Before You Build: Start with Discovery

“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:

PhaseFocusKey Deliverables
1. Business ContextWhat decisions are we supporting?Use case inventory, success metrics
2. Data Profiling & QualityCan we trust the data? Where are the gaps?Data quality assessment, profiling summary
3. Governance & OwnershipWho owns what? Is there clarity and stewardship?RACI matrix, data domain model, lineage maps
4. Security & PrivacyAre we compliant and protected?Access controls, classification strategy
5. Metadata & LineageCan users trace what they’re seeing?Glossary, transformation lineage, semantic mapping
6. Architecture FitWhat will work in your environment?Stack recommendations aligned to your org’s maturity
7. Organizational ReadinessDo we have the skills and buy-in to succeed?Capability gap analysis, enablement roadmap
8. PrioritizationWhat should we build first, and why?Feasibility vs. value scoring, roadmap
9. MVP DesignHow 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

A Message from the Founder

Welcome to Datagize! 🎉

This moment has been a long time coming, and I couldn’t be more excited to finally share what we’ve been building. Datagize is more than a consulting firm—it’s a dream brought to life. The dream? Helping organizations like yours turn data into actionable insights, measurable results, and real business impact (and having some fun along the way).

Throughout my career, I’ve seen how data can be both an organization’s greatest asset—and its biggest headache. From scattered spreadsheets to “cloud confusion” (you know what I mean), too many businesses are stuck wrestling with their data instead of letting it work for them. That’s why I started Datagize: to cut through the complexity and make your data realized.

What We’re All About

At Datagize, we’re on a mission to empower organizations to make smarter, faster decisions with trusted, near-real-time insights. Our unique approach, which we call Strategize. Energize. Datagize., ensures that we deliver value at every stage of your data journey:

  • Strategize – We lay the groundwork with assessments, roadmaps, and strategies tailored to your goals.
  • Energize – We refine and validate those ideas, building the architecture and plan for scalable growth.
  • Datagize – We roll up our sleeves and make it happen with seamless implementation and ongoing support.

Basically, we take the stress out of data transformation and replace it with results (and maybe a happy dance or two).

Why Datagize?

Here’s the deal – we’re not just another consulting firm, and we’re certainly not about cookie-cutter solutions. We focus on:

  1. Pragmatic Solutions – No buzzword fluff. Just practical, effective strategies.
  2. Integrity – Your success is our North Star. We don’t play favorites with tools or vendors.
  3. Results – Because at the end of the day, that’s what matters most.

What’s Next?

As we launch Datagize, I can’t help but feel grateful for the support that’s brought us here and excited for what’s ahead. If you’re ready to turn your data into your greatest advantage, let’s chat.

📩 Seriously, reach out! Whether you’re tackling a big project or just wondering where to start, we’re here to help.

Let’s strategize, energize, and datagize together—and have some fun doing it.

Here’s to making data work for you! 🚀
Guy Wilnai
Founder & CEO, Datagize

The Truth About Data Assessments

Why ‘You Don’t Know What You Don’t Know’

Introduction: A Common Analytics Challenge

Many organizations are investing heavily in data analytics, confident that their insights are driving smarter business decisions. Yet, a closer look often reveals a different reality—misaligned metrics, inconsistent definitions, and eroding trust in reporting.

Teams across different business units define and interpret the same KPIs differently, leading to executives receiving “directionally correct” (but ultimately unreliable) reports. The result? Decision-makers second-guess the numbers, and teams spend more time debating data than acting on it.

This isn’t a failure of technology or effort—it’s simply that many companies don’t have a clear, unified assessment of their data landscape. That’s where a structured data assessment comes in.


The Metrics Mismatch: When Data Doesn’t Add Up

One of the most common issues we see in organizations is metric inconsistency. Take a simple KPI like “customer churn.” Does it mean customers who canceled a subscription? Customers who stopped engaging? Those who downgraded a service? Depending on who you ask—marketing, finance, or customer success—the answer (and the calculation) may be different.

Without a unified definition, teams unintentionally create data silos, and executives receive reports that don’t match up. When leadership starts questioning reports instead of trusting them, data-driven decision-making stalls.

A data assessment identifies these gaps, helping organizations standardize key metrics and ensure alignment across teams.


The Hidden Costs of Spreadsheet Overload

Even when data alignment issues are addressed, many companies still face another major hurdle—the sheer manual effort required to compile reports. We see this all the time: analysts with MBAs spending 80-90% of their time pulling data from different sources, manually merging spreadsheets, and reconciling inconsistencies.

This problem—what we call “spreadsheet hell”—not only wastes valuable talent but also delays insights and increases the risk of human error. A data assessment can pinpoint these inefficiencies and lay out a roadmap for automating reporting workflows, freeing up analysts to focus on high-value analysis instead of data wrangling.


How a Data Assessment Uncovers Opportunities

A Datagize Data Assessment is designed to provide a clear, actionable understanding of an organization’s data health. It evaluates:

  • Metric & Definition Alignment – Are business metrics standardized and consistently defined across teams?
  • Data Governance & Security – Is sensitive data properly classified, protected, and accessible only to the right people?
  • Organizational Readiness – Does your team have the right skills and processes in place to scale data initiatives effectively?
  • Cloud & Architecture Health – Is your infrastructure optimized for performance, scalability, and cost efficiency?
  • Data Maturity Benchmarking – Where does your organization stand on the analytics maturity curve (Gartner, TDWI, etc.)?

