Data management and governance key to successful AI use

AI's effectiveness is limited by data quality. Building strong data management and governance programs are crucial to handling the challenges that AI presents to organizations.

Data is the fuel for an organization's productive and creative engines, and AI is the ultimate driver. Data management and data governance have never been more crucial practices than they are today for businesses and society as we enter a new era of how we use data to drive business success and as consumers.

Data volumes are increasing due to AI usage, with more employees using data to make smarter work decisions to drive the business forward and reduce time and costs. Conversational AI enhances customer experience as generative AI is quickly becoming a part of business applications and processes.

But, to achieve success in AI, solid data management and governance should be a priority. The decisions even the most sophisticated AI algorithms make are only as good as the data available for training, learning and decision-making. As AI-enabled applications become more prevalent, data quality, relevance and accessibility become more important for compliance, competitive advantage, assurance, security and detailed insight into AI outcomes.

Data management focuses on organizing, storing, processing, monitoring and securing incoming and outgoing data throughout the data lifecycle, while the world of AI involves the shaping and interpretation of data in increasingly useful ways for organizations.

Data governance manages policies, processes and controls that outline the collection, use and sharing of data. In an era of AI, data governance is a challenging and urgent pursuit to steer organizations through the maze of regulations while balancing the availability of data. It protects privacy and mitigates risks of data misuse or unauthorized access. Data governance also dictates rules around data ownership, access and use, ensuring openness, transparency and accountability in data-driven decision-making.

Despite the potential of data-driven AI, in practice, effective data management and governance often pose real organizational challenges, such as navigating complex regulatory frameworks, ensuring cross-silo interoperability and conquering cultural resistance to sharing and collaboration. With the explosion in both the amount and variety of data, as well as the growing number of applications that incorporate AI, the need for scalability keeps growing.

Given the steep rise in the bar set by AI, being able to use all the new technologies involves innovation in data management and governance. Organizations should get support from next-generation technology and AI-driven practices such as machine learning, natural language processing, generative AI, predictive AI and vector search capabilities to help automate data management tasks, migrate legacy data, ensure compliance and enhance data quality. They should cultivate a culture of data literacy and collaboration across business units to empower employees with the right skills to ensure everyone can use data to deliver on the promise of continuous innovation.

Strong data management and comprehensive governance are two of the most important building blocks for organizational success in the AI era. When organizations make substantive investments in both, they reap the benefits of AI, including improved data quality, data privacy and data trust. Across every industry and every continent, organizations must continue to be nimble and agile in adapting their data strategies to the technology of tomorrow.

Stephen Catanzano is a senior analyst at TechTarget's Enterprise Strategy Group, where he covers data management and analytics.

Enterprise Strategy Group is a division of TechTarget. Its analysts have business relationships with technology vendors.

Dig Deeper on Data governance

Business Analytics
Content Management