Aligning data and AI initiatives to mission objectives
In financial services, data is arguably as important a resource as the monetary assets these organizations oversee. Everyone from banks and investment firms to mortgage companies and payment processing organizations rely on data for sustainable growt
Understanding your “why” first
Defining value
Sample Public Sector outcomes and the initiatives that support them
The importance of data in the public sector, including federal and state and local governments, cannot be overstated. Data informs the decision makers of important agencies and departments, aids warfighters across the globe, helps deliver critical and important services that have lasting impacts on citizens. And, in the continued quest for digital transformation, data plays a pivotal role in enhancing citizen services at both federal and state levels. To ensure these missions are successful, the efficient management of supply chains, business operations, human resources and all other interconnected aspects of running government operations relies on the intelligent use of data.
However, with the strategic importance of data comes heavy regulations and the absolute need for trust from communities being served. Laws like the Federal Data Strategy provide a blueprint for the government to leverage data as a strategic
asset while helping to ensure that the use of data is responsible, ethical and in alignment with the overarching mission of public service.
To compound these challenges, there has been a surge of interest in artificial intelligence (AI), specifically generative AI (GenAI), in the public sector. AI can be utilized in a variety of use cases, such as allocating healthcare resources more efficiently, enhancing productivity through content generation and automation of repetitive tasks, and improving citizen support through chatbots and AI agents. Even “old” analytical AI models are now seeing new life with expanded use cases and an even greater need for faster and better outputs. And with this focus on new use cases, there is a widening gap between what departments and agencies want to do with data and AI and what they can do. This can lead to risky initiatives without a unified governance foundation.
While these problems seem disconnected, they boil down to one thing: governance fragmentation. Control, visibility and even meaning are tied to specific systems, sources and even compute platforms. And fragmentation extends to your people, as technical solutions aren’t accessible to the stakeholders who need to create policies, steward and use the data.
The organizations that overcome these challenges to build a solid foundation for data governance will accelerate and strengthen every data and AI use case — without the risk.
