Using Existing Data to Boost Trust

I violated data best practices to deliver a $40K ROI. (The client renewed. Here's why.) For 4 years, I've preached data best practices: Build proper data models. Minimize tech debt. Do it right the first time. Then reality hits. A mid-sized healthcare company hires us. They need a manual report automated. Fast. Your offer as a consultant is speed-centric. Their "source of truth" is 400 stored procedures written by a DBA who left 2 years ago. Zero documentation. Spaghetti SQL everywhere. 30+ Power BI reports querying directly off the transactional database. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱 𝘁𝗼 𝗱𝗼: Build a clean data warehouse from scratch. Proper dimensional modeling. Governed metrics. Best practices. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗶𝗱: Replicated their messy legacy logic in the cloud. Matched their numbers exactly—even the parts I knew were questionable. Automated the manual report in 6 weeks. Delivered the $40K ROI we guaranteed. 𝗪𝗵𝘆? Because many executives don't care about best practices. They care about results. Now. You don't get 3-6 months to "do it right." You get 6 weeks to prove you're worth keeping. 𝗧𝗵𝗲 𝘁𝗿𝘂𝘀𝘁-𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: If you show up and tell them their legacy logic is wrong, they won't trust you. If you replicate it perfectly first, they do. Once trust is built? Then you can challenge the legacy logic. Then you can propose the proper data model. Then you can start fixing the mess. But not before. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗾𝘂𝗶𝗰𝗸 𝘄𝗶𝗻𝘀 𝘁𝗵𝗮𝘁 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝘁𝗿𝘂𝘀𝘁 Automate one critical report. Match legacy numbers. Show ROI fast. 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝘁𝗵𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 "This works, but it creates tech debt. Here's the plan to fix it long-term." 𝗖𝗮𝗿𝘃𝗲 𝗼𝘂𝘁 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗲𝗯𝘂𝗶𝗹𝗱 Once trust is established, allocate hours to build the proper foundation. 𝗞𝗲𝗲𝗽 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝘄𝗵𝗶𝗹𝗲 𝘆𝗼𝘂 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 Don't stop showing ROI while you refactor. Balance both. 𝗧𝗟;𝗗𝗥: Best practices are the North Star. But speed to value is survival. Deliver quick wins. Build trust. Then improve the foundation. Perfection kills consulting businesses. Progress builds them. Agree or Disagree? P.S. - Full breakdown of how to balance speed vs. best practices in this week's newsletter. Link in comments. 👇 ♻️ Share this if you've ever had to choose between doing it "right" and doing it "fast." Follow me for real talk on what data consulting actually looks like in the wild.

📌 Data Governance 101 for BI Teams (How to Build Trust Without the Bureaucracy) Most companies don’t need an enterprise-grade data governance policy with 50 pages of rules and acronyms no one will ever read. They just need one thing: trust in their dashboards. Because the real problem isn’t the lack of data. It’s usually the lack of trust in it. And part of that confusion starts with the term itself. Data Governance is usually a vague phrase thrown around in meetings and strategy decks. Ask 10 people what it means, and you’ll get 12 different answers. Some think it’s about compliance. Others think it’s about permissions. And a few just assume it’s something IT should "handle." But at its core, governance isn’t about bureaucracy or control. It’s about clarity: → Knowing who owns what → How it’s defined → And whether it can be trusted when it matters most. You see this pattern everywhere. A marketing dashboard shows "Revenue" that doesn’t match what Finance is reporting. Sales metrics look inflated because duplicates slipped through the CRM. Operations teams export data manually just to double-check if Power BI is "right." And before anyone notices, confidence starts to fade. It’s a governance gap. And the good news? It doesn’t have to be complicated with endless documentation. It can be lean and practical but still effective. 1️⃣ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 Start by assigning clear owners for each data domain. When something breaks, you know exactly who’s responsible for fixing it. When KPIs need to be updated, you know who makes the call. 2️⃣ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐞 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧𝐬 This one might sound boring, but it’s the most underrated. If everyone defines KPIs differently, nothing else matters. When teams work from shared definitions, alignment happens naturally. You spend less time debating numbers and more time using them. Start simple. Keep a shared file, often called a Data Dictionary, listing each metric and its business definition. It doesn’t have to be perfect. It just needs to exist. 3️⃣ 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐀𝐜𝐜𝐞𝐬𝐬 Not everyone needs to see everything. That doesn’t mean you should hide data. It means you should curate it. Whether it’s for executives, managers, analysts, etc. A few clear access groups can reduce confusion and protect data integrity. Too much visibility without context can be just as dangerous as too little. 4️⃣ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 This is where trust is built or lost. If your dashboards show wrong numbers even once, users will remember it. It’s like credibility. You only get one chance. But it doesn’t have to be complicated. Start small: → Monitor refresh failures. → Detect duplicates. → Validate key fields like IDs or categories. These simple checks catch small issues before they break trust. And that’s how confidence in data slowly grows. If you get these four steps right, you’ll already be ahead of 90% of companies trying to become “data-driven.”

