Transitioning Data Value to Non-tech Folks Begins with a Focus on Outcomes — MD THINK Executive Director

(US & Canada) Patrick McLoughlin, Executive Director at MD THINK, speaks with Jeff Strane, Senior Director, Government and Education Affairs, SHI, in a video interview about the intention behind the creation of a state CDO position, materializing data-driven decision-making at scale, the pathway to cross-agency collaborations, transitioning the value of data to the non-tech side, and the challenges around terminology and data ownership.

MD THINK or “Maryland Total Human-services Integrated NetworK” is a transformative and ambitious cloud-based platform that is revolutionizing health and human services delivery for the citizens of Maryland.

At the outset, McLoughlin discusses the intention behind the creation of the state CDO position. The role was formed to envision and direct how data management and governance practices would run across the executive branch for the state of Maryland.

The focus primarily was on setting foundational practices, getting executive buy-in across various agencies within the branch, and building a data-driven culture, says McLoughlin. Based in the governor’s office, he mentions creating an agency data officer position for each agency, resulting in a one-on-one information pipeline between the statewide data office and agency data offices.

When asked how data-driven decision-making materialized at scale, McLoughlin first took into consideration the varied levels of data maturity in each agency. He states that it was a mix between understanding their core business objectives and identifying opportunities where they are leveraging data regularly.

McLoughlin adds that the agencies may or may not be aware of the degree to which data is utilized and that it is really driving the outcomes. He mentions that bringing the role of data to the fore so that its contribution is clear is critical.

Additionally, McLoughlin advocates meeting agencies where they are without overcomplicating the value of data for a particular agency. Therefore, it boils down to balancing overall goals and the outcomes to be driven and understanding the current state of data maturity. Then, one can find ways to incorporate data-driven and informed decision-making using existing data or by leveraging data from other agencies to deliver outcomes.

Shedding light on the pathway to collaborations, McLoughlin shares that there are two approaches from a governance perspective. The first approach involves creating a state organization of all the agency data officers, structured around monthly meet-ups to discuss principles and initiatives.

This was in parallel with the first state data plan designed to align key outcomes. Looking from the statewide perspective, McLoughlin recognized that the path to achieving these goals would vary slightly based on age and agency.

To make the initiatives accessible, they were documented in a clear, easy-to-understand, and repeatable format, and were discussed consistently, bringing various groups together. The process also brought forth stories of how different agencies were tackling the initiatives and where they stood.

Alongside this, regular one-on-one meetings were established, say McLoughlin, wherein he met with the agency and teams on a monthly basis to review overall status, priorities, and challenges.

Next, McLoughlin says that transitioning the value of data to the non-technical folks begins with focusing on outcomes, understanding the goals, and explaining in a straightforward way how data supports those efforts.

For example, an agency aims to achieve a specific outcome, which may be around reducing or increasing a measure. Then, the story can be told in a way that resonates with executive leadership or other agencies. McLoughlin refers to it as a B2B explanation where relevant data points are weaved in to show the role data plays.

The idea is to explain how the outcome was reached by analyzing different factors from data sets without overwhelming the conversation with complex data concepts or jargon. Instead, the data is presented as supporting evidence that helps explain how the outcome was achieved.

Highlighting the challenges, McLoughlin states that there were many crucial discussions about terminology. He mentions that they identified early on the need to standardize terms across the state.

Adding on, McLoughlin recalls a conversation with an agency excited about their new “data lake.” When asked for details, they affirmed renaming their SQL server as a data lake. While the initiative was good, the concepts were entirely different, he notes.

Therefore, it was collectively decided from the beginning that standardizing terminologies was critical. This way, everyone could use the same terms throughout the process with a reference point to clarify any questions.

Furthermore, data ownership also became a significant challenge, says McLoughlin. While all data belongs to the state, individual agencies are responsible for generating, managing, and curating specific types of data.

Determining which agency would be the authoritative source for each data was challenging because, in many government entities, multiple agencies handle similar data. He asserts that working through this is an ongoing process, and it continues to evolve.

In conclusion, McLoughlin states that it also goes back to how certain legislation designates a specific agency as responsible for a particular data type. Also, there are other federal requirements that lead to a particular agency assuming that responsibility.

CDO Magazine appreciates Patrick McLoughlin for sharing his insights with our global community.