?? When Data Strategy Meets Zen Philosophy

Hello, data Shokunin-deshi!

The Annual Data Strategy Dance

As the year wraps up, you're in yet another data strategy meeting. Stakeholders have a mountain of requests:

• Marketing wants better campaign analytics—this time, with a 360-degree revenue forecast!
• Finance needs consolidated projections.
• The product demands more user insights.
• And analytics teams? They’re frustrated with a system that’s too slow to be useful.

But this year feels different. You've set up a ticketing system to organize it all. Your stakeholders gave their blessing, management is on board. This time, you think, you're ready.

Fast Forward Three Months

The familiar chaos returns:

• A backlog that's grown out of control
• Every team claims their needs are "urgent"
• Strategic goals buried under daily firefighting
• Data volumes exploding, query engines struggling
• Costs creeping upward
• Your 8-hour workday? A distant memory

I've seen this cycle in countless organizations. It was then I realized we need more than just a better strategy – we need an entirely new approach.

The Basketball Coach Who Changed My Perspective

One day, I was reading "The Last Season: A Team in Search of Its Soul" by Phil Jackson. His approach fascinated me—specifically how he brought Zen principles to the basketball court. Jackson's success—11 NBA rings—wasn't just about tactics. It was about balance, focus, and sustainable success.

Right after publishing my book "Data is Like a Plate of Hummus," I had an epiphany: our data ecosystem needs a similar transformation. Data success isn't just about better KPIs or faster dashboards. It's about creating an ecosystem that's resilient, adaptable, and aligned with real business needs.

“In the beginner’s mind there are many possibilities; in the expert’s mind there are few.”― Phil Jackson, Eleven Rings: The Soul of Success

The Middle Way: Beyond Binary Choices

In data architecture, we often face seemingly binary choices:

• Build vs. Buy
• Speed vs. Scalability
• Innovation vs. Stability

But what if there's a third path?

Real-World Application: The Idealo Case

At idealo, we faced a classic dilemma:

• A processing engine causing user frustration
• A fragmented data ingestion process needs to be reform

Instead of choosing between two "urgent" priorities, we applied the Middle Way to find a balanced solution. We evaluated:

• Potential damage to company operations
• Impact on spending
• Possible savings from each solution
• Risks of inaction

While already deep into fixing the ingestion engine, we realized we needed to shift. We embraced imperfection and found a middle path between applying another quick patch and building an entirely new, more advanced solution.

The Zalando Experience: Building Trust Through Balance

At Zalando, we tackled an ambitious challenge: building a forecasting model to predict revenue per session for 30, 360, and 720 days ahead. Our journey centered on balancing:

• Technical accuracy (90% forecast accuracy goal)
• User trust and adoption

We embraced the Wabi-Sabi philosophy of imperfection, acknowledging that while models can't achieve 100% accuracy, we could build user trust by being transparent about capabilities and limitations. This approach helped us move forward without getting stuck pursuing impossible perfection.

Another example came from our dashboard development. Caught between slow business objectives visualization and a year-long Microstrategy implementation, we found a middle way with Tableau. This balanced solution allowed us to collect, store, process, and visualize data in weeks instead of months and later on create the base for the Microstrategy dashboard.

The Data Ecosystem Vision Board: Where Philosophy Meets Practice

This framework emerges from combining Zen principles with practical data needs. It consists of three key layers:

1. Present Reality Layer Current capabilities Pain points Resource constraints
2. Future Vision Layer Desired capabilities Growth targets Tool-agnostic solutions
3. Success Metrics Layer Data Utilization KPIs ROI measurements User adoption metrics

This framework acknowledges data imperfections and challenges, balancing simplification against complexity. It helps prevent both oversimplification (which can have downstream consequences) and over-complexity (which can lead to delayed value delivery).

“There’s a Zen saying I often cite that goes, “Before enlightenment, chop wood, carry water. After enlightenment, chop wood, carry water.” The point: Stay focused on the task at hand rather than dwelling on the past or worrying about the future.”― Phil Jackson, Eleven Rings: The Soul of Success

Moving Forward: Balancing Action and Reflection

The path to data excellence isn't about perfect solutions—it's about balanced progress. By embracing both Eastern wisdom and practical metrics, we can build data ecosystems that truly serve our organizations' needs.

Having northern stars to guide us and principles to simplify decisions, we can:

• Reduce frustration
• Improve communication
• Transform data from a cost center to an impact driver with measurable ROI

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