Using data as a strategic asset is perhaps one of the most pronounced and highest priorities for most federal agencies, acting as a great stimulus for both intramural and extramural collaboration across the federal landscape.
Don’t let urgency undermine success
Data is the lifeblood of decision-making at all levels. High-quality data that is easily accessible in real time is paramount to mission success. At many agencies, legacy systems and processes limit the usefulness and value of their data. As a result, leaders must make decisions based on insufficient or poor-quality data—decisions that affect lives, quality of life, national security, healthcare, economies, global relations and more. They are all the while aware that the data they need exists somewhere—just not at their fingertips.
Data continues to stream in from traditional sources, and also from an ever-growing array of newer sources such as sensors, artificial intelligence and internet user data. Inundated by the sheer volume, agencies must also cope with the variety of data – video, audio, call center voice data, social media data, and geo-location data.
Most agencies struggle with legacy data management systems, data silos and data of uncertain quality. The lack of systematic access to data collected and maintained in other federal agencies exacerbates these concerns.
However, data modernization requires a strategic, methodical journey that serves the unique mission and objectives of each agency. It feels urgent—and it is—but doing it smartly is essential.
Start with individualized strategy
It is tempting for agency leaders to launch multiple efforts in parallel—a data-cleansing project perhaps, and maybe a simultaneous retooling of their database technology. But just as parallel lines don’t meet, parallel efforts often don’t produce cohesive results.
It is well worth the time to have a concerted strategy exercise. Instead of embarking on parallel efforts, first decide on the optimal data modernization strategy tailored to fulfill your mission for the near future. Missions vary widely, and so will data strategies.
Not only is a clearly articulated strategy essential for guiding work and investments, it is also vital for gaining widespread alignment and ability to measure return on investment (ROI). Nothing undermines employee productivity like uncertainty about the point of their efforts.
Create a data strategy framework
A good data strategy must address the following 10 elements:
- Alignment with business strategy: The primary purpose of data is to enable effective and efficient decision making to achieve better business outcomes. You must understand the business and program mission of your organization to develop a data strategy that will support that mission.
- Roadmap: Develop a roadmap that defines major milestones for no more than 24 months, expecting to modify the plan as time passes and new factors emerge.
- Data policies: Assess the agency’s data policies, producing an agreement on how its organizations will use and share data—by whom, for what purposes and any related restrictions. Revise outdated existing policies.
- Data governance: The sensitivity of data along with the dispersed nature of data sources demands that agencies develop a governance structure. Most agencies need to work with both their own and others’ data. They will neither own all the data they use, nor will they be able to assemble all the data in a common place. The need to work cooperatively with other data owners demands a versatile governance structure.
- Systems assessment: Assess all the agency’s data-related systems. Which can be retooled and which must be replaced? What are their dependencies on other systems? Although most legacy systems do not lend themselves well to data sharing, it is not practical to modernize or replace all systems concurrently. Your data strategy needs to coordinate with infrastructure and systems modernization plans.
- Data catalog: Most agencies do not have a good handle on all the existing data. It certainly doesn’t help that the volume, velocity and variety of data are increasing by the day. Create a data catalog to determine what data exists—structured vs unstructured—and assign a completeness and maturity index to the data.
- Data security: Data proliferation and related digital transformations have unfolded faster than cybersecurity risk management can keep up. Make data security a foundational and non-negotiable element of the data strategy. Data strategy also means looking at the datasets that train AI/ML algorithms to ensure they are the appropriate choices.
- Data architecture: A target data architecture will create a good foundation for addressing full lifecycle of data from creation to storage, sharing, usage, and finally, to archival or destruction. If you short-change this step, it will be like starting to build rooms without laying out a solid foundation for a new house. However, keep in mind the need to scale, incorporate future technologies and simplify.
- Data tools: Data strategy should include an assessment and analysis of alternative tools and technology the agency needs for end-to-end data management. Key considerations for choice of tools include scalability, security, interoperability and most importantly, ease of use. Remember too that tools and technology are a means to an end—choose technology that supports the mission.
- Workforce: You will need to invest in retooling and training human capital with the skills needed to design and operate in the new environment. The retraining is not limited to hard skills; it is also to create a culture change among everyone in the ecosystem that touches data.
Following these guidelines, state and federal agencies should be able to lay out a strong foundation for data modernization initiatives. It is a multi-year journey for all organizations, and the most successful ones are where they can measure successes, tie those back to mission outcomes, incorporate lessons learned and adapt along the way. The theme of “data as a strategic asset” should always be your key driver.