A Serviceberry-Inspired Perspective on Data Management
This piece is inspired by a blog post from my friend and colleague Kathy Allen, The Nature of Relationships, which explores the idea of adopting a gift economy mindset based on reciprocity, sharing, and relationships. Kathy’s reflections on the serviceberry tree—an emblem of mutual flourishing in nature—offer a powerful metaphor for how we should think about data management. Instead of treating data as a scarce resource to be hoarded, what if we approached it as an evolving, interdependent system where value is created through thoughtful exchange and collaboration?
Courtesy Simon & Schuster
The Death of the Data Silo is Not the End of the Problem
For years, we’ve heard that breaking down data silos is the holy grail of business transformation. We’ve been told that better pipelines, integrated analytics, and AI-driven decision-making will finally unlock the full potential of enterprise data. But here’s the question no one seems to ask: What if we’re still thinking too small?
The real challenge isn’t just technological—it’s conceptual. We don’t just need better data governance or cleaner metadata. We need a way of thinking that moves beyond technical optimization and into deeper creative problem-solving. That’s where multidisciplinary thinking comes in.
Why Data Needs the Complexity of Cross-Disciplinary Perspectives
Traditionally, data management has been an engineering and compliance function. It was about control—keeping things structured, auditable, and efficient. But as AI and automation embed themselves deeper into our decision-making processes, the old guard of data thinking is hitting a wall with recommendations of “federated” or “unfederated” when it must be both. Today's most pressing challenges aren’t just technical; they’re ethical, philosophical, and deeply human.
A multidisciplinary approach forces us to confront these complexities:
From Systems Thinking (Ecology & Biology): Instead of treating data as a static asset, we begin to see it as an evolving ecosystem. How does data self-organize? What relationships form between datasets, and how do they adapt over time?
From Cognitive Science & Behavioral Economics: We start to question how humans interpret and act on data. Where do biases creep in? How do different users—executives, frontline workers, customers—experience and misinterpret the same dataset?
From Philosophy & Ethics: We examine the real-world consequences of AI-driven decision-making. Who bears the burden when an AI rejects a loan application or prioritizes one job candidate over another?
From Design & Storytelling: We move beyond dashboards and ask how data is experienced. How do we make complex information intuitive? How do we design for insight rather than just access?
This is a fundamental shift—from data as input to data as narrative, from managing transactions to managing consequences.
The Shift from Hype to Real Problem Solving
The AI-driven data revolution has been awash in jargon: DataOps, ML pipelines, AI-first enterprises. But what does it all mean if businesses still struggle to extract real value? Multidisciplinary thinking helps cut through the hype and exposes the gaps in our current approach:
Hype: “AI will automate decision-making.”
Reality: AI doesn’t understand nuance. It amplifies existing biases, meaning the burden of thoughtful governance is greater, not less.
Craft: Data managers must evolve from custodians of data to stewards of insight—shaping how AI is trained, validated, and audited for fairness and effectiveness.
Hype: “Data-driven organizations move faster.”
Reality: Speed without understanding is just acceleration toward the wrong conclusions.
Craft: Practitioners need to prioritize critical thinking over automation—designing workflows that ensure humans remain the ultimate decision-makers, not just rubber stampers of AI recommendations.
Hype: “Breaking down silos will solve everything.”
Reality: Silos are often a symptom of deeper misalignments—between incentives, cultures, and goals.
Craft: The craft of data management requires bridge-building—facilitating conversations between disciplines, ensuring data serves broader organizational narratives rather than isolated technical objectives.
What This Means for Data Management Leaders
If we take this shift seriously (and we should) it changes the role of data management from an IT concern to a much more strategic discipline. It means:
Hiring not just data engineers and data science workers, but cognitive scientists, ethicists, and user experience designers.
Expanding governance models to include ethical risk assessment, not just compliance.
Rethinking dashboards and reports as interfaces for human decision-making, not just reflections of stored data.
Prioritizing interpretability over sheer analytical power—because a perfectly optimized model that no one trusts or understands is useless.
The Future: Learning to Think With Data, Not Just Process It
The companies that thrive in an AI-first world won’t just be the ones that invest in the best technology. They will be the ones that rethink their approach to data itself—embracing complexity, drawing from multiple disciplines, and moving beyond the tired clichés of “data as the new oil.”
Data is not oil. It is not a resource to extract, refine, and burn for immediate value. Kathy Allen, author of Leading From the Roots, likes to look at it more like an ecosystem—living, evolving, and interconnected. I like to look at it more like a craft that gets honed and developed over a lifetime of practice (yes-yes, the book is still pending!). And just like in nature, the organizations that learn how to nurture rather than just consume data will be the ones that shape the future. And craftspeople-because of how well they adapt to change—find themselves beautifully equipped to deal with a world that no longer exists.
Are we ready to move beyond the old paradigms? Or will we keep applying the same thinking to a world that is growing increasingly complex? The answer will determine who thrives in the next era of data-driven innovation.
CHRISTINE HASKELL, PhD, is a collaborative advisor, educator, and author with 30 years in technology, driving data-driven innovation and teaching graduate courses in executive MBA programs at Washington State University’s Carson School of Business and is a visiting lecturer at the University of Washington’s iSchool. She lives in Seattle.
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Driving Your Self-Discovery (2024), Driving Data Projects: A comprehensive guide (2024), and Driving Results Through Others (2021)