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.

Why Data Governance is More Critical Than Ever in an AI-Driven World

Introduction: AI is Accelerating the Need for Stronger Data Governance

The rapid rise of generative AI is increasing the urgency for organizations to strengthen their data governance frameworks. According to McKinsey’s 2024 State of AI report, 65% of organizations are now using generative AI, nearly double the percentage from just 10 months ago. Gartner predicts that by 2026, over 80% of enterprises will have integrated Gen AI into their operations.

Yet, as AI adoption soars, many organizations are relying on unstructured, inconsistent, and poorly governed data to feed these models. The result? Misinformed AI outputs, regulatory risks, and compounding data integrity issues. Companies must act now to assess their governance gaps and strengthen oversight before AI-driven insights lead to unreliable decisions.


Governance Gaps in an AI-Enabled Data Landscape

Generative AI models are only as good as the data they’re trained on. If organizations lack governance, they risk:

  • Inaccurate & Biased Outputs – Poor data quality leads to AI “hallucinations,” generating false or misleading results.
  • Security & Privacy RisksUnprotected PII and proprietary data may be exposed through AI interactions.
  • Compliance Failures – Regulations like GDPR, HIPAA, and the EU AI Act demand transparency, fairness, and accountability in AI applications.
  • Lack of Data Trust – Without governance, companies struggle to ensure data quality, leading to poor decision-making.

As George Firican, a leading data governance expert, puts it:

“Data governance and data privacy go hand in hand. Without strong governance, organizations can’t ensure compliance, security, or data integrity.”

Organizations must rapidly evaluate their governance gaps to prevent AI from compounding data-related risks instead of solving them.


The Risks of Poor Data Governance in an AI-Powered World

The consequences of weak governance in AI deployment are already becoming evident:

  • Data Integrity Issues: Poorly governed data leads to inaccurate reporting, inconsistent business metrics, and flawed AI-driven insights.
  • Regulatory Violations & Fines: Organizations lacking structured data governance risk non-compliance with regulations like GDPR, HIPAA, and the EU AI Act.
  • Data Silos & Inefficiencies: Without governance, organizations struggle to maintain centralized, accessible, and high-quality data for enterprise-wide AI initiatives.

McKinsey warns that without strong governance, AI-driven decision-making can lead to reputational damage, legal challenges, and loss of customer trust.


Data Governance’s Role in Compliance & Risk Management

Governments worldwide are introducing stricter regulations to ensure responsible AI adoption, but data governance is the foundation of compliance. Organizations can’t meet regulatory requirements without structured governance ensuring data quality, lineage, and security.

Key ways data governance supports compliance:

  • Data Transparency & Auditability – Strong governance ensures organizations can trace data lineage and maintain records to prove compliance.
  • Access Controls & Data Classification – Enforcing role-based access and securing sensitive data helps meet GDPR and HIPAA standards.
  • AI Training & Data Ethics – Organizations with clear governance policies can mitigate AI bias and prevent the misuse of sensitive data.
  • Regulatory Alignment – Governance frameworks help companies adapt to evolving AI-related regulations like the EU AI Act without disruption.

Without proactive data governance, companies risk reactive, last-minute compliance efforts that lead to rushed implementations, costly fines, and reputational damage.


How Organizations Can Strengthen Data Governance in an AI-Enabled World

To future-proof data governance, organizations must embed governance into their overall data strategies by:

🔹 Standardizing Data Definitions & Business Rules – Ensure enterprise-wide consistency in how key metrics and terms are defined.
🔹 Implementing Data Lineage & Cataloging – Establish a clear understanding of where data originates, how it flows, and who has access.
🔹 Improving Data Quality & Master Data Management – Deploy processes that continuously validate and cleanse data before it enters AI models.
🔹 Enhancing Access Control & Security Policies – Ensure sensitive data is classified correctly and governed with role-based access.
🔹 Aligning Governance with Regulatory Compliance – Adapt governance frameworks to meet GDPR, the EU AI Act, and evolving global data laws.


