Trench Tale #4 : The UX Behind BI Adoption

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:

  1. Tool-first thinking: Choosing a platform before understanding user needs.
  2. Feature-for-feature rebuilds: Replicating old reports exactly, even if they weren’t ideal to begin with.
  3. Ignoring data flow realities: Real-time needs vs. batch refresh capabilities.
  4. 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

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.

AI Adoption Without the Hype: Building the Right Roadmap (Part 3 of 3)

Introduction: Avoiding the AI Pitfalls 

The final part of our AI adoption series focuses on how to implement AI strategically. Strategic AI adoption needs to balance innovation with practicality, security, and staffing.

🔹 Part 1: AI Readiness – A Practical Guide for Strategic Adoption

🔹 Part 2: AI in Action – Practical Use Cases for Strategic Adoption

🔹 Part 3 (this post): AI Adoption Without the Hype – Building the Right Roadmap

Cloud vs. On-Prem: Does AI Require the Cloud?

When Cloud is Required: Large-scale AI workloads, federated learning, AI-powered SaaS.

When On-Prem Works Fine: Pre-trained ML models, localized analytics, security-sensitive industries.

AI Security: It’s More Than Just Privacy

🔹 Bias & Fairness – Avoiding discriminatory AI outcomes.

🔹 Model Explainability – Ensuring stakeholders understand AI-driven decisions.

🔹 Adversarial Attacks – Protecting AI from being manipulated.

AI Adoption: Aligning Investments with Business Priorities

Organizations often struggle to decide where to allocate AI resources. The key to successful AI adoption is aligning AI investments with business priorities, rather than chasing trends. A high-impact AI roadmap focuses on:

1️⃣ Quick Wins – Small AI projects that prove value fast (e.g., AI-assisted reporting in finance).

2️⃣ Strategic Growth – Scaling AI where it aligns with long-term business objectives (e.g., predictive analytics for customer behavior).

3️⃣ Risk Management – Implementing AI governance frameworks to manage compliance, ethics, and security risks.

Instead of treating AI as a separate initiative, businesses should integrate AI into their existing analytics and decision-making processes. This approach prevents AI projects from becoming siloed experiments and instead makes them scalable, sustainable drivers of business value.

Building an AI-Ready Workforce

AI adoption is not just about technology—it’s about having the right people and expertise to execute. Companies often struggle with whether to build AI capabilities in-house or rely on external expertise. Key considerations include:

Upskilling Internal Teams – Training analysts and engineers to use AI-driven tools and integrate AI insights into existing workflows.

Hiring AI Specialists – Recruiting data scientists and AI engineers for advanced AI/ML development where needed.

Leveraging Fractional AI Leadership – If an organization lacks a CDO, engaging a fractional CDO can serve as a bridge to develop an AI strategy until full-time leadership is in place.

Partnering with Data Analytics & AI Service Providers – Engaging experts who specialize in data analytics and AI integration ensures that AI-driven insights align with broader business intelligence and decision-making goals.

A hybrid approach — where organizations upskill internal teams while strategically leveraging external expertise — is often the most practical and cost-effective path forward.

From Strategy to Execution: Making AI Work for You

AI adoption isn’t just about technology—it’s about execution. Organizations that succeed don’t just explore AI; they integrate it into their existing analytics, decision-making, and business strategy. Now that you have a roadmap for AI readiness, real-world applications, and strategic adoption, how do you take the next step?

📌 Assess Your AI Maturity – Evaluate where your organization stands and identify gaps in AI readiness, data infrastructure, and analytics capabilities.

📌 Prioritize High-Impact AI Initiatives – Focus on quick wins that deliver measurable value while building a roadmap for long-term AI scalability.

📌 Develop Your AI Talent Strategy – Decide whether to upskill your team, hire AI talent, or leverage external AI & data analytics expertise to bridge skill gaps.

📌 Integrate AI Into Business Strategy – Ensure AI investments align with core business objectives rather than becoming siloed technical projects.

