The Datagize Way Archives

Before You Build: Start with Discovery

Posted on May 9, 2025May 12, 2025 by Guy Wilnai

“We’re deciding between Snowflake and Databricks — can you help us design the right architecture?”

That’s how a lot of our first calls begin. Prospects want to talk tech: lakehouse vs. warehouse, dbt vs. Informatica, Power BI vs. Looker. But the moment we ask about business needs, data trust, or source system readiness, the picture changes.

One client told us they’d already captured all their business requirements and wanted to start with design. Weeks later, we were running multi-day working sessions with users across geographies just to gather the foundational use cases that had been missed.

We spent more time on requirements and data profiling in that project than almost any other — and because we did, it succeeded.

Here’s the reality:

The first 15–20% of a data project determines whether it will succeed — with 100% accuracy.

Do discovery right? You deliver on-time, on-budget, with something that solves the real problem and gets adopted.

Skip it? You will fail. Guaranteed.

Discovery Is More Than Requirements

It’s about aligning business needs to the right technical solutions — grounded in a clear-eyed understanding of your organization’s data, culture, and constraints.

That’s why every Datagize engagement begins with our Discovery & Data Health Framework:

Phase	Focus	Key Deliverables
1. Business Context	What decisions are we supporting?	Use case inventory, success metrics
2. Data Profiling & Quality	Can we trust the data? Where are the gaps?	Data quality assessment, profiling summary
3. Governance & Ownership	Who owns what? Is there clarity and stewardship?	RACI matrix, data domain model, lineage maps
4. Security & Privacy	Are we compliant and protected?	Access controls, classification strategy
5. Metadata & Lineage	Can users trace what they’re seeing?	Glossary, transformation lineage, semantic mapping
6. Architecture Fit	What will work in your environment?	Stack recommendations aligned to your org’s maturity
7. Organizational Readiness	Do we have the skills and buy-in to succeed?	Capability gap analysis, enablement roadmap
8. Prioritization	What should we build first, and why?	Feasibility vs. value scoring, roadmap
9. MVP Design	How do we learn fast before scaling?	Prototype or working PoC with feedback loop

Tools Don’t Deliver Value. Clarity Does.

Architectural decisions — whether it’s Snowflake, Databricks, BigQuery, or Synapse — should come after you’ve clarified the problem you’re solving.

Too many teams burn time and budget chasing trends instead of solving the problems that matter.

With a strong discovery foundation, the right architecture emerges naturally.

Let’s Talk Before You Build

If you’re planning a data transformation, Datagize can help you lay the groundwork for success — and avoid the rework and regret that come from skipping discovery.

Schedule a discovery session

Trench Tale #4 : The UX Behind BI Adoption

Posted on April 22, 2025April 22, 2025 by Guy Wilnai

Modern BI tools promise a lot — faster insights, cleaner visuals, empowered users.

But here’s the truth from the trenches:

BI adoption fails when tools come before trust.

We’ve helped organizations untangle BI rollouts that looked good on paper but missed the mark in practice. And time after time, it wasn’t the tool’s fault. It was the approach.

When BI Modernization Stalls

At one State Fund, Tableau had been chosen as the flagship BI tool. On the surface, it made sense: sleek dashboards, interactive visuals, and wide industry adoption.

But within weeks, adoption began to slow. Reports that had been workhorses for years were suddenly out of reach — or worse, rebuilt in ways that didn’t serve the business.

Two examples stood out:

Contingent Commission: A regulatory report with complex conditional formatting, pixel-perfect layout, and business rule-based rendering. Tableau struggled to deliver both the structure and export fidelity required for compliance.
Agency Current Book: A report built with page sets, custom summary rows, and advanced crosstab logic — again, poorly suited to Tableau’s flat design model.

And then there was the OCR (Open Claims Review) — a report designed for live meetings with outside agents.

For OCR, the business needed to override data values temporarily, reflecting last-minute changes that wouldn’t hit the source system for up to an hour. This meant:

Users could write back changes in the source system.
The DW received a near-real-time push of just this data item.
Cognos picked it up immediately, ensuring the report reflected real-time adjustments.

Tableau, by contrast, required a full refresh to pick up those changes — not viable for these time-sensitive meetings.

