Creating Data Governance Programs with Proven ROI: Precisely

Data governance is defined as people, processes and technologies that manage, protect, and leverage data assets effectively. Critical objectives include the ability to ensure data privacy and enterprise data management compliance with regulations, drive operational improvements, and accelerate decision-making with more accurate, consistent, and contextualized data integrity.

But according to David Woods, SVP of Strategic Services at Precisely, data governance is not simply building a framework for processes and data within functional areas or lines of the business. To truly deliver return on investment (ROI), Woods underlines that data governance initiatives should prioritize linking data directly to the business benefits and outcomes that the organization wants to achieve.

While insurers certainly need to govern those functional areas that create, update, consume or syndicate data in support of those goals, the rationale for this value-based approach is that there always needs to be a focus on the data that is most important for the insurer to reach its key performance indicators (KPIs) and objectives.

Understanding the impact or relationship that data within one functional area can have on other functional areas of the business is also essential. This includes a need to comprehend and have in place accurate governance and data quality metrics that are visible and accountable across business functions and teams.

Leading organizations “extend” their data governance framework from the system, table, and field level up to functional processes and performance measures, ultimately connecting them to business goals and objectives. Once that’s completed, from an operational perspective it’s easier to define data as “relevant” and “owned” by specific functional areas and define appropriate governance strategies and mobilize for execution. The aim is to create value across all areas.

Woods will continue this talk as part of a panel of industry experts to debate and discuss  “Validate the Accuracy, Quality and Timeliness of Data to Drive Business Value”, an Intelligent Insurer webinar to be held at 11am EST on March 31, 2022. The key points of discussion include how to identify and publish metrics that quantify the ROI of a data governance program to enable better operational performance, risk mitigation, and advanced analytics.

The panel will discuss how to build engagement by motivating and engaging strategic leaders, operational and tactical teams with visibility into how their individual KPIs and goals are being enabled by data governance. The need is to govern only the most critical data, which requires the strategic identification of data assets that impact KPIs, objectives and business goals for all teams to focus data governance on what truly matters. The issue of cultural adoption practices and support will also be debated.

Intelligent Insurer spoke to Woods ahead of the webinar.

What are the best ways to quantify the ROI of a data governance program?

Leading organizations categorize their metrics across three distinct levels and dimensionalize them to provide context relevant to the organization. The lowest metric level is focused on efficiency and effectiveness, which is a set of metrics that tell us what we are governing and where we have controls in place. These metrics may involve the number of systems, data domains, datasets, date elements, processes, cycle times, data standards, and/or business rules.

The second metric level builds upon the lowest level and focuses on performance and value metrics, which tells us how we’re doing. Are we improving and getting better? These metrics include things such as cycle times versus service-level agreements (SLAs), number of touches (productivity), data quality, and so on.

The final and most important metric level focuses on business impact metrics, which tell us if our data governance efforts are making a positive or negative impact on the business. These metrics include both quantitative and qualitative measures that align directly to specific business outcomes, such as project acceleration, business process execution, and organizational priorities.

Across all these levels, and most importantly at the top level, it’s vital to define and capture data quality scores that can be directly tied to our data governance metrics. As an example, if we identify a set of critical data elements in a claims-handling process that drive retention, we would want to define a set of data quality measures tied to our governance efforts that showcase its value.

Not all business rules can be directly tied to an ROI model individually. However, most often you can combine multiple business rules into a composite measure that demonstrates meaningful ROI for your governance efforts. Much like preparing meal based on a great recipe, each ingredient by itself may not deliver value, but together they can be shown to create a fabulous meal.

Why is it vital for insurance leaders to build engagement?

Personalizing data governance messaging to specific audiences and people is the only way to drive sustainable followership and advocacy. Executive communications and support always get things going, but over time you must deliver data governance messaging that provides more personalized value.

We have found over time that unless users can clearly answer the question: “What’s in it for me?” they may be supportive, but they may not be fully engaged in the program. We refer to it as ensuring we instill an “owner versus renter” mentality.

How can insurers identify the data assets that impact business goals for all teams to focus data governance on what truly matters?

The organizations that have done this well have all taken a structured and programmatic approach to iteratively defining critical data elements and connecting them to KPIs, objectives and business goals.

Organizations that have failed to do this successfully have typically attempted to select critical data elements by asking the organization what data matters. The problem with that approach is that it tends to be very anecdotal and is largely biased based on recent issues with data. The only way to effectively prioritize is to develop and follow a set of organizational criteria that determines whether a data element is critical, rather than try to select individual data elements.

Focusing on criteria that are aligned to business outcomes ensures that our critical data will always be prioritized and will evolve as the business changes and pivots. The data elements that are important to an organization today may not be so important six months from now.

An example of this in the insurance industry involves the data that drives digital transformation. In the recent past, data reconciliation was a primary data value driver, but today data elements that drive digital transformation are some of those driving the most differentiated value.

Focusing on defining business criteria to identify critical data elements, prioritize and define governance strategies is the only way to know you’re focusing only on the data that matters the most.

How can insurers leverage the culture of their organizations to build enterprise-wide engagement and collaboration?

Data governance organizations that do this well always look for proven communication methods that are currently widely adopted and effective within their organizations and use them to showcase their data governance activities. Every organization is unique and consumes information differently—some respond to top-down messaging, some to outside benchmarks, and some to internal success stories.

Understanding how to message based on these organizational norms is always important, but in all cases, we should look for specific use cases where we can highlight successes that can be measured and have clearly benefited from our data governance efforts.

What are the best ways to break down functional area silos to deliver value across them all as part of data governance?

This is another area where having a Data Governance Framework pays off. When designed properly it will clearly outline the cross-functional impacts of the data. In terms of starting to operationalize a data governance program, we usually try to prioritize initial datasets that are relied upon or shared across functional areas.

This approach allows us to engage and benefit multiple stakeholders simultaneously, and it also drives collaboration to deliver governance approaches that satisfy multiple parties, which we can showcase as we expand the program.

What are the key takeaways you’d like delegates to learn from the webinar?

There is a need to clearly define your data governance approach and prioritize your efforts based on your most important data. The vital data must be aligned to business outcomes and objectives to showcase ROI and to gain followership from the organization.

It’s essential to establish a governance framework that connects your data to process, performance measures and organizational priorities that delivers value now, but will also scale. Insurers should design governance operating models that connect your data governance and quality efforts to promote user adoption and support collaboration. Last, to assess performance and impact, it’s crucial to define a measurement model that drives adoption and quantifies the value of your program.

To hear more on this topic sign up for the upcoming webinar “ “Validate the Accuracy, Quality and Timeliness of Data to Drive Business Value”, being held at 11am EST on March 31, 2022.