If data is a strategic asset, then data governance needs to be a business discipline. Regardless of the industry, it’s probably only a matter of time before every business will be measured based on their ability—or inability—to govern data appropriately. In this digital-first age, we’re moving at jet speed to a state where data will become a business’s great differentiator, and it will make the topic of governance all the more important.
But executing a mature data governance program is fraught with challenges, especially if your business is going through a digital transformation and you’re migrating data to a cloud environment. As I’ve explained elsewhere about the concept of a data governance toll gate, before you move anything to the cloud or a new system, data governance can be used to toll gate or ensure data has proper meaning, ownership, lineage, and that quality rules and privacy controls are defined and measurable.
The idea of a toll gate provides a formal checklist for CDOs or organizations that are looking to expand data governance to address growing needs for regulatory compliance, self-service analytics, customer experience, digital transformation, and more. The toll gate process ensures that data is turned into actionable insight by capturing its meaning, the ownership, its source of origin, as well as its quality and privacy controls.
A quick customer story that comes to mind is of a large insurance provider going through digital transformation. They were faced with three main challenges, and had set up three enterprise teams for resolution:
During this company’s digital transformation journey, the three teams operated via a toll gate process making up the three pillars of a toll gate structure. The Data Governance Council identified the key business areas, data domains, KPIs, and policies where data silos had the greatest impact. The Enterprise Data Stewardship then operationalized governance by capturing business definitions and linking corresponding technical metadata for lineage. And lastly, the Enterprise Data Quality team documented all data quality controls, along with stakeholder approval and populated scorecards and dashboards.
Using this process, they were able to transform their data into actionable information. This organization gained agility to drive digital transformation, provided one platform for self-service, and delivered a holistic view on all data quality controls.
Additionally, I believe this insurance organization gained more confidence in the value of their data and—because they have made data governance a business discipline—they should also realize a positive downstream effect, which will allow them to be even more competitive in the market.
I’m looking forward to speaking at the upcoming Data Governance and Information Quality conference in December in Delray Beach, FL. I’m curious to learn more about how other businesses are maturing with their data governance programs and whether they believe organized data governance provides a real competitive advantage.
In my session, I’ll provide a market perspective on data governance, where I’ll provide additional details about the toll gate approach, and feature this insurance company story with even more detail about how they’ve partnered with Informatica to make data governance a business discipline.
I look forward to seeing you there as well and continuing the conversation.