Data Management in the Age of AI: 6 Things CDOs Need to Do

Artificial intelligence (AI) is taking the world by storm, transforming the ways in which companies operate, compete, and innovate. However, to capitalize on the many benefits AI has to offer,  chief data officers (CDOs) must rethink how they manage their enterprise data.  

To succeed in an AI-driven world, organizations need data that is high-quality, enriched, and trustworthy. But too often, data management strategies remain rooted in legacy practices. Outdated, rules-based master data management (MDM) solutions can’t scale as data grows in size and complexity. Data silos trap valuable insights, making it difficult for decision-makers to gain a 360-degree view of key business entities. And CDOs are often expected to focus primarily on data hygiene, rather than applying the business insight needed to drive strategic change. 

To successfully navigate the new AI-driven era, CDOs need to rewrite their data management playbook to ensure their MDM strategies remain relevant in the age of AI. Let’s take a closer look.

AI in Data Management: 6 Shifts CDOs Need to Make

As AI reshapes the business landscape, CDOs must make six fundamental shifts that enable them to succeed with AI in master data management. 

1. From data steward to business strategist

CDOs, once viewed as technical experts, must now establish themselves as strategic business partners responsible for developing a data management strategy that fuels organizational growth, improves operational efficiency, and drives competitive advantage. To be successful in the AI era, not only must CDOs prove they are technically savvy, but they must also demonstrate they are expert collaborators, communicators, educators, and strategic thinkers. 

‍2. From rules-based to AI-native

Many organizations still rely on traditional, rules-based MDM solutions to deliver the golden records their organization needs to make confident decisions. This is a mistake. Instead, organizations need to adopt an AI-native MDM solution that enables real-time entity resolution. With AI-native MDM, CDOs can deliver accurate, comprehensive, and durable golden records faster and at a lower cost. But buyer beware! Don’t be fooled by vendors that bolt AI on to their legacy solutions. These solutions simply can’t deliver the same level of value as AI-native ones. 

‍3. From static to real-time

AI demands real-time data. But legacy systems weren’t built to manage data in real time. That’s why CDOs need to make the case for solutions that provide the real-time capabilities needed to resolve entities while the data is still in motion. Doing so not only prevents the creation of duplicate records but also ensures everyone has the best version of the organization’s data available for decision-making and day-to-day operations. 

‍4. From data silos to data unification 

Data silos are a pervasive problem for businesses today. But AI thrives on trustworthy data that’s connected across systems and sources. That’s why it’s imperative that CDOs implement systems and processes to break down silos—ensuring seamless integration, consistency, and accessibility across the enterprise.

‍5. From governance-first to governance-enabled

In the past, data governance was a bottleneck: adding incremental steps, processes, and controls in an effort to deliver better data. And while data governance is now part of mainstream data management for most organizations, it can still cause disruption in the process. Shifting to a model where governance is embedded within workflows and automated whenever possible increases the reliability of the AI—without sacrificing data quality, privacy, or compliance. 

‍6. From reliance on internal data to enrichment via third-party sources

AI needs high-quality, trustworthy data. But internal data is often incomplete, inconsistent, or outdated. In fact, for most organizations, the best version of their data often resides outside of their company’s firewalls. That’s why CDOs must enrich their data by linking their valuable, internal information with trustworthy external data provided by third parties, vendors, or public data sources.

Successfully managing data

Successfully managing data in the age of AI requires more than just new tools. It also calls for a fundamental rethinking of data management. From embracing real-time entity resolution and breaking down data silos, to enriching internal data with third-party sources and adopting AI-native solutions, CDOs must leave legacy practices behind and align their MDM strategy with AI goals. This shift will enable CDOs to uncover new opportunities to grow revenue, drive smarter decisions, and position their organizations for long-term success.