Data Governance: Bridging the Gap Between Data Creators and Consumers

Data governance is foundational to many business strategies. Digital efforts expose corporate data externally; incomplete, dated, or inaccurate data can damage an organization’s reputation. Many customer initiatives depend on a 360-degree view of the customer relationship. Efforts to improve underwriting results require risk data and cause-of-loss information. Timely, complete, and high-quality data is critical for achieving many corporate goals, yet poor data quality persists. Insurers are still contending with “garbage in, garbage out.”

Data governance can address the data quality challenge, but many organizations find it hard to align the enterprise behind a data governance initiative. Insurers fall into two camps: data creators and data consumers. Data creators are responsible for “garbage in,” and data consumers are responsible for “garbage out.”

Data creators—underwriters, adjusters, CSRs—create data. They focus on efficient transaction processing. The quality of data entry is a secondary concern for them; it may not affect them at all. (Paying attention to data quality may slow them down if anything.) Conversely, data consumers—management, actuaries, finance, analytics teams—have a considerable stake data quality

The key to quality data is bridging the gap between data creators and data consumers, making data governance tangible, impactful, visible, and successful. Consider a multi-pronged approach:

Self-Interest

One approach is for insurers to rally their organizations around data problems that matter to both sides. A popular starting point is the customer. An insurer may not have current emails or mobile numbers, for example, or perhaps the insurer isn’t sure if the view of the customer policies is complete. This lack of data quality makes it harder for CSRs, underwriters, and adjusters to do their jobs and impedes analytics initiatives. Appealing to self-interest and educating data creators on the benefits that data governance will accrue can be effective.

Partnering

Many data quality challenges are not addressable by self-interest. These instances will require insurers to build support across the organization. One insurer that Novarica knows was failing to capture accurate loss cause data, which prevented actuaries from identifying profit drivers and fine-tuning rating. The data governance leader identified the parts of the organization that had an interest in the cause: actuarial, underwriting, management, and finance. These interested parties became partners in addressing the problem.

Accountability

Data governance leaders should commit to accountability once the initial data governance objectives have been set. They can accomplish this by announcing the mission, publishing the project plan, and regularly publishing the project status. Once data governance establishes itself as an accountable entity, it will have permission to hold others accountable, enabling it to publish regular data quality metrics and hold data creators accountable.

Translating data governance from an esoteric concept with an unclear value proposition into a meaningful effort to improve the enterprise with high levels of accountability and important stakeholders will improve the likelihood of success. It may even make data governance exciting!

If looking for more data governance, check out the following Novarica reports:

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