The Power of Data in Generative AI

Eleodor Sotropa

Updated on:

Generative AI has emerged as a game-changer in the technological landscape, and at its heart lies the pivotal role of data. If your data isn’t primed for generative AI, then your business isn’t either.

Recent research suggests that generative AI could infuse between $2.6 trillion to $4.4 trillion in annual economic benefits across diverse use cases. The common thread binding these cases? Data. The quality and structure of your data determine the potential of generative AI in your business.

For Chief Data Officers (CDOs), this presents a daunting challenge, especially when a whopping 72% of top-tier organizations cite data management as a significant hurdle in scaling AI applications. The task ahead for CDOs is to navigate the changes that will unlock the maximum potential of generative AI for businesses.

While the terrain of generative AI is still evolving, our extensive work with clients and data leaders has illuminated seven pivotal actions for data leaders transitioning from experimentation to large-scale implementation:

  1. Value-Driven Approach: CDOs must pinpoint where the value lies and identify the requisite data to harness it.
  2. Enhance Data Architecture: Integrate capabilities like vector databases and data processing pipelines, especially for unstructured data.
  3. Prioritize Data Quality: Implement human and automated interventions throughout the data lifecycle to ensure the integrity of all data, including unstructured forms.
  4. Guard Sensitive Data: With the ever-evolving regulatory landscape, it’s crucial to secure proprietary data and safeguard personal information.
  5. Invest in Data Engineering Talent: The focus should shift towards data engineers and architects, as generative AI tools increasingly perform coding tasks.
  6. Leverage Generative AI for Data Management: Generative AI can streamline tasks across the data value chain, from data engineering to governance and analysis.
  7. Monitor and Adjust Promptly: Invest in performance metrics and closely oversee implementations to continually enhance data performance.

Zooming into the Value-Driven Approach

CDOs should adopt a mindset reminiscent of President John F. Kennedy’s famous words, rephrased for our context: “Ask not what your business can do for generative AI; ask what generative AI can do for your business.” This value-centric approach is crucial to counterbalance the rush to merely “do something” with generative AI.

Three primary archetypes emerge in the business’s approach to generative AI:

  • Taker: Businesses that utilize pre-existing services via basic interfaces. Here, the CDO’s role is to ensure quality data for generative AI models and validate the results.
  • Shaper: Businesses that fine-tune models using their data. CDOs must evaluate how data management needs to evolve to achieve desired outcomes.
  • Maker: Businesses crafting their foundational models. Here, CDOs must devise sophisticated data labeling strategies and make substantial investments.

The Importance of Data Quality

The age-old adage “garbage in/garbage out” has never been more relevant. With generative AI models relying on vast amounts of data, ensuring data quality is paramount. CDOs must extend their data observability programs for generative AI applications and develop interventions to address issues that arise.

Data is the lifeblood of generative AI. It’s not just about managing data but understanding how to strategically leverage it to lead the business forward. As generative AI continues to shape the future, businesses that prioritize and optimize their data will be best positioned to reap the rewards. At, we’re committed to helping businesses harness the full potential of generative AI, powered by robust and insightful data.