🌐 Edition One: You Can’t Manage AI if You’re Not Measuring the Data

The #1 mistake leaders make when scaling data and AI initiatives.

Welcome to The Practical Architect. My name is Gary Cronin, and I’ve spent over 25 years in the data trenches, working with organizations like TCS, Grant Thornton, IBM, Accenture, and a mix of ambitious startups. From large-scale successes to the hands-on failures that taught me even more, I’ve seen just about everything when it comes to data strategy and engineering.

Here’s what I’ve learned: Most people are missing the point when it comes to data. Too often, data is confused with technology. The conversation veers into the weeds of configuration, specific tools, and endless tech options, all of which are just means to an end. The real value of data doesn’t come from the tech itself, but from building a data-first foundation that empowers teams to manage and measure what truly matters.

In this space, I want to offer something different. I’m not here to push the latest tool or get bogged down in technical specifics. Instead, I’ll be sharing data best practices that actually work—the methods that reduce stress, simplify processes, and make data a true asset to the business. Think of this as a toolkit filled with frameworks, principles, and solution accelerators that you can apply across any platform or tech stack.

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Common Pitfalls in Data Strategy: Start with the Right Mindset

The biggest pitfall I’ve seen across countless projects is the tendency to think of data as just another tech asset. Companies pour money into the latest tools, hoping they’ll transform data into insights, but without a solid strategy, those tools are underutilized at best.

Data is like the backbone of an organization—it needs structure, consistency, and the right approach to stay strong. In my experience, successful data strategy starts with these three essentials:

1. Measurement is Key: What We Can’t Measure, We Can’t Manage

If your data strategy isn’t built around measurable metrics, you’re navigating in the dark. Many organizations rely on superficial metrics like record counts or dataset coverage, missing out on insights that could actually drive the business. In reality, a strong data solution should produce metrics about its own health, quality, usage, and cost—data that’s actionable, not just informative.

Imagine if every data solution in your organization had built-in metrics that tracked quality, operational efficiency, and cost control without needing manual input. This is the kind of robust, self-sustaining system we’ll discuss here—solutions that measure themselves so you can manage them.

2. Focus on Foundational Data Practices Before Advanced Tech

Much like learning to cook, data strategy starts with the basics before tackling complex, “holiday meal” challenges. Initially, focus on establishing solid data foundations—such as ensuring data quality, defining roles and accountability, and implementing scalable frameworks.

Over time, these basics can expand to include higher-level strategic initiatives. I’ll guide you through practical accelerators that build from the ground up, including examples like these:

• Estimate Calculators: Simple tools to help forecast data costs and resource needs.

• Business Data Strategy Templates: Frameworks to align data goals with business objectives.

• Test Plans and Quality Checks: Systems that ensure data reliability, accuracy, and consistency.

• And many others….

Think of these as the everyday tools that keep your data ecosystem running smoothly, without jumping straight into advanced, overly complex systems that aren’t ready for scale.

3. Prioritize Data Over Technology

In many ways, the data-first mindset is about cutting through the noise. Technology will always change, but the principles of good data management are constant. My approach is rooted in engineering best practices that can be adapted across any platform. Instead of letting technology drive your data practices, I’ll show you how to set up a framework that makes technology work for your data.

Here at The Practical Architect, we’ll cover data strategies that remain resilient and adaptable even as tech stacks evolve. I’ll introduce you to actionable frameworks that allow you to build a scalable, technology-agnostic data foundation that meets today’s needs and grows with tomorrow’s challenges.

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What’s Coming Up: Solution Accelerators and Real-World Frameworks

Throughout this series, I’ll be deep-diving into practical strategies and solution accelerators that simplify data management, make metrics intuitive, and establish a data foundation that your entire organization can rely on. Here’s a flavor of what’s to come:

• Business Data Strategy Frameworks: High-level templates for aligning data initiatives with business priorities.

• Data Quality & Governance Blueprints: How to set up automated quality checks and governance structures that scale.

• Usage Metrics and Health Checks: Tools to make sure your data solutions are working as intended, without relying on technical expertise for basic insights.

Functional Decompostion Example: Value Mettics to Board Level KPI

If you’re tired of chasing technology and want a data strategy that actually delivers, you’re in the right place. My goal with The Practical Architect is to give you practical, actionable insights that you can apply right now to reduce stress and drive real impact.

Let’s make data work for you—not the other way around.