Centralized vs. Decentralized Data Teams: The Insights Supply Chain Framework Part 2

[00:00:00] Michael Meyers: We're crossing a threshold where being data-driven is no longer just a competitive advantage. It's becoming a requirement for survival, and yet for most organizations, the process of transforming data into actionable insights is hindered by many challenges, among the biggest being ad hoc collaboration and unclear ownership.

[00:00:17] Michael Meyers: That's why Dr. Duru Ahanotu, the leader of Tag1's data strategy team, created the Insight Supply Chain Framework. It brings structure to how you organize your data professionals and teams, enabling insights to flow through your organization.

[00:00:33] Michael Meyers: This is the second episode in our series on the framework. In our first episode, we provided a general overview. Today, we're diving deep into a critical decision: whether organizations should centralize their data experts into one team or decentralize them across business units.

[00:00:57] Michael Meyers: Choosing the right structure is fundamental to success with both your data professionals and your data. In upcoming episodes, we'll explore the Data Organization Matrix, whether data should be a core competency, how AI impacts this framework, and how organizations progress along the data maturity curve. Thanks for joining us. Let's get started.

[00:01:33] Michael Meyers: Hello, welcome to Tag1 Team Talks, the Tag1 Consulting podcast. I'm Michael Meyers, Managing Director at Tag1, and today I'm joined by Dr. Duru Ahanotu, creator of the Insight Supply Chain Framework and leader of Tag1’s Data Science team. Duru, welcome back to the show.

[00:01:50] Dr. Duru Ahanotu: Thank you, Michael. It's good to be here.

[00:01:52] Michael Meyers: With three degrees from Stanford, including a PhD in management science and engineering, and a career spanning expert systems, data science leadership at Yahoo and Dictionary.com, and scaling client data ecosystems at Tag1, Duru brings both academic depth and real-world experience.

[00:02:20] Michael Meyers: Before we dive deeper, I want to share a little about Tag1. We are the number two all-time contributor to Drupal, the world’s second most popular content management system. For nearly 20 years, we've helped architect the open web.

[00:02:42] Michael Meyers: We're a full-service strategic partner, applying architectural expertise across discovery, design, AI strategy, infrastructure, and large-scale web platforms. We're trusted by organizations like Sumitomo, NTT Data, the European Patent Office, and the American Federation of Teachers.

[00:03:13] Michael Meyers: Duru, for those who missed our first episode, can you walk us through the core problem your framework addresses and why it's fundamental?

[00:03:45] Dr. Duru Ahanotu: The fundamental problem is how organizations organize data professionals without constantly cycling between centralization and decentralization. The framework provides a structured way to understand how data flows from raw inputs to insights and which roles support decision-making along the way.

[00:04:26] Michael Meyers: Today we're going to dive deep into centralized versus decentralized resourcing, but first, can you preview the Data Organization Matrix and how it factors into these decisions?

[00:05:02] Dr. Duru Ahanotu: At a strategic level, organizations must decide whether data is a core competency. That determines how skill sets and teams should be centralized or decentralized and where they land on the Data Org Matrix.

[00:06:00] Dr. Duru Ahanotu: One axis represents centralized versus decentralized skill sets—generalists versus specialists. The other represents how teams are organized. Each point on the matrix defines how collaboration happens along the Insight Supply Chain.

[00:07:14] Dr. Duru Ahanotu: How you apply this depends on your data maturity. Organizations with a single product often benefit from centralized teams to standardize metrics, while diversified organizations may need decentralized teams aligned closely to business units.

[00:10:52] Dr. Duru Ahanotu: Context matters more than size. Data teams must align with how value is delivered to customers. Career paths and opportunities may differ based on organizational structure and growth stage.

[00:12:09] Michael Meyers: Career paths are critical, especially given how difficult it is to find and retain strong data scientists. Your framework clearly emphasizes that.

[00:13:11] Dr. Duru Ahanotu: Data professionals need clear paths and visibility into where they create value. That motivation is embedded directly into the Insight Supply Chain.

[00:14:43] Dr. Duru Ahanotu: Data users exist everywhere, but data professionals enable them. The role of centralized teams is to provide strong data foundations so others can focus on solving business problems.

[00:17:30] Dr. Duru Ahanotu: My experience at Yahoo—moving between decentralized and centralized models—highlighted how arbitrary these decisions often were. That’s what led me to create a more principled framework.

[00:23:14] Dr. Duru Ahanotu: You can brute-force any model, but it’s exhausting and inefficient. Frameworks exist to reduce friction, preserve energy, and create sustainable systems.

[00:26:22] Dr. Duru Ahanotu: Clear frameworks create healthier environments, better career paths, and stronger alignment between data professionals and business value.

[00:28:02] Michael Meyers: Thank you for digging into centralization versus decentralization with us. We look forward to future episodes on the Data Org Matrix, data maturity, and AI’s role in the framework.

[00:28:30] Michael Meyers: You can find more episodes at tag1.com/podcast. We’d love your feedback. Thanks to Tracy Cooper and June Gregg for producing today’s episode, with input from Hank VanZile and Cassey Bowden. Until next time, take care.