How to execute a data strategy to drive better performance

February 28, 2020

The past year has been eventful for data management in the federal government space. On Jan. 14, 2019, the OPEN Government Data Act (Title II of the Foundations for Evidence-Based Policymaking Act ) became law. Two key requirements are that the “all non-sensitive government data be made available in machine-readable formats by default” and that “head of each agency shall designate a nonpolitical appointee employee in the agency as the Chief Data Officer.” Then on June 4, an Office of Management and Budget memorandum established a Federal Data Strategy framework to “enable Government to fully leverage data as a strategic asset.”

Of the 20 required actions in the FDS action plan, six must be implemented by individual agencies:

  1. Identify data needs to answer priority agency questions.
  2. Constitute a diverse data governance body.
  3. Assess data and related infrastructure maturity.
  4. Identify opportunities to increase staff data skills.
  5. Identify priority data assets for agency open data plans.
  6. Publish and update data inventories.

Although these legislative and policy changes are focused on federal agencies, they will have a lasting effect on state and local governments as well. These changes recognize that data is one of the most valuable assets and must be effectively leveraged for greater public good. This strategy calls for adoption of enterprise data management — “an organization’s ability to effectively create, integrate, disseminate and manage data for all enterprise applications, processes and entities requiring timely and accurate data delivery.”

Now that the stage is set for taking charge of public-sector data, federal, state and local government leaders must start acting. It is easy to get tangled in the technical complexities and paralyzed by the prospect of boiling oceans of data. Knowing where to start and what to execute in the short term is critical for the overall success.

The following are foundational data strategies that public-sector agencies should take now term to enable them to become data-driven organizations that successfully leverage data as a strategic asset for public good.

Find a right data leader 
Aligns with the OPEN Data Act

As leadership coach John Maxwell said, “Everything rises and falls on leadership” — and that includes successful execution of an enterprise data management strategy.  It’s complexity requires a relentless champion with strategic acumen coupled with technical prowess and coalition building skills. Charging implementation to a committee or another executive such as the chief operating officer, the CFO or even the CIO is not a recipe for success. Agencies need a leader with undivided attention who believes in the power of leveraging data and can continuously inspire an organization to move forward on this journey. That champion should be included in the senior executive team, as this role is as important as the one of CIO or CTO. If the data leader is expected to direct organizational change, it is only reasonable to have such a leader report directly to the top executive.

Honestly assess the current state
Aligns with FDS Action 3 and Action 4

Enterprise data strategy execution is a journey. To move forward, an organization must understand where it currently stands and where its gaps and strengths are. Data maturity assessment accomplishes just that. There are a number of assessment models to choose from, so organizations can pick a relevant model based on their unique needs.

Regardless of the approach, data maturity assessment provides vital reconnaissance that must be conducted at the start of the journey to become a data driven organization. One option is to use the Data Maturity Model developed by the Federal Data Cabinet. It offers a comprehensive look at the state of data and helps set a baseline. The strength of this model is that it focuses not only on data governance but also on the full range of enterprise data management components including analytics capability, data culture, data management, data personnel, data systems and technology as well as data governance. Other enterprise data management assessment models to consider have been developed by GartnerStanford UniversityIBMOracle and DataFlux. This is neither an exhaustive nor suggested list of models, but rather a sample of options. 

Once data maturity model is selected, agencies should consider using the assessment effort to also gauge staff data-literacy levels and identify opportunities to increase data skills. Data maturity assessment will help organizations define current and future states. Analyzing the gap between two states will highlight the missing data skills the enterprise must develop to support the future state. For example, if an organization strives to develop a culture of self-serving analytics but the workforce lacks knowledge of business intelligence tools (such as Tableau, Power BI, etc.), this gap should be identified and the needed skills identified.

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