By taking a pragmatic, business-first approach, companies can move beyond the AI hype and achieve real, sustainable value. AI isn’t just about what’s possible—it’s about what’s practical, achievable, and aligned with your business goals.

📌 Read Part 1: AI Readiness – A Practical Guide for Strategic Adoption

📌 Read Part 2: AI in Action – Practical Use Cases for Strategic Adoption

AI in Action: Practical Use Cases for Strategic Adoption (Part 2 of 3)

Introduction: AI/ML Isn’t Just for Tech Giants 

Once the groundwork is set, companies can start leveraging AI — not for futuristic, abstract use cases, but for real business needs. This blog, part 2 of our series, outlines practical AI applications in data analytics across business functions that strategic organizations can start using today. The Maturity Stage framework we are using in the table below was introduced in Part 1 of this series.

🔹 Part 1: AI Readiness – A Practical Guide for Strategic Adoption

🔹 Part 2 (this post): AI in Action – Practical Use Cases for Strategic Adoption

🔹 Part 3: AI Adoption Without the Hype – Building the Right Roadmap

AI/ML Use Cases Across Business Functions

Business FunctionAI/ML Use Case for Data InsightsAI or ML?Maturity Stage
SalesML-driven forecasting models analyze historical pipeline data, seasonality, and external factors (e.g., economic trends) to predict revenue and deal closures.MLRun → Fly
AI evaluates win/loss rates and lead conversion patterns to identify which prospect attributes and sales behaviors drive success.AIRun
FinanceAI detects anomalies in financial transactions, predicts cash flow trends, and identifies cost-saving opportunities by analyzing spending patterns.AI/MLRun → Fly
ML-powered fraud detection models continuously learn from new transactions to spot fraudulent activities before they escalate.MLRun → Fly
Customer ServiceAI performs sentiment analysis on support tickets, call transcripts, and social media to uncover root causes of dissatisfaction.AIWalk → Run
ML predicts customer churn risk based on behavioral patterns and past interactions, helping teams proactively retain at-risk customers.MLRun
HRAI analyzes employee engagement survey responses and HR data to predict turnover risks and retention drivers.AI/MLWalk → Run
AI identifies skills gaps and training effectiveness by analyzing workforce performance data.AIWalk → Run
MarketingAI evaluates campaign performance, customer behavior, and attribution models to determine which channels drive the most conversions.AIRun
ML models predict customer lifetime value (CLV) by analyzing purchase history, engagement, and demographic factors.MLRun
Operations & Supply ChainAI analyzes historical logistics and inventory data to predict demand fluctuations and optimize procurement.AI/MLRun → Fly
ML-powered IoT data analysis detects patterns in equipment sensor data to predict failures and enable predictive maintenance.MLRun → Fly

Applying AI at the Right Time

Implementing AI in business functions isn’t about using the latest technology just for the sake of it. Companies should identify where AI aligns with their strategic goals and ensure that they are applying the right level of AI maturity for their current state. Just as a company wouldn’t implement machine learning models without clean data, they also shouldn’t push AI into areas where traditional analytics would be more effective.

Instead of aiming for full AI automation from day one, organizations should look at AI augmentation — where AI assists decision-makers without completely replacing human expertise. For example, sales teams can start with AI-assisted forecasting before moving to fully automated lead-scoring systems. Finance departments can first leverage fraud detection models to flag anomalies before shifting to AI-driven risk modeling. The key is to let AI enhance human decision-making rather than forcing AI-first strategies prematurely.

Next Steps: Building an AI Roadmap Without the Hype

Understanding what AI can do is only half the battle — implementing it effectively requires a roadmap.

📌 Check out Part 1: AI Readiness – A Practical Guide for Strategic Adoption

📌 Check out Part 3: AI Adoption Without the Hype – Building the Right Roadmap (coming soon)

AI Readiness: A Practical Guide for Strategic Adoption (Part 1 of 3)

Introduction: The AI Hype vs. Reality 

AI is everywhere, but most companies are struggling to move beyond the buzzwords. The truth is, AI is not a magic bullet, and jumping in without a solid data foundation leads to wasted time and money. AI and ML (Machine Learning) are often used interchangeably, but ML refers specifically to algorithms that learn from data patterns to make predictions. This three-part series will guide organizations through a practical, phased approach to AI adoption.

