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Report Highlights Data Quality as Key Barrier to Enterprise AI Adoption

• By David Ramel
• 02/08/2024

A recent Informatica survey points to data quality as the primary hurdle to corporate adoption of generative AI. The survey ("CDO Insights 2024: Charting a Course to AI Readiness") reveals that, while businesses are eager to leverage the new wave of AI technologies sparked by innovations such as ChatGPT, data challenges several areas persist. These include governance, privacy, and others.

Nearly 14 months following the advent of ChatGPT, about 45 percent of data executives reported that they have already adopted generative AI, and an additional 53 percent plan to do so—36 percent within two years. The data leaders who were polled last October in a survey commissioned by Informatica, represent more than 600 organizations from the U.S., EU, and APAC, each generating over $500 million in revenue.

The survey underscores the complexity these leaders are facing when it comes to adopting generative AI; all respondents said they are embracing or planning to adopt the technology—more than 99 percent. The obstacles are things like data quality (42 percent), data privacy and protection (40 percent), and AI ethics (38 percent).

"But adopting generative AI has not been easy," the report said. "All who are adopting or planning to adopt generative AI (more than 99 percent) have encountered challenges, including quality of data (42 percent, led by 49 percent of those in the U.S.) and data privacy & protection (40 percent) -- familiar big picture issues data leaders face elsewhere. And AI comes with its own unique challenges as well, including AI ethics (38 percent), quantity of domain-specific data for training and fine-tuning of LLMs (38 percent), and AI governance (36 percent). Other AI challenges include regulatory compliance (33 percent), avoiding bias (32 percent), and preparing unstructured data to work with LLMs (32 percent)."

A list of the key takeaways from the report by Informatica includes:

• Despite the challenges, 73 percent of data leaders intend to use generative AI to hasten the time to value with quicker data insights, and 66 percent aim to boost productivity via automation.
• Data readiness is increasingly important for effective AI and data strategy, with 43 percent of leaders measuring strategy success by the preparedness of data for AI, a shift from the previous year's focus on improving data usage in business decisions (45 percent).
• The complexity and fragmentation of data sources are ongoing issues, with 41 percent of leaders balancing over a thousand data sources, though this represents a decrease from the previous year (55 percent). However, a projected increase in data sources is anticipated by 79 percent in the coming year.
• Organizational resistance presents another challenge to data strategies, with 98 percent of leaders citing internal barriers, including lack of leadership support and difficulty justifying ROI for budgets.
• Investments in generative AI are paralleled by investments in data management, with priorities for 2024 including delivering reliable data for AI (39 percent) and enhancing data-driven culture (39 percent).

"With AI and data management both high priorities for data leaders' 2024 strategies and beyond, it's crucial these two go hand in hand: realizing value from AI means nothing without data management, and AI can be a powerful way to achieve data management goals," said the report's conclusion. "As data leaders consider both roadblocks as well as the tech- and data-related challenges, investments in data management can operate in tandem with AI to achieve their data management goals."

The report concludes that AI and data management must be aligned to realize the full potential of AI, with data management being a critical factor for extracting value from AI initiatives. For businesses to prepare for widespread AI deployment, including Microsoft's suite of Copilots, robust data governance is essential. Without it, organizations may face complications when implementing AI solutions broadly.

Data-related concerns are becoming more prominent in enterprise AI implementations as organizations realize that specialized AI constructs modeled after the LLM that powers ChatGPT are most useful when trained on their own data. That training requires clean, quality data, vaulting data governance into a top-most concern. On the Microsoft side of things, Virtualization & Cloud Review writer Paul Schnackenburg penned a detailed look at what needs to be done to prepare for widespread AI rollouts

The article explains what you need to do to prepare your business for all of Microsoft's different Copilots, "Because if you don't have good data governance, you'll likely have some 'interesting times' ahead if you roll out Copilot broadly."