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How State and Local Agencies Can Transform Their Relationship With Data

  • 4 mths ago

Data underpins so much of the work governments do. But too often, agencies have taken a reactive approach to how they manage and deploy data.

Data underpins so much of the work governments do. But too often, agencies have taken a reactive approach to how they manage and deploy data.

Governments must treat data as a core strategic asset to achieve mission-critical objectives like hybrid work and digital service delivery and to strengthen their operational and cyber resilience. As the public sector looks to integrate emerging technologies like cloud, artificial intelligence (AI) and machine learning, agencies will need to establish a solid foundation to become more digitally enabled and data-driven.

There is much work to be done. For example, the ability to share data across agency lines is a critical part of improving data management. Yet in a recent Center for Digital Government (CDG) survey of 103 state and local government leaders, 66 percent of respondents said their data-sharing practices are only somewhat mature or not mature at all.

“The COVID pandemic exposed the siloed nature of much of the data. There’s an inability to easily share with other databases and out into the private sector, along with legacy data structures that aren’t quick, easy, efficient and agile,” said Chris Gonzalez, director of business applications for state and local government at Microsoft. “The pandemic has really shown how difficult it is for governments to react quickly. Many found a way to do so through low-code/no-code applications, but they had to overcome a lot of internal [data] hurdles.”

Along with data accessibility and sharing challenges, different data formats and storage environments also make it more difficult for agencies to transform data into actionable intelligence. At the same time, governments must deal with an ever-evolving regulatory environment and an increasingly privacy-focused landscape that makes it safer for them to limit data sharing rather than expose themselves to added risks.

According to CDG survey respondents, the biggest challenges standing in the way of data analytics are security (41 percent), privacy concerns (38 percent), data quality (35 percent) and data silos (34 percent).


To prepare for future AI initiatives and other advanced analytics tools, governments should build a data management foundation around three key pillars.

Scalable cloud infrastructure
Governments must transition away from legacy, siloed data structures to a flexible computing environment that allows them to easily scale and deploy data for different use cases — whether it’s quickly standing up a new unemployment claims system, developing self-service tools for social service program applications and business licenses, or allowing employees to securely access critical systems remotely.

Enhanced data quality
Agencies also need to clean up their data and consider creating new data standards to reduce redundancy and duplication. A data set is only useful if it’s in a format that is usable. Governments must take steps to improve the consistency, accuracy and integrity of data.

Good data governance
Governments need a better understanding of where their data lives and who has access to it, said Keith Bauer, director of data, AI and application development for Microsoft’s U.S. state and local government business.

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