🔹 Part 1 (this post): AI Readiness – A Practical Guide for Strategic Adoption

🔹 Part 2: AI in Action – Practical Use Cases for Strategic Adoption

🔹 Part 3: AI Adoption Without the Hype – Building the Right Roadmap


The Crawl-Walk-Run-Fly Framework for AI Readiness

Many organizations feel pressure to implement AI quickly, fearing they’ll be left behind. However, AI adoption isn’t just about acquiring technology—it’s about ensuring that your organization is operationally and strategically prepared to derive real value from it. Rushing into AI without a strong foundation often leads to poor results, disillusionment, and wasted resources.

Instead of diving headfirst into AI/ML, companies should assess their AI readiness maturity level and take a phased approach:

Stage Focus Area AI/ML Readiness Key Steps
Crawl Data Architecture Health-Check Not Yet Ready for AI Identify & fix bad data structures, eliminate reporting inaccuracies
Walk Descriptive & Diagnostic Analytics Low – AI-Assisted Querying & Summarization ChatGPT-like AI for natural language queries, automated summaries, & data storytelling
Run Predictive Analytics Medium – ML for Forecasting ML for sales forecasting, anomaly detection, & customer segmentation
Fly Prescriptive & Automated Decision-Making High – AI/ML for Prescriptions AI-driven recommendations, process automation, & decision support

Step 1: Conduct a Data Architecture Health Check

Before AI can deliver insights, your data infrastructure must be sound. Many companies think they have a data warehouse — but poor architecture can introduce inaccuracies. A health check should cover:

✅ Data quality & governance – Ensuring accuracy, consistency, and proper governance across data sources.

Schema integrity & best practices – Ensuring star schema designs align with analytics needs, avoiding unnecessary complexity or performance bottlenecks.

Pipeline efficiency & scalability – Evaluating ETL/ELT processes for performance bottlenecks, latency, and future growth.

Measure definition & duplication – Identifying inconsistencies in KPI definitions and removing redundant calculations.

Security & compliance alignment – Ensuring adherence to regulatory standards and implementing proper access controls.

Data integration across silos – Enabling seamless interoperability between systems and reducing data fragmentation.

Step 2: AI Readiness Maturity Assessment

Companies need to evaluate where they stand today to define a roadmap forward:

✔️ Is our data structured & accessible enough for AI-driven insights?

✔️ Do we have the right reporting & analytics foundation?

✔️ What’s the business case for AI—where will it provide the most impact?

Laying the Right Foundation for AI Success

AI implementation is often derailed by a focus on tools rather than strategy. Companies need to shift their mindset from “How do we implement AI?” to “What outcomes do we want AI to drive?” Organizations that succeed in AI adoption start with clear, measurable business objectives before selecting any AI solutions.

For instance, a company struggling with fragmented customer data shouldn’t jump to AI-driven personalization tools before ensuring their data architecture supports accurate, consolidated customer insights. Similarly, a finance team interested in AI-based fraud detection must first establish reliable transaction monitoring systems. AI success starts with foundational improvements—not with cutting-edge algorithms alone.

Next Steps: Moving from Readiness to Real-World Use Cases

Once a company has a strong foundation, it’s time to explore how AI can be applied to real business challenges.

📌 Read Part 2: AI in Action – Practical Use Cases for Strategic Adoption 

📌 Read Part 3: AI Adoption Without the Hype – Building the Right Roadmap (coming soon)

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

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

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

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.

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

Achieving Near-Real-Time Data Warehousing on Azure with Datagize

Introduction

Today’s businesses demand instant insights from data. Traditional batch-driven data warehouses often create reporting lags of hours—or even days—making it challenging to make data-driven decisions in real time. At Datagize, we’ve built a near-real-time data warehousing architecture on Azure that delivers 3–5 second latencies from source databases to fact tables. In this blog, we’ll walk you through the key components of our solution and show how we tackled performance, reliability, and costs—without sacrificing maintainability or security.

