data trust issues in digital projects

📌 The Modern Data Quality Framework for BI Every company wants better dashboards, better insights, better AI. But very few stop to ask the one question that actually matters: Can we trust the data we’re using in the first place? Because the hard truth is this: Most data issues don’t come from tools. They come from unreliable foundations that nobody notices until something breaks in production. When I look at the teams that consistently ship trustworthy data, there’s always the same pattern behind the scenes. Let me walk you through my reasoning. 1️⃣ 𝐓𝐡𝐞 5 𝐏𝐢𝐥𝐥𝐚𝐫𝐬 𝐀𝐫𝐞 𝐒𝐭𝐢𝐥𝐥 𝐭𝐡𝐞 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐏𝐨𝐢𝐧𝐭 Accuracy, completeness, consistency, timeliness, and validity. We all know them. But most teams still treat these as “definitions.” On the other hand, the best teams treat them as operational targets. It’s a completely different mindset. Accuracy isn’t “nice to have.” It’s whether your revenue aligns with reality. Completeness isn’t a rule. It’s whether you trust the KPI enough to act on it. Everything changes once you start thinking this way. 2️⃣ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐂𝐡𝐞𝐜𝐤𝐬 𝐌𝐚𝐤𝐞 𝐨𝐫 𝐁𝐫𝐞𝐚𝐤 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 This is where issues hide. I can’t count the number of times I’ve seen dashboards fail not because the model was wrong but because nobody noticed: → A column changed type → A pipeline skipped 2% of rows → A source table silently dropped a field → A null explosion went undetected for weeks This layer is invisible to most of the business, yet it’s the one that protects trust. If you don’t have anomaly detection or CI/CD tests, you’re relying on luck. And luck is not a data strategy. 3️⃣ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐌𝐚𝐤𝐞𝐬 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐖𝐨𝐫𝐤 Data catalogs, lineage, ownership, contracts. People talk about them like buzzwords, but the impact is very real. Lineage isn’t a diagram. It’s how you debug issues in minutes instead of days. Contracts aren’t bureaucracy. They’re how producers guarantee stability for downstream teams. Stewardship isn’t a title. It’s accountability. What I’ve learned from my experience is simple: When governance is strong, you don’t spend your life firefighting. 4️⃣ 𝐀𝐭 𝐭𝐡𝐞 𝐂𝐞𝐧𝐭𝐞𝐫 𝐨𝐟 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠: 𝐃𝐚𝐭𝐚 𝐓𝐫𝐮𝐬𝐭 This is the part people underestimate. Trust is not something you “announce” on a slide. It’s something you earn, build, and protect over time. It shows up in adoption. It shows up in business confidence. It shows up in how quickly you can respond when an anomaly hits. Trust is the real KPI. And when it’s strong, everything else becomes easier. Executives stop asking "where did this number come from." Why does this matter so much? Because a lot of companies are scaling GenAI without first fixing data quality. And when AI learns from unreliable data, it becomes unreliable itself. If you want to improve decision-making, data quality is not a side topic. Everything else is built on top of it.

Building Trust Between Data Producers and Data Consumers at Scale Trust does not scale with your data platform. But most organizations assume it does. Data moves faster than people. And decisions depend on data you did not produce. So let me ask you: When a critical dataset changes upstream, who is accountable for the decisions that break downstream? In most enterprises, the answer is unclear. And that is where friction starts. At scale, you are not managing datasets. You are managing dependencies across teams with different incentives, priorities, and timelines. That complexity is where trust erodes. A global retailer saw this play out. They spent 18 months building a customer lifetime value model. Strong analytics. Well validated. Then a merchandising system update changed the transaction data structure. No alert. No coordination. Three core features became invalid overnight. The model didn't fail. The relationship between producer and consumer was never defined. ➜ Trust in data is not a downstream validation problem. ➜ It is an upstream accountability design. That distinction is where most data strategies fall short. Organizations invest heavily in visibility. But visibility is not the same as trust. You can see the data and still not trust it. You see it in patterns like: ➞ Producers optimized for system performance, not downstream reliability ➞ Consumers inheriting data they cannot influence or enforce ➞ Schema changes communicated locally, not across dependencies ➞ Data quality measured in isolation from business impact The result is predictable. ➞ Teams spend more time validating than building ➞ AI initiatives slow down under repeated scrutiny ➞ Decisions are made with hesitation or hidden doubt And over time, confidence declines. Not because the data is always wrong. Because no one can confidently say it will be right tomorrow. The shift required is structural. ➞ Producers must know who depends on their data and why it matters ➞ Consumers must be informed of changes before they feel the impact ➞ Quality metrics must reflect decision impact, not system health ➞ Accountability must exist on both sides of the relationship Without that, trust remains accidental. And accidental trust does not scale. Data does not become trusted when it is consumed. It becomes trusted when accountability is designed at creation. This is not about better tooling. It is about aligning ownership with the decisions data enables. Organizations that do this well move differently. Less validation. Faster deployment. Higher confidence in action. Because trust is not rebuilt every time. It is built once, structurally. Follow Arun Gamidi for data, AI, and the leadership decisions that shape real outcomes.