Conclusion: Data Governance is the Foundation for AI Success

The AI revolution is here—but without strong data governance, it creates more problems than solutions. As companies accelerate AI adoption, they must ensure their governance frameworks evolve just as quickly to maintain trust, accuracy, and compliance.

📩 Want to assess and strengthen your data governance? Let’s strategize, energize, and datagize your governance framework today.

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.

From Data to Decisions

Unlocking the Power of Real-Time Analytics

Introduction: The Need for Speed in Decision-Making

In today’s fast-paced business world, decisions can’t wait—yet many organizations still rely on delayed, batch-processed reporting that doesn’t reflect what’s happening right now.

Enter real-time analytics—a game-changer for companies that need immediate insights to respond to market shifts, customer behavior, and operational changes. Whether it’s financial reporting, shop floor optimization, or sales performance tracking, having up-to-the-minute data can mean the difference between proactive leadership and playing catch-up.


What Real-Time Analytics Really Means

Many companies believe they have real-time data, but in reality, they’re working with daily or hourly refreshes that don’t provide a live view.

True real-time analytics delivers insights as events happen, enabling organizations to act proactively instead of reactively. Here’s a quick comparison:

  • Batch Processing: Data is collected, processed, and stored at scheduled intervals (e.g., overnight or hourly refreshes). Useful but often outdated.
  • Real-Time Analytics: Data is continuously ingested and processed, ensuring decision-makers have the latest, most relevant insights.

Where Real-Time Analytics Makes an Impact

Real-time insights bridge the gap between data and action across industries:

Financial Accounting Software Firm – Customers needed instant updates to their financial reports. Real-time analytics enabled near-real-time financial dashboards, helping clients make faster, more informed financial decisions while ensuring compliance with regulations.

Manufacturing Firm – The company wanted large TV monitors on the shop floor displaying real-time raw material and subassembly updates. They also tracked individual and team productivity, using it as a motivational tool to drive performance and efficiency.

Banking Client – To drive friendly competition, a banking client desired real-time leaderboards in hallways displaying up-to-the-minute sales, underwriting, and loan processing data. The visibility would ultimately improve motivation and help leadership identify areas needing support in real time.


Key Challenges in Achieving Real-Time Analytics

As valuable as real-time analytics is, implementing it comes with challenges:

  • Data Integration & Latency – Ensuring low-latency streaming while integrating data from multiple sources is complex.
  • Scalability – Real-time processing demands scalable cloud architecture to handle high-speed, high-volume data.
  • Cost vs. Value – Not every business function requires real-time insights. Prioritizing the right use cases is key.

For a closer look at how to architect real-time analytics in the cloud, check out our deep dive on near-real-time Azure architecture.


How Datagize Helps Unlock Real-Time Analytics

Datagize specializes in helping organizations design, implement, and optimize real-time analytics solutions:

🔹 Assessing Readiness – Evaluating an organization’s infrastructure and identifying real-time gaps.

🔹 Designing Near-Real-Time Architectures – Leveraging cloud technologies, event-driven pipelines, and optimized data models.

🔹 Ensuring Data Governance & Quality – Because bad data at real-time speed is still bad data—trust and accuracy matter.

🔹 Delivering Actionable Dashboards – Enabling business leaders to see and act on insights instantly.


Conclusion: The Future of Decision-Making Is Now

The competitive edge belongs to those who move from stale reports to real-time insights. Organizations that embrace real-time analytics today will be better positioned for AI, automation, and future innovations.

📩 Is your organization ready to unlock the power of real-time decision-making? Let’s strategize, energize, and datagize your real-time analytics capabilities.

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.

Building Near-Real-Time Data Pipelines

Best Practices and Pitfalls

Introduction: Why Near-Real-Time Matters

In today’s data-driven world, businesses rely on timely insights to make informed decisions. But while real-time data processing is often the ideal, it can be costly, complex, and over-engineered for many use cases. Instead, near-real-time data pipelines offer a practical balance between speed, scalability, and cost-effectiveness—delivering insights within seconds or minutes rather than milliseconds.