As usual, I listened on speaker phone to my dad’s bi-weekly appointment. His physician commented first on his quick wit, noting he must be feeling better as his sharp sense of humor was on point. She then walked through her opinion that we needed to make a big change in the treatment plan, carefully outlining which side effects he may and may not have based on his previous treatments. I smiled to myself thinking she really “gets” my dad. We immediately trusted her recommendation. It's easy to build a trusting patient relationship when someone is actively managing a disease, especially a longer-term diagnosis that requires frequent visits where the treatment team and patient (and family) have time to get to know one another. But most acute care is infrequent, and preventive care even less frequent. Yet, trust is equally important to motivate people to schedule and adhere to those care plans. Often, we question if we can mirror the trust built through human connection, digitally. Taking a step back, and reflecting on my experience above, it’s clear that the human connection underpinning trust is founded on feeling seen, heard, and known. My dad’s physician understood the side effects that bother him most; she knows how much he values humor and expert direction (versus lots of evidence and choices to make). We can offer this same experience – feeling seen and known – digitally, perhaps even more so. In doing so, we preserve our human resources to navigate complex patients. By using the vast data we have about healthcare consumers, we can curate unique digital experiences that grow trust because they are based on the patient’s values, barriers to care, service preferences, and more, establishing trust and effectively nudging patients to schedule cancer screenings, wellness visits, and follow up care.

Enforce Data Trust | Garantiza la confianza en los datos Throwing an expensive BI tool at dirty data is like putting a high-performance sports car engine into a vehicle with no steering wheel. The push for "real-time dashboards" has created a mountain of ungoverned data that leads to fast, expensive mistakes. This is the exact 3-step "Data Trust Tempo" I use to align leadership and establish a single source of truth. To prevent your organization from chasing "digital dust," you must establish a system where data definitions are treated with the same rigor as financial accounting. 𝗦𝘁𝗲𝗽 𝟭: Codify the "Business Dictionary" ➖ Lock your business leaders in a room. ➖ Define your top 5 to 10 KPIs (e.g., "Active Customer," "Net Profit Margin"). ➖ Do not write a single line of software code until everyone signs off on the literal, text-based definitions. 𝗦𝘁𝗲𝗽 𝟮: Appoint Business Data Stewards ➖ IT cannot be the custodian of data logic. ➖ Assign a business owner to each core metric. If the sales data is wrong, the VP of Sales must own the resolution process, not the database administrator. 𝗦𝘁𝗲𝗽 𝟯: Implement the Gatekeeper Protocol ➖ Block any new report or dashboard from being distributed to the executive suite unless it has been formally certified by your data stewards. ➖ If a metric hasn't been verified, it stays out of the boardroom. This system shifts your organization from reactive debating to proactive execution. What is the most heavily debated metric in your company’s board meetings? Let me know in the comments or contact Digital Transformation Strategist to help you.