Through this process, organizations gain a clear picture of their data landscape—where they’re strong, where there are gaps, and what steps to take next.


Conclusion: You Can’t Optimize What You Can’t See

The reality is that most organizations are further along in their analytics journey than they think in some areas—and further behind in others. The key is knowing where to focus to maximize the value of your data investments.

A structured data assessment helps organizations move forward with confidence, eliminating blind spots and setting the foundation for a truly data-driven future. If you’re looking to streamline reporting, improve data trust, and accelerate insights, let’s start with a conversation.

📩 Interested in understanding your data landscape? Reach out to Datagize for a comprehensive Data Assessment today.

Leveraging the Data Wishlist

Introduction

Gathering meaningful business requirements can be one of the biggest challenges in any data-related project. IT teams often find themselves navigating unclear priorities, communication silos, and competing agendas. Yet, without clear alignment between business needs and technical solutions, projects can veer off track, wasting resources and missing the mark.

That’s where the Data Wishlist approach comes in. By asking a simple, open-ended question, you can break through barriers, uncover hidden needs, and spark meaningful conversations that lead to actionable insights.

The Challenge

Understanding Business Needs For many organizations, the gap between business stakeholders and IT teams is wide. Business teams may struggle to articulate their needs in technical terms, while IT teams are left guessing how to deliver value. Common roadblocks include:

  • Skill Gaps: IT teams sometimes face challenges in translating technical possibilities into business terms, while business teams may struggle to articulate their needs due to limited awareness of available solutions. This communication gap often leaves IT looking for explicit requirements while business teams wait for IT to propose feasible solutions. Bridging this gap requires a skilled facilitator who can uncover how the business operates, how they use or could use data, and what tools or insights they need to achieve their goals.
  • Cultural Barriers: Invisible walls between departments can stifle collaboration and trust.
  • Misaligned Priorities: Business and IT teams often operate under different assumptions about what success looks like. Bridging this gap requires a unique skill set—one that involves understanding how business teams perform their roles, how they use or would use data, what data they need for reporting, how they are measured (KPIs/goals), and more. A skilled requirements gatherer can then translate these needs into actionable plans, advocating effectively for both sides.

These challenges can lead to misaligned solutions, underutilized systems, and frustration on both sides. To move forward, you need a capability to foster better communication and understanding between these groups.

The Data Wishlist Approach

One of the simplest yet most effective techniques I’ve used is asking stakeholders this question:

“What’s on your data wishlist? Let’s start with three key items that could transform how you work.”

This question does several things:

  1. Encourages Open Thinking: It removes technical jargon and invites stakeholders to focus on outcomes rather than limitations.
  2. Uncovers Hidden Needs: Stakeholders often reveal pain points or aspirations they hadn’t previously articulated.
  3. Breaks Down Barriers: The conversational tone fosters trust and collaboration, even in politically charged environments.

Practical Examples Here’s how the Data Wishlist approach has worked in real-world scenarios:

Example 1: A Global Retailer’s Data Transformation Wishes During a project with a global retailer, I met with teams across the organization to understand their challenges. Their data wishlist items were ambitious and practical: closing the books faster, providing accurate actual vs. plan/budget vs. forecast reporting in any currency, establishing a common definition of terms, and improving KPIs and metrics. These wishes formed the foundation of a multi-phase data warehouse program, paired with a robust data governance initiative that established a governance team, charter, and processes. The outcome? Greater visibility, improved planning, reduced lead times, and significant cost savings.

Example 2: Finance Team’s Single Source of Truth In another instance, a finance department wished for a “single source of truth” for their operational reporting. This simple wish highlighted inconsistent data definitions and reporting tools across departments. We prioritized data governance initiatives, which ultimately saved hours of manual reconciliation and improved decision-making.

Example 3: Streamlining Procurement for an Oil and Gas Giant One of my early projects involved an oil and gas client with over $40 billion in annual procurement spend. Their procurement team’s wishes centered on reducing costs by providing data and reporting in a consumable format across their global procurement platform. Specifically, they sought a 1-2% reduction in procurement costs. This wish became the cornerstone of a multi-phase data mart project that streamlined procurement processes and delivered hundreds of millions in cost savings. It’s a reminder that addressing seemingly straightforward needs can yield transformative results.

From Wishes to Results

The power of the Data Wishlist approach doesn’t stop at gathering input. The next step is to:

  1. Correlate Responses: Identify common themes and align them with organizational goals.
  2. Assess Feasibility: Match wishes against existing IT capabilities and resource constraints.
  3. Create an Actionable Plan: Turn aspirations into concrete, prioritized steps for implementation.

This process not only builds understanding between business and IT but also creates a shared sense of ownership and direction.

Conclusion

Asking stakeholders about their data wishlist is more than a clever exercise. It’s a powerful way to uncover hidden needs, foster collaboration, and set the stage for successful outcomes. At Datagize, we specialize in bridging the gap between business and IT, helping organizations turn their wishes into results.

Ready to uncover your team’s hidden needs? Let’s talk. Schedule a consultation today and let us help you realize your data’s full potential.