AI’s data problem: How to build the right foundation

2024 was undoubtedly “the year of AI,” with businesses across the globe attempting to fast-track implementations. In fact, EY’s 2024 Work Reimagined Survey found that Generative AI (GenAI) adoption skyrocketed from 22% in 2023 to 75% in 2024. Meanwhile, Forrester found that 67% of AI decision-makers plan to ramp up their GenAI investments in the coming year.

The potential value GenAI could deliver is undeniable, particularly in expediting content generation or powering smarter chatbots. With the right systems in place, businesses could exponentially increase their productivity.

Yet, as transformative as GenAI can be, unlocking its full potential requires more than enthusiasm—it demands a strong foundation in data management, infrastructure flexibility, and governance. Without these critical elements in place, organizations risk stumbling over hurdles that could derail their AI ambitions.

Trusted, Governed Data

The output of any GenAI tool is entirely reliant on the data it’s given. The better the data, the stronger the results. It sounds simple enough, but organizations are struggling to find the most trusted, accurate data sources.

According to a recent Cloudera survey, just over a quarter (26%) of IT decision-makers said trusted data was a challenge to implementing AI within their organizations. On top of that, 73% of respondents said their company’s data exists in silos and is disconnected, and while 40% believe they are the sole person who knows where data exists in the organization.

With data existing in a variety of architectures and forms, it can be impossible to discern which resources are the best for fueling GenAI. Not only that, but giving GenAI access to any data sources also opens up incredible governance risks. If you don’t know where your data exists or which data your LLMs have access to, how can you ensure you’re being compliant?

As AI solutions process more data and move it across environments, organizations must closely monitor data flows to safeguard sensitive information and meet both internal governance guidelines and external regulatory requirements. Enterprises that fail to adapt risk severe consequences, including hefty legal penalties and irreparable reputational damage.

The Right Foundation

Having trustworthy, governed data starts with modern, effective data management and storage practices. This means having an environment capable of handling data in all its forms—structured and unstructured—which is increasingly complex to manage as data volumes grow.

A hybrid approach often offers the best solution, allowing organizations to store and process sensitive information securely on-premises while leveraging the scalability and flexibility of the cloud for less critical workloads. It means managing and storing data where it will bring the most value to the enterprise, without having to move it. With the right hybrid data architecture, you can bring AI models to your data instead of the other way around, ensuring safer, more governed deployments.

While only one-third of respondents currently deploy multi-cloud or hybrid data architectures, an overwhelming 93% agreed that these capabilities are essential for adapting to change. The infrastructure flexibility afforded by a hybrid approach ensures your company is ready to integrate tomorrow’s innovations, rather than being constrained by the limitations of yesterday’s solutions.

Ensuring Data is Secure and Compliant

GenAI has the potential to revolutionize productivity, but to harness its power, organizations need a strong foundation. By adopting the right data management practices, enterprises gain the ability to track, secure, and govern their data seamlessly from end to end, empowering them to know that the data powering their AI initiatives will deliver the most trustworthy, valuable insights.

To learn more about GenAI and how Cloudera can help you maximize your investments, click here.