A few weeks ago, I was working on a project where multiple data sources were supposed to align perfectly… but of course, they were not. Duplicate entries, missing fields, inconsistent formats — the classic data nightmare. 😅 Instead of rushing into analysis, I paused and reframed the problem: “How can I make this data reliable enough to trust the insights?” Here’s what I did step-by-step: 1️⃣ Created a clear data cleaning checklist identify, remove, and standardize. 2️⃣ Used SQL for quick validation queries and Excel for spot-checking anomalies. 3️⃣ Documented every assumption so the team understood what changed and why. The result? ✅ A dashboard with 100% accurate KPIs ✅ A 25% faster reporting process ✅ Stakeholders who finally trusted the data again Data analysis isn’t about fancy visuals or tools — it’s about building trust in the numbers first. If you’re working with data, slow down and fix the foundation before you visualize the outcome. What’s one challenge you’ve faced recently that taught you a valuable lesson? #dataanalyst

After 8+ years of experience working closely with dozens of data partners to integrate 40+ data sources into one platform, we've learned: 1. Invest in relationships Focus on trust before technology. Connect, feed people, show up, operate with integrity, apologize, fix it when you get it wrong, do excellent work...It's not complicated, but it takes time. 2. Educate on what's legally and technically possible Most concerns about data sharing are born from a place of confusion or lack of knowledge. When you aren't sure what's legal or ethical, then you are usually more risk averse. 3. Write strong and clear data sharing agreements Make agreements clear, solid, and simple. People feel better when they understand what the boundaries of the partnership look like. Strong agreements aren't an indication of a lack of faith. In fact, the opposite is true. The clearer your agreements, the more trust you can build with partners. 4. Show why it matters Don't just extract value. Deliver value back. Create win-wins. That always makes sharing more fun. What's your take? How can the anti-trafficking movement build strong, trust-based data sharing partnerships? #data #lighthouse #humantrafficking

Earning Users’ Trust with Quality When users interact with an AI-driven product, they may not see your data pipelines, but they definitely notice when the system outputs something that doesn’t make sense. Each unexpected error chips away at credibility. Conversely, consistently accurate, sensible recommendations gradually build lasting trust. The secret to winning that trust? Prioritize data quality above all else. How data quality fosters user confidence: Consistent performance: Reliable data inputs yield stable outputs. Users become comfortable knowing the AI rarely “goes rogue” with bizarre suggestions. Predictable behavior: High-quality data preserves known patterns. When the AI behaves predictably—reflecting real-world trends—users can rely on it for critical tasks. Transparent provenance: Even if users don’t dig into the data details, they appreciate knowing there’s a rigorous process behind the scenes. When you communicate your governance efforts—without overwhelming them—you reinforce trust. Error mitigation: When anomalies do appear, high-quality data pipelines often include fallback mechanisms (e.g., default rules, human-in-the-loop checks) that stop glaring mistakes from reaching end users. Consequences of ignoring data quality: User frustration: Imagine an e-commerce AI recommending out-of-stock products or the wrong sizes repeatedly. Frustration mounts quickly. Brand erosion: A few high-profile misfires can tarnish your company’s reputation. “AI that goes haywire” becomes a memorable tagline that sticks. Decreased adoption: Users who lose faith won’t invest time learning or relying on your platform. They revert to manual processes or competitor tools they perceive as more reliable. Building user trust isn’t a one-time effort; it’s continuous vigilance. Regularly audit your data sources, validate inputs, and refine processes so your AI outputs remain solid. Over time, this dedication to data quality cements confidence, turning skeptics into loyal advocates who believe in your product’s reliability.

