Federal agencies are hard at work developing strategies to overcome data challenges and ensure they’re meeting critical mission needs, Federal data experts from the U.S. Postal Service (USPS) and the Centers for Disease Control and Prevention (CDC) said during the Databricks Data and AI Summit 2022 in late June.
The rapid pace of technology evolution has created enormous volumes of data, which if used effectively can provide important insights to improve the Federal government’s overall performance. However, Federal agencies continue to struggle with deriving value from all that data.
“These challenges hinder their ability to extract any actionable insight,” said Howard Levenson, general manager for Databricks Federal.
From an industry perspective, Levenson explained that some of the most pressing issues agencies face include:
- Fragmented views of mission-critical data due to siloed information;
- Legacy systems that can’t scale for expanding data;
- Siloed data preventing opportunities for real-time data use; and
- A lack of tools available to transition from descriptive to predictive data analytics.
Federal data experts on stage with Levenson at the Databricks Data and AI Summit echoed his sentiments on those challenges, but also explained how they are moving to overcome them.
CDC: Data Key to Being Response Ready
Public health data systems are critical sources of actionable intelligence used by Federal, state, tribal, local, and territorial public health agencies to protect Americans against health-related threats. However, many of the nation’s public health data systems are antiquated, siloed, chronically underfunded, and rely on older surveillance methods – leading to delayed detection and response.
“The COVID-19 pandemic proved that we needed to move from those siloed data systems to a more connected and adaptable system,” said Alan Sim, chief data officer for the Centers for Disease Control and Prevention (CDC).
Before the pandemic hit, Sim said, the CDC was in the process of implementing and formulating its Data Modernization Initiative (DMI), which was designed to offer solutions to a host of data-centric problems.
Currently, the CDC has five objectives it is focusing on as it implements the DMI framework. Those include:
- Building the right foundation to strengthen and unify critical infrastructure for a response-ready public health ecosystem;
- Accelerating data into action to improve decision-making and protect health;
- Developing a state-of-the-art workforce;
- Supporting and extending partnerships with state, territorial, local, and tribal government officials; and
- Managing change and governance to support new ways of thinking and working.
USPS: Data in the Cloud
The key to meeting the demands of an increasingly digital society is up-to-date, reliable, user-friendly, and open data, said Fredy Diaz, analytics director for the U.S. Postal Service (USPS) Office of Inspector General (OIG).
Meeting those demands, however, becomes difficult due to the complexities of legacy systems within Federal agencies, including steep maintenance costs to scale expanding data needs, and persistent data silos that prevent integration between systems, he said.
“At the [USPS OIG] we essentially use data analytics – which includes data mining, risk assessments, and predictive analytics – to help us better detect fraud and misconduct. Data analytics also help us identify the root causes of problems or inefficiencies, and then develop solutions,” Diaz said.
Turning to cloud platforms, Diaz explained, has provided the USPS OIG with the benefit of scalability to adapt to the growing volume of data coming into agency systems.
“If we need an extra terabyte, it’s not going to require approval from Congress. That was a limitation we experienced before,” Diaz said. “Now, the limitation is our ability to execute on those use cases that come up. USPS has plenty of unstructured data that we can tap into now. It’s more about our imagination, and our ability to execute on that.”