Wabi Sabi Your Data: The Art of Data ROI

Unlock Data ROI: Master the Balance of Cost and Value for Strategic Impact

Hello and happy Hump Day, data Shokunin-deshi!

I used to think data was all about numbers—cold, precise, and absolute. But the more I worked with it, the more I realized that data is messy, ambiguous, and full of contradictions. And that's where the magic happens.

A few years ago, I found myself leading a data transformation project. Everyone wanted results—fast. Leadership wanted dashboards yesterday, teams were drowning in ad hoc requests, and somewhere in the middle, the actual value of our data was getting lost. We were either too rigid with governance or too chaotic in exploration. The problem wasn’t data—it was how we used it. We needed a middle path: a way to bring structure without killing creativity.

The Trade-offs We Can't Ignore

Every data decision comes with a trade-off. Take KPIs—if you focus too much on short-term goals, you risk optimizing for vanity metrics. Go too deep into long-term vision, and you might never ship anything practical. I've seen teams paralyzed by overplanning and others burning out from chasing short-lived wins.

So, how do you balance it? Ask yourself:

• What's the smallest meaningful insight we can act on today?

• What's the bigger picture we can't afford to lose sight of?

• How do we align our data efforts with real business impact?

Why This Matters Now More Than Ever

A recent survey showed that 70% of CEOs are prioritizing GenAI tools. The pressure on data teams is increasing. If we don't build strategies that balance quick wins with long-term growth, we'll end up chasing trends instead of creating real value. The companies that win aren't the ones that collect the most data—they're the ones that make it work for them.

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What we will learn today:

In this edition, we'll master the delicate art of Data ROI by:

• ✅ Pinpoint where your data investments are driving real business impact

• ✅ Avoid common pitfalls that turn data into a cost center

• ✅ Make better decisions by knowing when to trust data vs. intuition

• ✅ Identify and sunset low-value data initiatives to maximize ROI"

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Wabi Sabi Your Data: The Garden of Data ROI

Imagine you're tending to tomato plants in your garden. Each season brings different conditions—soil quality changes, sunlight patterns shift, and new pests emerge. Last year, we had so many snails that it was impossible to leave plants outside without losing the young ones to the mouths of the snails. The Zen principle of Shoshin (beginner's mind) teaches us to approach each growing season with fresh eyes, noticing subtle changes we might otherwise miss.

Just like in gardening, data investments require seasonal adjustments. Some initiatives need pruning, some need more sunlight (visibility), and others need to be retired. What worked last year may not work today.

Similarly, each data initiative is an Ichigo Ichie—a unique moment that will never happen again. By carefully analyzing what contributes to a successful "harvest" of insights, you can optimize your data practices and increase your overall yield (ROI).

As MIT Sloan research revealed, only 15% of companies fully integrate their data strategy with their business strategy, creating misaligned investments that fail to drive growth. Let's correct this imbalance.

Strategic Pitfalls in Data ROI Management

1. Fragmented Cost Visibility Executive Pitfall: When costs are spread across departments, the true investment remains hidden. Strategic Solution: Implement centralized cost tracking with cloud tags and user identification. if, due to GDPR, you can’t tag individuals, then tag teams, creating financial transparency that drives accountability.

2. Value Attribution Blindness Executive Pitfall: Without clear value metrics, data initiatives appear as pure cost centers. Strategic Solution: Start with simple metrics—marketing campaigns optimized (€1,000 saved), analyst time reclaimed (€40/hour)—then build more sophisticated attribution models.

3. Strategic Misalignment Executive Pitfall: Data investments disconnected from business objectives create impressive dashboards that drive no decisions. Strategic Solution: For each data initiative, articulate its direct connection to at least one strategic business goal and expected return.

4. Innovation-Optimization Imbalance Executive Pitfall: Focusing exclusively on new capabilities while ignoring optimization of existing data assets. Strategic Solution: Balance your portfolio: 70% optimization of existing assets, 30% innovation for new capabilities.

Why don’t we treat data like meeting costs? Every meeting has a visible cost in salaries and time, but we still question their necessity. Yet, data initiatives—where costs are often hidden across infrastructure, teams, and maintenance—rarely face the same scrutiny. What if every dashboard had an ‘operational cost’ label?

The Business Impact Trifecta

Effective Data ROI strategies create value across three critical business dimensions:

1. Cost Savings: Reduced infrastructure spending, optimized processing, and automated repetitive tasks

2. Operational Excellence: Faster decision cycles, reduced errors, and enhanced productivity

3. Revenue Generation: Improved customer targeting, personalized experiences, and new data-driven products

A Gartner study found that 85% of AI projects fail to deliver value due to rushed implementation and unclear business goals. Avoid becoming part of this statistic by approaching your data garden with intention and care.

Case Study: The ROI of Less Data

An e-commerce client was spending €32,000 monthly on detailed user funnel tracking—capturing every click from the landing page to checkout. The returns on having the data were sketchy and unclear, so we decided to explore a bit more. After applying the ROI framework, we discovered that only three key transition points influenced conversion strategy.

By focusing on these critical points, they reduced data processing costs by 70% while maintaining the same conversion optimization capabilities. The freed budget was reinvested in customer retention analytics, generating a 15% improvement in repeat purchases.

You don't need to have all the data daily. Even quarterly or half-yearly analysis can provide good direction while maintaining low costs. I don't argue against using data at all—I argue you should set the timing intervals you need because maybe you can use it for decisions but can't drive value every day with it.

The lesson? Sometimes, the most valuable data strategy is deciding what not to measure.

Reflection Questions:

1. Which data initiatives might deliver greater value through strategic pruning rather than expansion?

2. How might your organization's approach to data change if every initiative required a clear ROI forecast?

3. What if you viewed your data strategy as a garden requiring different care at different seasons?

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Data isn't just a technical challenge; it's a leadership challenge. The best teams aren't the ones with the most advanced tools but the ones that navigate complexity with clarity and purpose. Where do you see the biggest trade-offs in your data strategy? Join me for a technical deep dive with tools and suggestions.

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