By Ramakrishnan Krishnamurthy Federal agencies are flooded with huge amounts of data — procurement records at the General Services Administration, data on health care claims at the Centers for Medicare and Medicaid Services and infrastructure sensor data at the Department of Transportation, to name just a few examples.
Much of this data is contained within isolated systems and departmental silos, which hinder collaboration and slow down decision-making processes. The Department of Government Efficiency is seeking to address these issues by promoting efficient government operations through improved access, comprehension and utilization of data across agencies.
By adopting modern data architectures like enterprise data lakes, agencies can dismantle these silos, transforming data from a burden into a powerful tool for improving citizen services and maximizing taxpayer value.
Data trapped in silos has real consequences
Pervasive data silos lead to tangible consequences for federal agencies and the public they serve. Imagine trying to get a complete picture of a citizen’s interaction with various government services when their information is scattered across dozens of different databases, managed by separate agencies or even sub-departments. Or, think about coordinating disaster response when critical infrastructure data, population demographics and emergency service resources reside in incompatible systems.
This fragmentation results in duplication of effort, with different teams collecting or processing similar data independently, wasting resources. It also gives rise to inconsistent information and discrepancies that arise when the same data exists in multiple places with varying updates or definitions. In turn, this leads to slow decisionmaking because analysts and policymakers waste valuable time searching for, accessing and manually integrating data. And the inability to connect disparate datasets prevents holistic analysis that could reveal insights for cost savings, improved program effectiveness or better citizen services.
These silos are a significant impediment to DOGE’s mission of streamlining operations and maximizing the impact of taxpayer dollars.
The solution: enterprise data lakes as a central hub
An enterprise data lake offers a transformative solution by providing a centralized, scalable repository where data from any source — structured or unstructured — can be stored and accessed. Unlike traditional data warehouses with rigid schemas, a data lake accommodates diverse data types, from CMS claims and the Federal Emergency Management Agency’s data exchange to internet of things sensor data on infrastructure and social media feeds for public sentiment.
This approach gives federal agencies unified data access with consolidated data from legacy systems, cloud apps and external sources in one accessible hub. For example, the Department of Health and Human Services could integrate hospital utilization data with disease outbreak statistics from the Centers for Disease Control and Prevention to predict resource needs.
It also allows analysts with proper permissions to combine data across agencies, like, for example, merging Census and IRS data to identify underserved communities for targeted programs. This approach also allows data scientists to use raw data for AI models (for such actions as predicting fraud at CMS), while policymakers can access curated datasets for reporting – all from the same platform.
FEMA offers a compelling example. Their FEMA Data Exchange — or FEMADex — pilot implementation showcases significant efficiency and processing improvements. For instance, in COVID-19 predictive analytics use case, it slashed time-to-insights from 48 hours to just one hour. Furthermore, its application in grant tracking for the Enterprise Grants Data division led to a 14% reduction in the “median time to completion” for grant closeout tasks and an 11.4% improvement in project funding adjudication, ultimately delivering funds to grantees more rapidly.
For DOGE, a federal data lake can similarly drive efficiency by enabling agencies to identify redundancies, optimize budgets and enhance service delivery through data-driven insights.
From data lakes to data swamps?
Implementing a data lake in the federal space isn’t without hurdles. The scale of government data, stringent security requirements and bureaucratic resistance pose significant challenges. Without careful planning, a data lake risks becoming a “data swamp” – a chaotic, untrustworthy repository that exacerbates inefficiencies.
To avoid this, government agencies should establish cross-agency data governance to ensure quality and consistency; implement role-based access controls and encryption to protect sensitive data, like air-gapped environments for classified information; build a searchable catalog to make data discoverable, ensuring analysts can quickly find relevant datasets; and foster a culture of collaboration by incentivizing data sharing, such as a pilot program to share dashboards to demonstrate value to hesitant agencies.
Breaking down data silos is the first step toward a truly data-driven government, one that maximizes efficiency, improves citizen services and fulfills DOGE’s mission. Enterprise data lakes provide the foundation, consolidating diverse data and enabling cross-agency insights, as demonstrated by FEMA’s success with FEMADex in drastically reducing predictive analytics processing time from 48 hours to just one hour.
By adopting these modern data strategies – supported by robust governance frameworks and a culture of collaboration – federal agencies can shift from being overwhelmed by data to harnessing its full potential, driving measurable improvements in operations and citizen outcomes for a more effective government.
Ramakrishnan “Ramki” Krishnamurthy, REI Systems’ data analytics lead, is an accomplished technology leader with 25+ years of experience defining and executing transformative data strategies for government organizations, including Fannie Mae, HRSA and FEMA.
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