6 Game-changing GenAI Use Cases for Enhanced Data Governance

Generative AI (GenAI), the new mantra of the digital economy, is quickly making its way onto executives' strategic agendas. Companies across industries are developing their applications to strengthen customer relationships, accelerate the personalization of their products and services, further automate their processes, and improve risk and quality management.

These opportunities for growth and performance depend more than ever on the quality of their data. But GenAI, which is even more data-intensive than other AI models and, above all, a major consumer of so-called "unstructured" data — text, images, PDF documents, audio, or video recordings – is putting pressure on data governance.

Led by the Chief Data Officer, this set of policies, tools, and processes ensures that the company's databases are high quality, secure, reliable, easily accessible, and compliant with increasingly stringent regulations. With GenAI, this still fragile but now critical capability in an economy dominated by the exploitation of these eminently strategic assets will have to manage an influx of uncontrolled data that is difficult to track, classify, and categorize.

This is an immense challenge that is virtually impossible to meet for activities that are typically performed manually and that already mobilize many business and IT professionals. Managers face a dilemma. They can't afford to be left behind by this new wave of technology and miss out on its value-creating potential. Nor can they risk compromising their databases or exposing themselves to regulatory risks.

But every cloud has a silver lining.

GenAI provides the solution to these data governance problems. Better yet, its ability to process unstructured information and generate content of all types means that most key data management tasks, such as tagging, can be automated, freeing IT and business teams to focus on more value-added tasks. Applying GenAI to data governance processes brings the efficiency that has been lacking. For many organizations, this increasingly complex and regulated function remains an Achilles' heel.

And yet, over the past decade, companies have been investing heavily in this area. According to BCG’s 2024 Data and AI Capability Maturity Assessment (DAICAMA) survey, 20% of companies have now achieved a high level of maturity, up from just 1% in 2015. Despite these efforts, data governance is rarely at the level required to create a competitive advantage. By leveraging GenAI, CDOs finally have the means to industrialize their processes while ensuring greater reliability and security.

We have identified six use cases in the area of data governance:

1. Creating metadata labels: These labels specify details such as the source of the data, applicable usage rights, and how the content relates to other data. Metadata helps ensure that companies train algorithms on the right data in the right context in responsible ways, complying with any applicable regulation, constraint, or policy.

2. Annotating lineage information: GenAI can accelerate the process through code-parsing techniques and by generating initial drafts of lineage data. Instead of manually creating the lineage information, data governance teams validate the GenAI output, making for a more efficient use of their time.

3. Augmenting data quality: GenAI models can accelerate and even automate many key tasks in data remediation — removing duplicate records; standardizing data formats, types, and values; and filling in gaps in values.

4. Enhancing data cleansing: With training and prompt engineering, GenAI can create the code to fix data anomalies, freeing up teams that would otherwise take on this work.

5. Managing policy compliance: Companies can foster awareness and observance of their data policies through GenAI-powered knowledge bases, compliance checks, and action recommendations. The technology can also power chatbots, providing an interactive, conversational way for employees to explore policies – and an alternative to ad-hoc support and training.

6. Anonymizing data: GenAI can transform data that contains sensitive or personally identifiable information. This lets companies ensure confidentiality and privacy – bolstering their risk and compliance posture – while preserving the utility and integrity of the data.

Implementing these use cases has helped companies achieve impressive results, with accuracy rates of up to 80-90%. Top players in data and AI have oriented their data governance organizations to deliver business-ready data, rather than just complying with regulation.

Generative AI opens up a whole new world of possibilities in the realm of data governance, promising productivity gains and sources of value across many sectors.