A new MIT Technology Review Insights report highlights that C-suite executives are prioritizing data readiness to achieve AI success. Successful executives share a common focus on robust data foundations, emphasizing data integration, governance and security.

AI's potential to significantly boost the global economy is enormous, with generative AI alone possibly adding an additional $4 trillion annually. Businesses in every industry are considering the impact of AI and developing strategies to integrate data to meet their AI goals effectively. The report, which surveyed 300 global C-suite and senior technology leaders, stresses the long-term need for AI-ready data tools.

[CTA_MODULE]

Data integration is the top priority for AI readiness

The MIT survey reveals that data integration is the leading investment area for C-suite leaders when it comes to AI and generative AI initiatives, with 64% citing it as a key priority. A staggering 77% of respondents agree that "data integration or data movement has been a significant challenge," and 45% identify data integration and pipelines as the number-one obstacle to achieving AI readiness.

Stewart Bond, Vice President of Data Intelligence and Integration Software Service at IDC, explains the underlying reason: “Data is all over the place. It's very diverse with so many different kinds, types, formats and modes. It's also very dynamic, always moving and changing.” 

Mike Hite, Chief Technology Officer at Saks, provides a compelling example of the data volume his company handles: “In a single week, Saks considers a wide range of roughly a half a billion lines of data. That’s how you’re interacting with marketing campaigns; how you’re interacting with the site; all of your post-purchase experience, like calls to the call center; as well as everything that’s happening in our fulfillment center and in our broader network.”

Asked about challenging aspects of data integration, respondents identified four key areas: 

• Managing data volume (45%)
• Moving data from on-premises to the cloud (43%)
• Enabling real-time access (41%)
• Managing changes to data (40%)

The survey highlights the widespread recognition of the need for effective data integration, with 83% of respondents stating that their "organization has identified numerous sources of data that must be brought together to enable AI initiatives." Furthermore, 82% are prioritizing long-term, platform-agnostic solutions that can automate the process.

Governance, compliance and security are also foundational 

While data integration emerges as the top priority for achieving AI readiness, MIT highlights that data governance, compliance and security are also crucial considerations. These factors, cited by 44% of respondents, are the second most common data readiness challenge.

As Bond explains, “Organizations may have no control over someone using a piece of data in a business application and sending it to a [pre-built] generative AI model. These are critical concerns.”

Hite adds that while some sectors face tighter regulations, data governance and security are essential for any large company handling customer information. “For Saks, we have a customer data governance framework in place that helps us ensure we are responsible with our customers' data and use it to better serve them.”

Overall, 60% of respondents agree that they “need to rectify data governance, trust and security issues” to achieve their AI goals. This highlights the widespread recognition that data governance and security should be integral to AI strategies from the start, rather than being addressed as an afterthought.

Building a strong data foundation for AI with Fivetran

The MIT findings are clear: AI readiness is the result of a strong data foundation. At Saks, Hite chose Fivetran for his AI data foundation. Where it once would have required several months to onboard data, it now takes the luxury retailer as little as an hour to onboard new data sources.

The research identified three key principles for creating a lasting data foundation that can serve both current and future technologies:

1. Data foundations must precede AI deployment: Most organizations will need to address the technical debt in their data infrastructure before they can even begin developing impactful AI applications.
2. Data integration is the fundamental activity of data readiness: Bringing together disparate stores of data is the biggest challenge of preparing data for AI — making data integration capabilities a key differentiator for producing business value.
3. Data governance and security should be addressed from day one: Data workflows and tools that consider governance and security first are necessary for the most responsible and compliant use of these technologies.
   

Find out why Fivetran is the industry-leading data movement platform that powers AI innovation with automated access to centralized, cleansed and governed data.

[CTA_MODULE]