy administration system wasn’t built to manage the specific fields for that risk, so race horse names would end up in the policy number field because it was a unique identifier for the policy – true story!

For the insurer with the data management initiative, they began to ask the right questions: How do we get our employees to think of the entire data flow and not just their business process? How do we make people accountable for the quality of data at their point in the data lifecycle? How can we capture local subject matter expertise in our data warehouse? How do we get senior executive buy-in across the board on the importance of data management? How do we integrate these processes without slowing the business down?

While they couldn’t answer all of these questions at once, they established some key data management initiatives to start them down the right path of changing the organization’s data culture by:

• Establishing enterprise governance standards and best practices.
• More governance and oversight of third party data assets.
• Establishment of enterprise quality standards and best practices through data stewardship programs.
• Integration of disparate data dictionaries and repositories that facilitates consistency of common information across business units.
• Incorporation of data management best practices in the system development lifecycle.
• Establish a communications mechanism to better engage and inform business resources.

If you’re just starting on your data management initiatives and you’re looking for a place to start, the Data Management International group and the Insurance Data Management Association offer frameworks, certifications, training and best practices that insurers can leverage. Keep in mind that it’s about doing what’s right for your organization – the frameworks provide starting points, but you’ll need to assess your current data management capabilities and identify opportunities that will drive the most business value.