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How State and Local Agencies Can Transform Their Relationship With Data

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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.

“If governments have data that’s on premises, in multiple different clouds and in software-as-a-service offerings, they often don’t have a good overall picture of it,” Bauer said. “Sometimes they don’t have access to the data lineage, so they don’t know where it comes from. However, if you do have that information available, it can help with data quality, quality of the audits and trust of the data.”

In addition, governments must better align their data with the people, processes and technologies the data supports; appropriately store and index this information; leverage advanced technologies to facilitate secure interdepartmental and enterprisewide data sharing; and ensure the privacy, confidentiality and security of all the data they collect.


As governments work to become more data-driven and AI-enabled, they should consider executing these specific tactics:

Assess current data management practices. Conduct a data inventory to determine what data exists within your organization, where it is stored and who has access to it.

Define data strategy and goals. Identify key use cases for each agency and review their current data governance policies to pinpoint any gaps. Create a plan for leveraging data to solve key business and service delivery challenges.

Assess AI-readiness. Convene key stakeholders to discuss how they will reshape their data architecture to execute their data strategy and better prepare for AI and other emerging technologies. They can start by assessing how they currently classify data, along with evaluating its accuracy and quality. From there, governments can create an innovation-driven data architecture focused on:1

  • Unifying and securing data across systems
  • Implementing scalable, cloud-enabled systems of record for certain data assets and/or a serverless computing environment
  • Shifting from rigid data models to flexible data schema
  • Integrating technologies that enable real-time data processing, instead of batch processing

Prepare your people. Embrace a growth and innovation mindset to become AI-enabled and better prepared for the future. Leaders must develop a strong strategic vision for data and constantly communicate and reinforce this vision throughout their organizations. Cultivate a culture of data stewardship and data ownership where everyone within the organization understands the true value of data and feels empowered to use it in their day-to-day work.

82 percent of organizations’ use of data has either increased or remained constant since 2020

“It’s a culture change that needs to focus on transformation and how you interact with your constituency,” Gonzalez said. “It needs to focus on the business processes you have in place. How are they serving your constituency? What challenges are you facing and what can you do better? Once you understand those challenges and what you can do better, it’s then taking what you know of AI and the solutions out there and leveraging them to address those challenges.”

Collaborate with a strategic partner. Collaborating with a strategic technology partner is one of the most effective ways for government agencies to expand their IT capacity to establish an AI-enabled data management foundation.

The right partner will offer solutions with built-in AI and machine learning capabilities that allow agencies to make their data more actionable and democratize the use of data throughout their organization. Additionally, a technology provider should offer not just products, but also expertise to help agencies take advantage of data-driven automation, realize new efficiencies and implement a proactive approach to data management.

“A strategic technology partner can help governments think outside the box,” Gonzalez said. “A partner that deals with AI and the advances of AI — and not just AI for AI’s sake, but how it gets integrated into products every single day — is crucial.”


Artificial intelligence will play a vital role in helping governments innovate and meet future service goals. Nearly 82 percent of CDG survey respondents said their organization’s use of data has either increased or remained constant since 2020; only 1 percent reported a decrease. Emerging technologies such as AI can enable governments to make better decisions, with greater speed, built on a foundation of trustworthy and transparent data to improve the public services they deliver.

“Governments need to make decisions in a much more timely manner than they ever have in the past,” Gonzalez said. “AI will help them do just that.”

Endnote: 1.