From Feature List to Data Wishlist

We shifted the conversation from features to needs.

We led the team through a data wishlist process:

What absolutely needed to stay?
What could evolve?
What was worth building new?

With that in hand, we ran a gap analysis and helped architect a hybrid BI environment:

Cognos handled high-complexity reports, real-time overrides, and print-perfect outputs.
Tableau supported exploratory dashboards and visual summaries where its strengths shone.

It wasn’t about holding onto the past. It was about preserving trust — and designing BI around how people worked, not just how the tool was sold.

BI Migration Pitfalls We See Again and Again:

Tool-first thinking: Choosing a platform before understanding user needs.
Feature-for-feature rebuilds: Replicating old reports exactly, even if they weren’t ideal to begin with.
Ignoring data flow realities: Real-time needs vs. batch refresh capabilities.
One-size-fits-all UX: Assuming executives and analysts want the same thing.

Successful BI isn’t about adopting what’s next — it’s about elevating what works and building for what’s possible.

Lessons in UX from the Field

Across industries, we’ve seen the same patterns:

At a regional public university system, HR dashboards were redesigned around what leaders did with the data — not just how they wanted it to look.
At a global retail group, dashboards were tailored for regional exploration and executive summaries, respecting different users’ needs.
At a behavioral health provider, manual Excel processes were automated and brought back into the BI platform, improving time to insight.

Our UX Framework for BI Adoption

We’ve distilled years of experience into a simple model for BI success:

Datagize UX Triangle

Audience: Who is using the data? What do they need to know quickly?
Purpose: What action does this report drive? Storytelling or exploration?
Tool: Which platform supports this best — structurally and visually?

UX Drives Trust. Trust Drives Adoption.

The best BI projects don’t start with tools — they start with users.

If your dashboards aren’t landing, or your adoption is stalling, it’s time to step back. Rethink who you’re designing for, not just what you’re building.

A Personal Note from the Trenches

When I first started building BI solutions in the ‘90s, it was all about getting the reports out — making sure leadership had what they asked for.

But I learned quickly: unless the people on the ground trust the data and know how to use it, none of it matters.

Over the years, I’ve built systems for sales, supply chain, finance, marketing, manufacturing, claims, and more. The tools have changed — but the challenge hasn’t.

BI adoption isn’t a tech problem. It’s a UX problem.

And that’s where we come in.

Ready to Rethink Your BI UX?

If you’re about to choose a tool, or already mid-migration, take a step back.

We’re offering a limited number of free 60-minute BI Trust & UX Fit consults this month.
Let’s talk about how UX could save you months — and rebuild trust before it’s lost.

📩 Drop us a note

You bring the road.
We’ll bring the steering wheel.

Trench Tale #3: What Really Makes Data Governance Stick

Posted on April 10, 2025April 22, 2025 by Guy Wilnai

At a recent AASCIF Data Track Zoom session, I had the opportunity to co-present with a State Fund colleague on a topic we’ve both lived deeply: how to build data governance that actually works. Not the kind that looks good in policy docs or gets announced at a town hall, but the kind that sticks. The kind that changes the way organizations use, trust, and think about data.

What became immediately clear in the session was this: nearly everyone had tried something related to data governance. And nearly everyone had seen it struggle. It wasn’t for lack of effort, or even executive interest. The issue, as always, was in the execution.

The Illusion of Starting with Tools

Most governance efforts start from the wrong end of the map. Organizations buy a shiny new tool—a data catalog, a quality dashboard, a metadata harvester—and expect it to create structure. But tools reflect structure; they don’t create it.

At Datagize, we anchor governance on three forces: People, Process, and Technology. But what makes that model work is the sequence in which they show up. It starts with principles: clear statements about how your organization believes data should be used, protected, and trusted. Then come the structures—your charter, your roles, your escalation paths. Only after that scaffolding is in place should technology enter the picture.

Skipping this sequence leads to shelfware, not stewardship.

What Actually Worked

In one engagement with a State Fund, we led with principles and purpose. That meant defining beliefs about openness, data risk, and accountability. We then co-created a governance charter and formed a data governance committee composed of business and technical leaders across the organization. The right people were critical—not just role-fillers, but passionate, well-positioned individuals who could influence change.