However, building a reliable near-real-time architecture is not as simple as flipping a switch. Many organizations underestimate the complexities, from data ingestion bottlenecks to governance challenges and scaling issues. In this post, we’ll cover best practices, common pitfalls, and how to choose between off-the-shelf solutions and custom-built architectures.


Defining Near-Real-Time Data Pipelines

  • What does ‘near-real-time’ actually mean? Depending on the use case, near-real-time might mean latencies of 1-5 seconds or up to a few minutes—far faster than traditional batch processing but without the extreme infrastructure demands of true real-time.
  • How it differs from batch and real-time processing:
    • Batch Processing: Data is collected and processed at scheduled intervals (e.g., hourly, daily).
    • Near-Real-Time: Data is processed with minimal delay, often in small micro-batches or event-driven workflows.
    • Real-Time Processing: Data is processed instantly, requiring high-performance, low-latency infrastructure.
  • Common use cases:
    • Streaming analytics – Operational dashboards, fraud detection.
    • IoT monitoring – Smart devices, predictive maintenance.
    • Customer personalization – Real-time recommendations, targeted marketing.
    • Financial transaction monitoring – Fraud detection, risk scoring.

Best Practices for Building Scalable Near-Real-Time Pipelines

Choose the Right Architecture – Event-driven vs. micro-batch processing.

  • Tools like Kafka, Azure Event Hubs, AWS Kinesis for event streaming.
  • Azure Functions, Lambda, Databricks, Flink for processing near-real-time workloads.

Optimize Data Ingestion & Streaming – Minimize latency with efficient message queues and pub-sub models.
Ensure Data Quality & Schema Management – Implement real-time governance, data contracts, and schema enforcement.
Design for Fault Tolerance & Scalability – Implement retries, dead-letter queues, and distributed processing.
Monitor, Measure, and Optimize – Use observability tools like Datadog, Prometheus, OpenTelemetry to track latency and performance.


Pitfalls to Avoid

⚠️ Underestimating Latency Needs – Not all ‘real-time’ requirements are truly real-time. Align business needs with technical feasibility.

⚠️ Over-Engineering the Solution – True real-time processing can introduce unnecessary complexity and costs when near-real-time suffices.

⚠️ Ignoring Data Governance – Ensuring security, lineage, and regulatory compliance in streaming environments is critical.

⚠️ Failure to Scale Efficiently – Costs can spiral if pipelines aren’t designed to handle data spikes gracefully.


Build vs. Buy – Choosing the Right Approach

Organizations must decide between off-the-shelf solutions and custom-built frameworks based on their latency, scalability, and cost needs.

Off-the-Shelf Solutions (Buy)

  • Pros: Faster setup, managed scaling, built-in reliability.
  • Cons: Limited customization, vendor lock-in, and hidden constraints (e.g., throttling, scaling limits).
  • Example: Azure CDC (Preview) appeared promising for a client’s use case but had a throttling limitation that prevented reaching the required 3-5 second latency.

Custom Development (Build)

  • Pros: Optimized performance, tailored to business needs, avoids vendor-imposed constraints.
  • Cons: Requires expertise, ongoing maintenance, and higher initial investment.

Hybrid Approach

  • Many organizations find success combining off-the-shelf tools for ingestion and storage with custom development for processing and governance.

Conclusion: The Right Approach to Near-Real-Time Success

Building near-real-time pipelines is a balancing actspeed vs. complexity vs. cost. The right approach depends on your specific use case, latency requirements, and long-term scalability goals. Organizations that carefully evaluate their needs and leverage a mix of off-the-shelf tools and custom development will achieve the best results.

📩 Looking to optimize your near-real-time data pipelines? Let’s strategize, energize, and datagize your solution.