Podcast: Don’t Panic! It’s Just Data 

Guest: Jignesh Patel, Director of Product Strategy at Stibo Systems and Elsebeth Gundersen Jensen, Product Owner at Nets

Host: Dr Joe Perez, Data Analytics Expert and Amazon Bestselling Author

We’re living in times of an always-on digital economy where there’s no room for data errors. In the recent episode of the Don’t Panic It’s Just Data podcast, host Dr Joe Perez, Data Analytics Expert and Amazon Bestselling Author, sat down with Jignesh Patel, Director of Product Strategy at Stibo Systems and Stibo Systems’ customer, Elsebeth Gundersen Jensen, Product Owner at Nets. 

Perez pointed out that even the smallest inconsistency can "ripple completely across an entire operation, instantaneously." This reality is prompting enterprise tech leaders to rethink how they manage, govern, and use data, especially with the rapid growth of AI adoption.

Overall, the guests send out a clear message – trusted, real-time data is now a crucial part of business infrastructure.

Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn't

What is the Hidden Cost of Untrusted Data?

For large enterprises, especially those growing through mergers and acquisitions, fragmented data systems are almost unavoidable. Jensen noted that when combining multiple customer portfolios, inconsistencies often arise in even the simplest fields, like organisation numbers formatted differently in various systems.

“When you bring in different customer portfolios, you will also get this scattered data picture that you don’t want in a master data management system,” she explained.

According to Patel, the lack of trusted data impacts four key areas, which include customer experience, revenue growth, decision-making, and operational efficiency. Without a unified customer view, enterprises struggle to offer personalised experiences or spot cross-sell opportunities. Moreover, analytics based on unreliable data undermine executive confidence and increase compliance risks.

These issues are made worse by speed. Alluding to her observations, Jensen told Perez and Patel that modern customers expect contract changes or service interactions to be updated almost instantly. “They don’t want to wait a day,” she stated. “Everything should be faster, better, and accurate.”

Also Watch: Why is a Customer Data Strategy a Competitive Edge?

How are Enterprises Mastering Intelligence?

Traditionally, Master Data Management (MDM) has focused on creating the “golden record,” a single, reliable version of key business entities like customers or products. While this remains important, Patel believes this idea is changing quickly in the AI era.

“MDM is moving beyond data correctness towards what I call mastering intelligence,” he said. “AI systems rely on trusted context—understanding what entities are, how they relate, and the business rules that apply.”

This change is part of a larger transformation in enterprise architecture. Decision-making is no longer limited to human-driven dashboards; it is increasingly spreading across applications, analytics platforms, and AI agents acting in real time. In such a setup, inconsistent data does not just create errors but it can amplify it.

“AI doesn’t eliminate the need for MDM or data governance. It emphasises it,” stated Patel. For enterprises heavily investing in AI, this insight is vital. Without a strong data foundation, AI models might provide insights but not dependable results.

As enterprises move toward AI-driven and even agent-based business models, the need for trusted data will grow even more important. Patel highlights new questions from the C-suite – How will AI agents find my products? Why isn’t my business being recommended?

Are you enjoying the content so far?

The answer increasingly depends on structured, high-quality data. “AI success is dependent on trustworthy data,” Director of Product Strategy at Stibo Systems says. “MDM and governance are the foundation for the next generation of intelligent business systems.”

For enterprise leaders, the key directive to note is in the race to implement AI, data trust is the competitive edge and not only the requirement. 

Key Takeaways

  • Real-time trusted data is essential for enterprise AI success and operational resilience.
  • Poor data quality directly impacts customer experience, revenue growth, and compliance.
  • Modern Master Data Management (MDM) is evolving from “golden records” to AI-ready data intelligence.
  • Proactive data governance must replace reactive data cleanup to scale in real-time environments.
  • A unified data model is the foundation for accurate, consistent, and AI-driven business insights.

Chapters

  • 00:00 Introduction to Data Governance and MDM
  • 02:06 The Shift to Real-Time Data
  • 05:27 Business Risks of Lacking Trusted Data
  • 08:20 Growth Through Mergers and Acquisitions
  • 15:29 The Role of MDM in AI Initiatives
  • 20:02 Transitioning to Proactive Data Management
  • 22:01 Advice for CIOs on Managing Product Data

For more information, please visit em360tech.com and stibosystems.com. 

To learn more about AI in the MDM space and how they’re progressing enterprise analytics intelligently, follow:

Stibo Systems LinkedIn: @StiboSystems

Stibo Systems X: @StiboSystems

Stibo Systems YouTube: @StiboSystemsGlobal

EM360Tech YouTube: @enterprisemanagement360

EM360Tech LinkedIn: @EM360Tech

EM360Tech X: @EM360Tech

Follow: @EM360Tech on YouTube, LinkedIn and X

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