From there, we prioritized high-impact data domains, focusing first on areas like claims, underwriting, and policyholder services where inconsistencies had real-world consequences. Only once those foundations were in place did we select a data cataloging tool to support the structure we had already built.

The result? Shared definitions. Certified datasets. Self-service reporting. And most importantly, business ownership of data.

Common Pitfalls to Avoid

Governance fails when:

IT tries to own it instead of enabling it
The organization tries to boil the ocean instead of starting small
ROI is assumed, not shown

The big question to ask is: Where is data causing rework, disputes, or decision delays? That’s where governance needs to start.

Also: remember that governance committee members have full-time jobs. Respect their time. Use working sessions to energize documentation and decision-making.

What Made It Stick

What worked in this engagement wasn’t magic. It was:

Executive sponsorship
Tangible early wins
Clear roles and decision rights
Cross-functional collaboration
Alignment among governance, analytics, and modernization

The client embedded governance into the day-to-day. Jira tickets now include governance metadata. Tableau dashboards are tied to certified datasets. Governance isn’t a side job—it’s part of the workflow.

And trust? That grew because the people using the data were the ones shaping its meaning.

What’s Next

Governance isn’t standing still. AI and automation are pushing data to its limits. Regulators are shifting from trusting policies to requiring proof. Cloud platforms expect you to arrive with a model, not build one on the fly.

Governance must evolve. That means:

Investing in maturity, not headcount
Integrating DG into analytics and AI planning
Embedding governance into the tools people already use
Upskilling your people to steward data with confidence

Our Approach

At Datagize, we built the DG Accelerator for organizations that need to move fast. It’s a five-week sprint with structure, working sessions, and real decisions—not just theory. But we also offer a Collaborative Roadmap for those who need to move together, aligning gradually and building buy-in along the way.

Both work. The key is setting the right pace.

Let’s build governance that lasts—not as a checkbox, but as a competitive advantage.

Want to dig deeper? Reach out to start your own Trench Tale.

Trench Tale #2: The 48-Hour War Room That Saved a Client

Posted on March 3, 2025March 6, 2025 by Guy Wilnai

Some consulting wins come from perfect execution. Others come from how you respond when things go wrong.

In consulting, some lessons come easy. Others are forged in high-stakes moments that test your integrity, resilience, and commitment to doing what’s right. Trench Tales is a blog series dedicated to sharing those defining experiences—the moments that shaped us, challenged us, and reinforced the core principles that guide Datagize today.

The year is 2000. The consulting firm I co-founded had built a custom sales commission system for a major client — before any of the big software vendors had even developed their own sales compensation modules. It handled sales territory assignments, overlays, and complex commission formulas. After the client was acquired, we adapted the system for their parent company.

One night, during a critical monthly commission run, something went wrong. The system wasn’t calculating commissions correctly. Time was running out. The client’s leadership was panicked. The executive of sales operations flew in from Canada to oversee the crisis firsthand.

I had just been diagnosed with pneumonia and was at home when the call came in. The IT Director was frantic. The system they had invested so much in was failing at the worst possible time. He wasn’t just asking for help — he was asking for our presence.

So I showed up.

The War Room

As soon as I arrived at the client’s office, I took charge and established a War Room. Within minutes, we had assembled a cross-functional response team — our consultants and client engineers, all focused on one goal: finding the root cause. The stakes were enormous. If we failed, it wouldn’t just damage our reputation — it could cost client executives their jobs.

For 48 hours, we lived in that War Room. We worked in teams, pushing through exhaustion. Some of the brightest minds in the industry were on that office floor, including a PhD software architect who later went on to design a revolutionary virtual keyboard. Yet, despite our collective expertise, the issue eluded us.

Still, we refused to fail.

After 40 straight hours of debugging, log analysis, and relentless testing, our architect spotted something — an anomaly buried deep in the system. A single point of failure. A fixable one.

We patched it. We tested it. It worked.

Just in time to complete the commission run and restore confidence in the system—and in us.

After the dust settled, the client executive pulled me aside. He told me he had never, in all his years professionally, seen a war room run so effectively. The way we coordinated efforts across teams, stayed focused under pressure, and executed with precision left a lasting impression. Our commitment impressed him more than anything technical we may have achieved.

