How to execute a data strategy to drive better performance
- By Vasil Jaiani
- Feb 25, 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:
- Identify data needs to answer priority agency questions.
- Constitute a diverse data governance body.
- Assess data and related infrastructure maturity.
- Identify opportunities to increase staff data skills.
- Identify priority data assets for agency open data plans.
- 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 Gartner, Stanford University, IBM, Oracle 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.
Create a strategic plan specific to the mission
It is critical for agencies to define their vision of becoming a data-driven organization and prioritize actions. Leveraging data as strategic asset may be the ultimate desired state. However, it is worthwhile to break this vision statement down into more specific goals based on an organization’s mission, maturity and business needs. Being a data-driven organization that leverages data as a strategic asset revolves around four dimensions: data availability, quality, use and security. A truly data-driven organization will have an environment where high-quality data is secure, easily available and actively used to solve most critical organizational challenges.
These dimensions could inform an agency’s thinking when it crafts its strategic plan. For example, at an organization with proper data security controls where people are willing and capable to use data but struggle with access, the priority could be focusing on availability. Creating a data warehouse and/or deploying collaborative business intelligence tools could be actions of first choice. In another agency, data may be available, but there may be a shortage of data-savvy users. In this case, the priority would be to promote data usage and train people to become data citizens. If used in strategic planning, these dimensions can help agencies direct their efforts to the areas of highest need leading to better returns on their efforts.
Identify the questions that matter most
Aligns with FDS Action 1
The only value of executing data strategy and becoming data driven is to be able to answer key business questions that help advance agencies' missions. Identifying these questions will not only satisfy one of the actions mandated by the FDS, but it will focus the agency on what’s important. These questions could be collected through from the senior executive team, organizationwide strategic and tactical plans, agencywide key performance indicators (KPIs), budget, internal surveys, focus groups, etc. The questions can then be grouped by the importance to various stakeholders such as executive leaders, middle management, frontline staff and external stakeholders. Once questions are identified and grouped, the data strategy execution activities should be prioritized around them. The next action helps organizations to identify availability or absence of data required to answer these key organizational questions.
Inventory critical datasets and data source systems
Aligns with OPEN Data Act and FDS Action 5 and Action 6
Along with identifying key questions, agencies must inventory all data systems and critical datasets. This will help determine what data is available and where gaps are. Complex, large organizations will likely have multiple enterprise-level systems, with datasets of varying quality, availability and importance. When creating an inventory of datasets, agencies should include the following information: dataset description, primary system of record, other systems where the data may be accessed, data owner, system gatekeepers, security consideration and any other information relevant to the organization.
Once the inventory is created, it is helpful to rate each dataset based on its quality, ease of access (availability) and importance. The Internal Prioritization Scoring Rubric from Chattanooga Data Inventory Guide could come handy for the exercise. Easily available quality datasets with no security risk identified during this exercise could become “low hanging fruit” for open data sharing. Agencies will also need to determine how well the compiled dataset inventory supports answering key organizational questions. Uncovered gaps could then be addressed over the course of data strategy implantation.
Finally, creating an inventory is just an initial step in mastering data. Agencies must sustain gains by building a system for keeping the datasets up to date. Data governance is a system to accomplish just that.
Pick a governance model and launch it
Aligns with FDS Action 2
Defined by DAMA as “[t]he exercise of authority, control and shared decision-making (planning, monitoring and enforcement) over the management of data assets,” data governance is a fundamental component in mastering enterprise data. Not only it is a critical necessity to ensure higher data quality, quicker data insights, increased accountability, improved data security and access, but it is also a vehicle to advance ownership of data throughout the organization. Governance calls for enterprisewide participation and collaboration and creates a formal conversation around data.
It is no surprise that the FDS implementation playbook emphasizes data governance as one of two critical action areas for getting started in data leadership. (The other is data maturity assessment.) First, organizations must choose the operating model, which can be centralized, federated or hybrid. Along with the model, an enterprisewide governance body consisting of executive level leadership should be formed. Senior representation will ensure that data governance aligns with the organization’s mission and has enough support to be taken seriously by the rank and file. The body will need to formulate a high-level policy creating the governance framework for the organization.
The processes and procedures ruling how data is collected, stored, accessed, used, shared and changed within the framework can then be created either by the body or by data governance teams consisting of data owners, data stewards and others oriented on day to day implementation.
Becoming data driven is not a compliance checkbox activity. It is a necessity to ensure effective mission delivery and responsible stewardship of public funds. Executing data strategy is key for every public-sector organizations’ success. The above steps provide an execution framework and hopefully serve as a helpful guide to unlock data as a strategic asset.