Data modernization, data governance, and AI strategies each have a critically important role to play in public sector digital transformation.
In-depth discussions with industry experts, including Hakkoda’s own Eric Barton, reveal key points of focus, challenges, and innovative strategies, aiding organizations as they implement their own data and AI strategies.
AI Adoption and the Data Maturity Curve
To understand the significance of this trifecta – data modernization, governance, and AI – let’s start with data. Approximately 42% of organizations are already utilizing AI tools. Moreover, a whopping 95% acknowledge AI as a critical path to their success. AI is not just about automation – it’s about magnifying human capabilities, making faster decisions, and fostering an environment that is receptive to AI technologies.
When adopting AI, organizations should follow a maturity curve beginning with establishing data infrastructure. Following this, progressing to crossing data boundaries, cataloging data, collaboration, data governance, and, finally, automation. This curve is integral for fostering trust and ensuring success in data governance.
Harnessing the Power of Data Governance and AI
An important concern during AI deployment at scale is data governance. Data governance assists in maintaining responsible innovation, and keeping trust and compliance intact. There’s been a notable paradigm shift from traditional data management to a more dynamic, AI-driven process. The change requires breaking down traditional silos and aligning data, technology, and people towards common business objectives.
Take for example the emergence of the AI context engine. This new approach takes both structured and unstructured data and organizes it into a graph structure with curated metadata. Not only does it improve the use of AI tools, but it also makes them more effective. The AI context engine can provide transparency and governance by visibly showing the thought process and data sources utilized to arrive at an answer.
An additional and essential aspect in the success of the data governance journey is minimizing disruption and maximizing efficiency. Many experts advocate for a data innovation journey framework which measures an organization’s maturity levels in data modernization and AI implementation. This approach could assist in developing a plan tailor-made to an organization’s unique needs, ensuring a seamless transition to a modern data environment.
It’s also important to ensure that the management and AI implementation components such as data catalog and lineage, security, privacy, and compliance ethics form part of your organization’s foundation. These components boost data capabilities, ensure regulatory compliance, and uphold ethical standards, leading to effective governance and AI use.
Hakkoda’s Holistic Approach to Public Sector Digital Transformation
When it comes to digital transformation in the public sector, the importance of a holistic approach that understands the close relationship between data governance, security, privacy, and legal compliance cannot be overemphasized. This approach combines robust technical solutions with a deep understanding of the human and organizational factors that shape everyday operations. To modernize how the public sector uses data and to layer practical AI use cases on top of that data architecture, it is necessary to address data management, governance, business outcomes, risk compliance, privacy, ethics, and data literacy.
Fortunately, governmental and educational institutions don’t have to go it alone in their struggle to keep pace with their public sector counterparts. Data consultancy services like Hakkoda exist for the express purpose of helping organizations in tightly regulated industries reach their data goals while generating strong returns on their investments in the cloud.
Ready to build the data governance and AI strategy you need to succeed in the cloud? Let’s talk today.