Why This Matters to The Datagize Way

This wasn’t about fixing a system. It was about showing up when it mattered most.

This experience helped shape the ethos of Datagize — where Integrity, Client-Centricity, and Pragmatism aren’t just words. They are how we operate. We take ownership. We stand by our clients. We do whatever it takes to get the job done.

Not every consulting firm would have stayed in that War Room. We did. And that relentless commitment is what defines The Datagize Way.

Want to work with a team that doesn’t back down from challenges? Click one of the buttons below to connect with Datagize.

Trench Tale: The Cost of Doing the Right Thing

Posted on February 19, 2025 by Guy Wilnai

In consulting, some lessons come easy. Others are forged in high-stakes moments that test your integrity, resilience, and commitment to doing what’s right. Trench Tales is a new blog series dedicated to sharing those defining experiences—the moments that shaped us, challenged us, and reinforced the core principles that guide Datagize today.

The year is 1996, early in my executive career. The fledgling consulting company I co-founded had just convinced a Silicon Valley giant—let’s call them Bigco—that our expertise in decision support systems and data warehousing could help them get their sales reporting under control. We put together a crack team of industry veterans, including some who had built the world’s first major data warehouse. The project was scoped for eight weeks.

Four weeks in, the phone rings. It’s the client. The project is completely off track, and if we don’t turn it around immediately, our future with Bigco is dead in the water. The only option, he tells me, is to remove the project manager, roll up my sleeves, and restart from scratch.

I pull the team together and quickly realize the hard truth: they’re not just missing the mark—they don’t even understand what the client actually needs. Worse, the only way to course-correct is to extend the timeline and effectively double our original budget. The math is brutal: if we do the right thing, we’ll take a major financial hit—one that could sink our small firm.

But I knew one thing for certain: the right thing was the only option.

I convinced my partner to take the loss, stepped into the trenches, and spent the next ten weeks leading the team to deliver exactly what Bigco had asked for.

Looking back from 2025, I can say this: it was the biggest financial loss I’ve ever taken on a project. But that decision—to honor our commitment, no matter the cost—defined my consulting career. It set the tone for everything that followed. And in the end, it wasn’t a loss at all: instead of walking away from us, Bigco became one of our largest clients for the next decade, fueling our growth to 300 consultants.

👉 Some lessons cost you. Others define you.

This story isn’t just about the past—it’s about what drives us today. At Datagize, we believe that Integrity, Client-Centricity, and Pragmatism aren’t just words; they are the foundation of how we do business. The right path isn’t always the easiest or the most profitable in the short term, but it is the one that builds trust, strengthens relationships, and delivers long-term success.

Want to work with a team that puts principles first? Let’s connect.

The Datagize Way: A Commitment to Integrity and Impact

Posted on February 18, 2025February 18, 2025 by Guy Wilnai

At Datagize, we believe that great consulting isn’t just about technology or methodology—it’s about principles. Our approach is built on three guiding values: Integrity, Client-Centricity, and Pragmatism. These values have shaped our careers and continue to define how we operate today.

That’s why we’re launching The Datagize Way, a blog category dedicated to sharing insights, experiences, and lessons learned over decades in consulting. This space will highlight what it truly takes to build trust, drive impact, and lead with integrity in an evolving industry.

The Datagize Way will feature multiple recurring series, including Trench Tales, which will share real-world stories of challenges faced and lessons learned. But we’ll also explore broader themes—leadership, innovation, ethical decision-making, and strategies for sustainable success in data-driven consulting. Our focus is on providing pragmatic consulting solutions that work in real-world scenarios, ensuring that our client-centric approach delivers measurable success.

We believe that integrity in consulting is the foundation of strong, long-term relationships. By prioritizing ethical practices and putting client needs at the center, we create meaningful impact that extends beyond individual projects.

Our hope? That these stories and insights spark conversations and resonate with those who, like us, believe consulting should be about more than just billable hours. It should be about making a lasting difference.

Stay tuned for our first Trench Tale, where we’ll dive into a defining moment that shaped our consulting journey.

Want to talk about data strategy with a team that leads with integrity? Let’s connect.