There's this one underrated skill I figured every data professional should have. Stakeholder Engagement. The clients or business partners reach out to you with their concerns, and you give them data-backed solutions. Great! But do they actually use all of it? Maybe because they didn't fully understand your solution.   Maybe 2 out of 20 graphs would suffice for their requirements.   Or maybe you gave an orange when they asked for an apple. Anyhow, an unhappy user is equivalent to poor value and grading of your work. Here’s how we can do better: 1. Keep them in the loop from day 0 - even while understanding the requirements. Ask a lot of questions and make them feel heard. Trust starts with you stepping over to their side of the boat. 2. Explain the data layer - they are the business experts, and you are the data expert. Explaining what each field is and how it's retrieved helps users draft better and more realistic requirements. 3. Educate - explaining how you built that KPI really boosts clarity. Explain the logic, show them the process, and ask for feedback on how we could make this better together. 4. Connect beyond meetings - recurring weekly updates might feel enough, but constant communication - be it a call, quick text, or an ad-hoc in-person conversation - results in better alignment. This ensures that the final solution you deliver is not a surprise handover; instead, they'll feel it's their own project - co-built. Happy insights, y'all! #dataanalytics #datascience #stakeholders

🧭 “𝐍𝐨𝐧𝐞 𝐨𝐟 𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐞𝐪𝐮𝐚𝐥 - 𝐬𝐨𝐦𝐞 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐦𝐨𝐫𝐞 𝐞𝐪𝐮𝐚𝐥 𝐭𝐡𝐚𝐧 𝐨𝐭𝐡𝐞𝐫𝐬” A Chief Data Officer once told me during a workshop: “I thought all our definitions were aligned and consistent… until a dashboard told me otherwise.” We both laughed — but the truth hit hard. That moment summed up the data reality for so many organizations today. 💡Millions invested in modern platforms. 💡Fancy dashboards everywhere. 💡Yet… conflicting numbers, duplicated data, and endless debates about which report to trust. That’s when the real problem shows up — not a lack of data, but a lack of trust. ⚙️ The Turning Point: From Data Chaos to Data Confidence At DataGalaxy, we’ve learned that not all data deserves the same level of attention. Some data fuels decisions, innovation, and growth. Other data? It’s just noise. That’s why we help organizations take a pragmatic path — one that starts with identifying and certifying what truly matters: Critical Data Elements (CDEs). Here’s the simple, human logic behind it 👇 1️⃣ Identify your key data elements. Which data really drives business outcomes? 2️⃣ Score & prioritize. Focus your data quality and governance energy where it counts most. 3️⃣ Establish data contracts. Know who owns what, where data comes from, and how it’s used. 4️⃣ Certify your data products. Give them a visible seal of quality — trusted, traceable, and ready for self-service. Think of it as building your own Data Marketplace, where every product is transparent, reliable, and business-aligned. 🚀 The Impact: Trust That Scales When certification becomes part of your culture, everything changes. ✅ Decision-makers stop arguing over “which number is right.” ✅ Teams move faster because ownership is clear. ✅ Data becomes a trusted business asset, not an ongoing frustration. Certification isn’t about bureaucracy — it’s about clarity, confidence, and credibility. It’s about creating a world where business and data teams finally speak the same language. 🎯 Ready to Act? Start Here 👇 💥 Step 1: Identify your top 10 Critical Data Elements. 💥 Step 2: Define a lightweight certification playbook — focus on quick wins. 💥 Step 3: Share success stories early. Visibility builds momentum. Small, consistent actions will create an unstoppable movement toward trusted data. ✨ Final thought: In the age of AI and automation, trustworthy data isn’t a luxury — it’s your competitive advantage. Let’s make certified data the new standard for business excellence. That's what you can practically learn during our CDO Masterclass sessions hosted by Kash Mehdi and Laurent Dresse ☁ (𝐒𝐞𝐚𝐬𝐨𝐧 12 is already opened, registration link in comments) #DataGovernance #DataQuality #CDO #DataProducts #AI #Metadata #Leadership #DataCertification #DataGalaxy #DataTrust