AI’s data tsunami: Why your data stewardship needs an overhaul

Picture this: You’re steering your organization’s ship through calm waters when suddenly, a massive wave looms on the horizon. It promises to propel you forward at unprecedented speeds, but it can also capsize your boat ruining everything you’ve built if you’re not prepared. This isn’t science fiction – it’s the reality for organizations that are unprepared for AI’s data tsunami.

As someone who’s navigated the turbulent data and analytics seas for more than 25 years, I can tell you that we’re at a critical juncture. The AI revolution isn’t coming – it’s here. And it’s transforming how we operate our businesses, recruit our teams, and manage data.

Here’s the kicker: Most organizations are woefully unprepared, particularly when it comes to data stewardship. If you’re not prioritizing data stewardship as part of your AI strategy, your ship is full of holes. The numbers don’t lie. A 2024 survey by Monte Carlo and Wakefield Research found that 100% of data leaders feel pressured to move forward with AI implementations even though two out of three doubt their data is AI-ready. Those organizations are sailing into the AI storm without a proper compass – a solid enterprise-wide data governance strategy.

Why is data stewardship suddenly so crucial?

It’s simple. AI amplifies everything. Remember the old adage “garbage in, garbage out?” With AI, it’s more like “garbage in, catastrophe out.”

AI systems make lightning-fast decisions whether the data they are using is good data or flawed. And the risk is not just about lost revenue – it’s about eroded customer trust, compliance nightmares, and missed opportunities that could set your business back for years.

But here’s the real rub: Most organizations’ data stewardship practices are stuck in the pre-AI era, using outdated practices, processes, and tools that can’t meet the challenge of modern use cases. Is this your org?

Well, here are some scary examples of what could happen if you rush to deploy AI before getting your data stewardship practices in order:

1. Data quality issues cause flawed AI outputs





2. Potential data breaches and privacy violations





3. Unintended biases in AI decisions





4. Compliance issues with emerging AI regulations





5. Unexpected costs, such as increased compute expenses

We need to evolve, and fast.

Data stewardship makes AI your superpower

In the AI era, data stewards are no longer just the data quality guardians. They’re architects of AI success. They ensure AI models are fed accurate, unbiased, and compliant data. They can tell if your customer lifetime value model is about to treat a whale like a minnow because of a data discrepancy.

Roles the data steward will play in the modern AI age include:

1. Transparency instigators: With AI often operating as a “black box,” data stewards must become masters of metadata, creating clear audit trails and data lineage. They can at least clarify how and what data supported AI to reach its conclusions.





2. AI architecture overseers: Data stewards should work closely with architecture teams to ensure the data infrastructure supports AI needs, including cloud capabilities, data enrichment and interoperability, and handling unstructured data.





3. Process and policy hall monitors: Data stewards must ensure data policies and standards are understood and followed, especially related to AI data use.





4. Bias detectives: AI doesn’t just maintain biases – it can amplify them. Data stewards must adeptly identify and mitigate bias in the data. It’s not just about fairness; it’s about avoiding disastrous decisions and PR nightmares.





5. Compliance navigators: As AI regulations evolve, data stewards become your frontline defense against non-compliance. They must stay ahead of the curve, understanding the rules and how they’re likely to change.





6. Quality gatekeepers: Data quality isn’t just about accuracy – it’s about suitability for specific AI use cases. Data stewards should understand the nuances of various AI models and ensure the data meets the unique quality thresholds for each.





7. Synthetic data specialists: As the use of synthetic data grows (BCC Research reports that the synthetic data market will reach $2.1 billion by 2028), data stewards must become experts in its generation and use, ensuring it’s representative and bias-free.

So where do you find these AI-savvy data stewards? The truth is you probably already have them – they just need the right tools and training. It’s about evolving your existing data stewardship roles to meet the demands of the AI era.

This evolution isn’t optional – it’s existential.

The time is now

The AI wave is here, whether you’re ready or not. But with strong data stewardship, you can ride the wave to new heights of innovation and success. Your data stewards will play a pivotal role in ensuring your AI initiatives are built on a foundation of high-quality, well-governed data, paving the way for responsible, ethical, and effective AI adoption across your enterprise.

For information on how EXL can help with your data stewardship needs, visit our website.

David Crolene, vice president of data, analytics & AI at EXL, a leading data analytics and digital operations and solutions company.