Who Should Read This Post?

  • Chief Data Officers (CDOs), CIOs, and Directors of BI/DW: Looking to modernize data platforms or enable real-time analytics.
  • Data Architects and Enterprise Architects: Evaluating Azure services for high-speed data ingestion, transformation, and reporting.
  • BI/Data Warehouse Managers: Wanting to understand how near-real-time can be implemented at scale.

Key Takeaways

  • Rapid Delivery: Datagize’s prebuilt Python components make near-real-time data pipelines easier and faster to implement.
  • Scalable Azure Stack: Leveraging Azure SQL Database, Azure Functions, Event Hubs, and Stream Analytics for both low latency and resiliency.
  • Cost-Effective & Flexible: Pay-as-you-go consumption model plus strategic tuning to keep overhead manageable.

Architecture Overview

Below is a generic architecture diagram depicting the end-to-end data flow: 

  1. Azure SQL Database (with CDC) – The source system uses Change Data Capture (CDC) on tables that need real-time syncing.
  2. Azure Logic Apps – This lightweight workflow orchestrates the frequency (e.g., every 2 seconds) of calls to our first Python-based Azure Function.
  3. Azure Functions (CDC Reader) – A bespoke Python script pulls new or updated rows from the source database (using CDC or a sequence ID), then writes these events to Azure Event Hubs.
  4. Azure Event Hubs – Receives and temporarily buffers incoming data events.
  5. Azure Stream Analytics – Consumes events in near real-time and calls our second Azure Function.
  6. Azure Functions (Procedure Caller) – Another Python script that processes the events and calls a stored procedure in the target data warehouse.
  7. Azure SQL Data Warehouse – The final destination for fact and dimension tables, updated on a streaming basis, with specialized logic to handle asynchronous arrivals and early-arriving facts.
  8. Power BI – Consumes the latest data from the warehouse for dashboards, reports, and analytics.

High-Level Data Flow:

  1. Detect Changes: Azure SQL DB logs table changes via CDC.
  2. Pull Changes: An Azure Logic App triggers every 2 seconds, invoking the CDC Reader Function.
  3. Queue Events: The Function sends new/updated rows to Azure Event Hubs.
  4. Stream & Process: Azure Stream Analytics picks up the event stream and calls the second Python Function.
  5. Load Warehouse: The second Function executes a stored procedure in the Azure SQL Data Warehouse, updating facts and dimensions in near-real time.
  6. Analytics: Power BI taps into the warehouse for dashboards and reports.

Key Implementation Details

Change Data Capture Setup

We set up CDC at the table level in Azure SQL Database. This allows us to track inserts, updates, and deletes without intrusive changes to application logic. Alternatively, if a reliable timestamp or sequence column exists, that can be used as a fallback or simpler approach.

Bespoke Python Functions

  • CDC Reader Function
    • Pulls incremental changes from source tables (using CDC or a custom sequence/timestamp).
    • Packages these changes into event payloads and pushes them to Azure Event Hubs.
    • Intelligent error handling, batching, and incremental read logic are part of Datagize’s “secret sauce.”
  • Procedure Caller Function
    • Subscribes to streaming events from Azure Stream Analytics.
    • Batches or processes row-by-row transactions as needed.
    • Invokes a stored procedure in the Azure SQL Data Warehouse for the final load. The stored procedure manages fact/dimension updates, handles upserts, and addresses early-arriving facts.

Performance Tuning (High-Level)

Although the specifics of our tuning remain proprietary, we rely on standard best practices like creating the right indexes, partitioning large tables, and carefully managing concurrency. These tactics help maintain 3–5 second latencies while handling real-world data volumes.


