Imagine this: You’re steering your company’s ship through calm waters when suddenly a gigantic wave appears on the horizon. It promises to propel you forward at unprecedented speed, but it can also capsize your boat and ruin everything you’ve built if you’re not prepared. This isn’t science fiction; it’s the reality of businesses that aren’t prepared for the AI data tsunami.
Having navigated the murky waters of data and analytics for over 25 years, I can tell you that we are at a critical juncture. The AI revolution is not in sight, it is already here. It is transforming the way we run our businesses, hire our teams, and manage our data.
The problem is that most organizations are woefully unprepared, especially when it comes to data management. If you’re not prioritizing data management as part of your AI strategy, your ship is full of holes. The numbers don’t lie. Monte Carlo and Wakefield Research 2024 Survey found that 100% of data leaders feel pressured to move forward with AI implementation, even if two out of three people doubt their data is ready for AI. These organizations are navigating the AI storm without a proper compass – a solid enterprise-wide data governance strategy.
Why is data management suddenly so crucial?
It’s simple. AI amplifies everything. Remember the old adage “garbage in is garbage out”? With AI, it’s more like “garbage in is disaster out.”
AI systems make very quick decisions about whether the data they’re using is reliable or flawed. And the risk isn’t just lost revenue: it’s lost customer trust, compliance issues, and missed opportunities that could set your business back for years.
But here’s the real problem: Most organizations’ data management practices are stuck in the pre-AI era, using outdated practices, processes, and tools that can’t meet the challenge of modern use cases.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 organization?
Well, here are some scary examples of what could happen if you rush to deploy AI before you have your data management practices in place:
- Data quality issues lead to inaccurate AI results
- Potential Data Breaches and Privacy Invasions
- Unintentional biases in AI decisions
- Compliance issues with new AI regulations
- Unexpected costs, such as increased IT expenses
We need to evolve, and fast.
Data Management Makes AI Your Superpower
In the age of AI, data stewards are no longer just the gatekeepers of data quality. They are the architects of AI success. They ensure that AI models are fed accurate, unbiased, and compliant data. They can determine whether your customer value model is about to treat a whale like a minnow due to a data discrepancy.
The roles that the data manager will play in the modern era of AI include:
- The instigators of transparency:Since AI often functions as a “black box,” data managers must become masters of metadata, creating clear audit trails and data traceability. At a minimum, they can clarify how and what data helped the AI reach its conclusions.
- AI Architecture Supervisors:Data stewards must work closely with architecture teams to ensure that the data infrastructure supports AI needs, including cloud capabilities, data enrichment and interoperability, and unstructured data management.
- Process and Policy Controllers:Data managers must ensure that data policies and standards are understood and adhered to, particularly around the use of AI data.
- Detectives of Prejudice:AI doesn’t just maintain bias, it can amplify it. Data managers must skillfully identify and mitigate bias in data. This isn’t just about fairness; it’s about avoiding disastrous decisions and PR nightmares.
- Compliance Browsers:As AI regulations evolve, data managers become your first line of defense against non-compliance. They need to stay ahead of the curve, understand the rules and how they are likely to change.
- Guardians of quality:Data quality is not just about accuracy; it’s also about how well-suited it is for specific AI use cases. Data stewards need to understand the nuances of different AI models and ensure that data meets the quality thresholds specific to each.
- Synthetic Data Specialists:As the use of synthetic data increases (BCC Research Reports (the synthetic data market will reach $2.1 billion by 2028), data managers must become experts in its generation and use, ensuring that it is representative and free of bias.
So where do you find these AI-savvy data managers? In fact, you probably already have some, they just need the right tools and training. It’s all about evolving your current data manager roles to meet the demands of the AI era.
This evolution is not optional: it is existential.
The time has come
The AI wave is here, whether you’re ready or not. But with rigorous data management, you can ride the wave and reach new heights of innovation and success. Your data stewards will play a critical role in ensuring your AI initiatives are built on high-quality, well-managed data, paving the way for responsible, ethical, and effective AI adoption across your organization.
For more information on how EXL can help you meet your data management needs, visit our website.
David Crolene, Vice President of Data, Analytics and AI at EXL, a leading data analytics and digital operations and solutions company.
