Chief Data Officer Insights on Generative AI and Data Strategy

I’ve often said that data is the genesis for modern invention. It only takes?one groundbreaking invention—one iconic idea that solves a widespread pain point for customers—to create or transform an industry forever. Today,?the recent interest in generative artificial intelligence (generative AI) has put a renewed emphasis on the importance of data and a well-crafted data strategy.? Organizations are increasingly appointing chief data officers (CDOs) to make sense of their data and drive value from it.?Generative AI presents both a challenge and an opportunity to these data leaders. In my conversations with customers, I’ve observed that their data and AI strategies are often intertwined.?

To understand how CDOs are pursuing the goal of becoming more data-centric and?tackling the generative AI challenge, Amazon Web Services (AWS) partnered with MIT Chief Data Officer and Information Quality (CDOIQ) Symposium and researchers Tom Davenport and Randy Bean for our second global CDO report: CDO Agenda 2024: Navigating Data and Generative AI Frontiers . They’ve observed that generative AI has captured the attention of CDOs, signaling a strong belief in its transformational potential. A resounding 80% of CDOs foresee generative AI changing the dynamics of their organization's business environment in the future.

Here?are my?five?main takeaways from this research.

Data is your differentiator: Almost half (46%) of CDOs view data quality as one of their top challenges. Data quality has always been important for CDOs, and generative AI puts a renewed importance and urgency around it. In?the?case?of?generative AI, just like all other forms of AI, the adage “input defines output” is very much true. AWS offers tools and guidance to reduce the manual work needed to ensure data quality and proactively detect and remediate data quality issues. When you want to build generative AI applications that are unique to your business needs, your organization’s data is your differentiator. That’s why ensuring that you’re working with high quality, trusted data is crucial to your generative AI success.

Starting with the right use case: The second greatest challenge for data leaders is finding the right use case to apply to their strategy. With generative AI,?the possibilities for innovation are endless—improve customer experiences with chatbots or virtual assistants; boost employee productivity with text summarization or code generation; turbocharge production of creative content, such as art, music, or animations; and improve overall operations. With all these possibilities, it’s easy to get overwhelmed. That’s why my biggest piece of advice for selecting the right use case is to not lose sight of the problem when evaluating possible solutions, or in other words, “fall in love with the problem, not the solution.”

Putting responsible AI at the top: Our research indicates that CDOs also agree that setting guardrails around the effective and responsible use of generative AI is a top challenge. The rapid growth of generative AI brings promising innovation and at the same time raises new challenges around its safe and responsible development and use. These challenges include some that were common before generative AI, such as bias and explainability, and new ones unique to foundation models, including hallucinations, toxicity, and intellectual property protection. Taking the steps to build AI responsibly is crucial for harnessing the potential of this technology. Some successful approaches to innovating responsibly include taking a risk-based approach, defining use cases with specificity, and continually testing to build a more responsible, safe experience for your customers.

The urgency of building a strong data foundation: Generative AI's success leans heavily on the foundation of an end-to-end data strategy. A staggering 93% of CDOs acknowledge the importance of an end-to-end data strategy and its role in making their generative AI initiatives a success, but 57% of CDOs surveyed have not made changes to their enterprise data environment for generative AI. A quarter of CDOs are pursuing data integration and cleaning, while nearly one-fifth are surveying data to understand what might support generative AI use cases.

Companies that will be successful in building generative AI applications with real business value are those that will invest in a strong data foundation and use their data to customize foundation models.

?Data governance remains important: Overall, CDOs’ focus on data governance is evolving. There's been a significant uptick in time spent on data governance activities—63% this year compared to 44% in last year's CDO report. This shift underscores the rising importance of safeguarding data while maximizing its utility. The new methods of establishing governance include an “enablement” focus—making it easier to do the right thing with data. Here at AWS, we take a holistic approach to?data governance . By putting the right data and AI governance strategy in place, you can enable your teams to move and innovate faster with ready access to high-quality data.?

As organizations navigate this terrain, CDOs and data leaders will remain at the forefront, driving decisions that shape the future of business in an AI-driven world. The key to accelerating data-driven decisions lies in pairing innovation with responsibility, and strategy with execution. This is only the beginning. It’s truly an exciting time for leaders who are looking to experiment with generative AI to re-imagine and transform their business.