Latency Achievements and Monitoring

We measured our 3–5 second end-to-end performance by inserting or updating small test batches (around 44 rows) in the source. Each major step (CDC detection, event publication, streaming, and data warehouse insertion) was timestamped. By comparing logs, we confirmed that data typically arrived in the warehouse in under 5 seconds—even under varying loads.

Monitoring

  • We used Azure Monitor and Application Insights to track function invocations, event processing times, and throughput.
  • Built-in Azure dashboards helped visualize average latency across the pipeline, alerting on potential bottlenecks.

Scalability and Resilience

Handling Spikes

Our architecture accounts for high-volume or bursty data through batching and concurrency. Azure Event Hubs and Stream Analytics can scale to handle large spikes, while the stored procedure approach in the data warehouse uses staging tables to handle large inserts efficiently.

Retries and Failures

Both Functions and Logic Apps can be configured with retry policies to handle transient errors. If an Azure Function or Event Hub experiences an outage, Azure’s built-in platform resilience ensures events aren’t lost and Functions can retry when systems recover.


Cost Management

Optimizing Azure Functions

Since Functions run on a consumption plan, cost is tied to execution time and frequency. By carefully setting polling intervals (in this case, every 2 seconds for near-real-time needs), we minimize unnecessary triggers. Also, optimizing the Python scripts reduces runtime and thus overall cost.

Other Services

  • Event Hubs & Stream Analytics: Typically adds about 5–20% overhead on top of core SQL costs. With efficient scaling and batch processing, these services remain relatively cost-effective.
  • Logic Apps: Minimal overhead given our lightweight approach (calls every 2 seconds).
  • Azure SQL Costs: The main expense usually comes from source and target Azure SQL DB environments. Our real-time pipeline approach adds only a manageable layer of overhead on top.

Security and Governance

Our Azure SQL environments use encryption at rest by default. We can also encrypt data in transit for end-to-end protection. Standard Azure security features—like network restrictions and IP whitelisting—can be applied to Functions, Event Hubs, and Stream Analytics. While not the focus of this article, robust data governance and role-based access control are critical for any production environment, especially when multiple teams need different levels of access.


Future Roadmap

We plan to explore the following enhancements:

  1. Delta Lakehouse with Databricks
    • Implementing a Delta Lake architecture can provide an advanced layer for structured and unstructured data.
    • With Databricks, we can unify batch and streaming data, enabling more complex transformations and near-real-time analytics on a broader data set.
  2. Further Cost Optimization
    • Exploring reserved capacity or other tiers for Event Hubs and Stream Analytics.
    • Tweaking polling intervals and function runtime to balance real-time needs with cost efficiency.
  3. Enhanced Security
    • Adding encryption in transit (TLS) for every service endpoint.
    • Exploring advanced firewall/network rules for each service.
  4. Edge Cases & Complex Scenarios
    • Continuous improvements to handle advanced use cases like multi-table transactions, referential integrity checks, and advanced data transformations.

Lessons Learned

  • Preview Features: The built-in CDC (Preview) feature in Azure Data Factory (ADF) has cost inefficiencies and throttling limitations.
  • Polling Intervals: Balancing near-real-time needs with cost overhead can be tricky—finding the right frequency is key.
  • Proprietary Tuning: Our Python-based approach gave us more control and better performance than off-the-shelf solutions.

Conclusion

By combining Azure SQL CDC, Azure Functions, Event Hubs, and Stream Analytics with Datagize’s bespoke Python components, we’ve delivered a solution that enables near-real-time data warehousing with latencies as low as 3–5 seconds. This architecture proves that speed, flexibility, and cost-effectiveness can coexist with the right design choices.

Ready to transform your data platform? Contact Datagize to learn how we can accelerate your journey to near-real-time data analytics on Azure—and explore the possibilities of Delta Lakehouse and Microsoft Fabric in your environment.


About Datagize
Datagize specializes in building scalable, high-performance data solutions that drive actionable insights. Our team of experts has deep experience with cloud-native architectures, BI, analytics, and machine learning—empowering businesses to stay ahead in a data-driven world.