Unlocking Enterprise Value: How Master Data Drives Better AI

A Q&A with Bernie Brennan, Chairman of Digital Wave Technology

In today’s AI-fueled race for innovation, companies are investing heavily in advanced technologies, yet many struggle to unlock true business value. The culprit? Fragmented, unreliable data.

We sat down with Bernie Brennan, Chairman of Digital Wave Technology, former Chairman of the National Retail Federation, and industry thought leader, to discuss why the key to unlocking AI’s full potential isn’t just smarter tools – it’s smarter data.

Q: Bernie, why is master data getting so much attention now in the context of AI?

Bernie Brennan: Because the difference between an AI strategy that scales and one that stalls. AI is only as good as the data it’s trained on. If that data is inconsistent, incomplete, or siloed, the outputs cannot be trusted, and the business outcomes will reflect that.

Today’s executives understand that to drive intelligent automation, personalization, and prediction, you need centralized, clean, and governed master data that is accessible in real time across the enterprise.

Q: What is the cost of ignoring the master data layer when implementing AI?

Bernie Brennan: It’s enormous – and growing. Companies are spending millions on AI tools only to find they are operating on top of broken data plumbing. That leads to inaccurate insights, disjointed customer experiences, and decision-making blind spots. 

For instance, a brand might launch a product across retail partners, only to discover that descriptions, images, and core specifications differ across each channel, which confuses shoppers and erodes trust. The issue wasn’t the creative team; it was inconsistent data at the source.

I have seen businesses with best-in-class AI models produce flawed pricing, content, and supply chain recommendations not because of the model, but because the data feeding it was out of sync. This results in missed revenue, margin erosion, and massive inefficiencies. Getting master data right is no longer optional; it’s a strategic mandate.

Q: Digital Wave Technology positions master data as central to the AI-native ONE℠ Platform. Why is that so important?

Bernie Brennan: Because real AI impact doesn’t come from a bolt-on. It comes from a unified foundation. At Digital Wave Technology, we built the AI-native ONE Platform from the ground up with master data management, AI, Generative AI, Agentic AI, and analytical intelligence embedded at the core.

We’re not retrofitting AI into an old architecture. We are fundamentally rethinking how AI and MDM should work together. It is about building an architecture where intelligence is inherent in every layer. That is what makes us unique.

Master data management is critical for AI and GenAI success because it ensures consistent, accurate, and unified data. This is the foundation for effective model training and decision-making. But we go further.

An AI-native platform built on MDM doesn’t just support AI, it optimizes the entire AI lifecycle by embedding intelligence into data management itself. It enables proactive data curation, automated insights, and adaptive governance, which are crucial for next-gen AI applications. Without this, organizations risk fragmented data ecosystems that undermine AI’s potential.

Our customers benefit from an integrated ecosystem that accelerates time to value, reduces TCO, and drives better, faster business decisions across the enterprise.

Q: What industries stand to benefit most from mastering their data foundation?

Bernie Brennan: We partner with retailers, CPG brands, and healthcare companies but truly, any organization with complex product or customer ecosystems, channel diversity, or large-scale personalization requirements will benefit from a strong master data foundation.

When you are managing thousands of products, forecasting inventory, or constantly evolving consumer expectations, clean and connected data becomes your greatest strategic asset. Successful companies are making master data a core pillar of their AI strategy, not an afterthought.

Q: What is your advice to executives just starting to clean up their data?

Bernie Brennan: Start where data drives revenue. Prioritize foundational domains like product data, attribution, and pricing. Choose a platform that is AI native, not retrofitted, and built to scale with your business complexity.

And here is the key: stop treating master data as a backend IT project. It’s a frontline enabler of growth, agility, and innovation. When executives lead with data quality and governance, the rest of the AI strategy becomes far more impactful.

Final Thoughts

Mastering your data may not generate flashy headlines, but it is what separates AI pilots from AI profits. As AI adoption accelerates and expectations rise, a strong data foundation becomes the differentiator.

As Bernie Brennan makes clear: to unlock enterprise value at scale, you don’t need just AI –you need AI built on a backbone of trusted master data, within an architecture designed from day one to support it.