Can You Trust Your Data the Way You Trust Your Best Team Member? Do you know the feeling when you walk into a meeting and rely on that colleague who always has the correct information? You trust them to steer the conversation, to answer tough questions, and to keep everyone on track. What if data could be the same way—reliable, trustworthy, always there when you need it? In business, we often talk about data being "the new oil," but let’s be honest: without proper management, it’s more like a messy garage full of random bits and pieces. It’s easy to forget how essential data trust is until something goes wrong—decisions are based on faulty numbers, reports are incomplete, and suddenly, you’re stuck cleaning up a mess. So, how do we ensure data is as trustworthy as that colleague you rely on? It starts with building a solid foundation through these nine pillars: ➤ Master Data Management (MDM): Consider MDM the colleague who always keeps the big picture in check, ensuring everything aligns and everyone is on the same page.     ➤ Reference Data Management (RDM): Have you ever been in a meeting where everyone uses a different term for the same thing? RDM removes the confusion by standardising key data categories across your business. ➤ Metadata Management: Metadata is like the notes and context we make on a project. It tracks how, when, and why decisions were made, so you can always refer to them later.     ➤ Data Catalog: Imagine a digital filing cabinet that’s not only organised but searchable, easy to navigate, and quick to find exactly what you need.     ➤ Data Lineage: This is your project’s timeline, tracking each step of the data’s journey so you always know where it has been and is going.     ➤ Data Versioning: Data evolves as we update project plans. Versioning keeps track of every change so you can revisit previous versions or understand shifts when needed.     ➤ Data Provenance: Provenance is the backstory—understanding where your data originated helps you assess its trustworthiness and quality.     ➤ Data Lifecycle Management: Data doesn’t last forever, just like projects have deadlines. Lifecycle management ensures your data is used and protected appropriately throughout its life.     ➤ Data Profiling: Consider profiling a health check for your data, spotting potential errors or inconsistencies before they affect business decisions. When we get these pillars right, data goes from being just a tool to being a trusted ally—one you can count on to help make decisions, drive strategies, and ultimately support growth. So, what pillar would you focus on to make your data more trustworthy? Cheers! Deepak Bhardwaj

𝗙𝗶𝘅 𝘁𝗿𝘂𝘀𝘁 𝗳𝗶𝗿𝘀𝘁, 𝗻𝗼𝘁 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲. A new Head of Data walks in. 𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟵𝟬 𝗱𝗮𝘆𝘀 𝗮𝗿𝗲 𝗮 𝘁𝗲𝘀𝘁. Many start with dashboards, pipelines, and plans. They rebuild what’s broken and expect trust to follow. 𝗕𝘂𝘁, 𝗺𝗼𝘀𝘁 𝗳𝗮𝗶𝗹. They forget that trust, not tools, is the real foundation. You can fix every schema and still have leaders asking, “Why are we still in this mess?” 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲, 𝗗𝗼𝗻’𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿. Meet every key person. Ask what data they trust. Listen to real pain, not just reports. Find your “data superusers.” See where data dies before it reaches the decision. 𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗔𝗹𝗶𝗴𝗻 𝗮𝗻𝗱 𝗗𝗲𝘀𝗶𝗴𝗻. Prioritize quick wins. Rank by impact, complexity, reach, and risk. Set clear ownership for metrics. Share updates every week. 𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗣𝗿𝗼𝗼𝗳, 𝗡𝗼𝘁 𝗣𝗿𝗼𝗺𝗶𝘀𝗲𝘀. Pick the highest priority. Deliver one visible win in 30-45 days. Align on definitions so everyone speaks the same language. Over communicate wins and issues. 𝗔𝘃𝗼𝗶𝗱 𝘁𝗵𝗲𝘀𝗲 𝘁𝗿𝗮𝗽𝘀: • Don’t rush to buy new tools. • Don’t rebuild dashboards before fixing trust. • Don’t promise AI if you have ten definitions of revenue. The first 90 days decide if data drives growth or stays a reporting chore. 𝗜𝗳 𝘆𝗼𝘂𝗿 𝗖𝗙𝗢 𝘀𝘁𝗶𝗹𝗹 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗯𝘆 𝗗𝗮𝘆 𝟵𝟬, 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. Trust comes first. Visible wins come next. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘀𝘁𝗼𝗽 𝗯𝗲𝗶𝗻𝗴 “𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗽𝗲𝗿𝘀𝗼𝗻” 𝗮𝗻𝗱 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘄𝗵𝗼 𝗺𝗮𝗸𝗲𝘀 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀?

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

Design of Experiments only pays off when your data is trustworthy, connected, and ready to analyze.     Most teams don’t have a data problem. They have a context problem. Experiments cross people, sites, instruments and time, yet the data arrives fragmented. That invites errors, slows tech transfer and forces your scientists to clean data instead of learning from it.     What’s worked across complex pipelines is building a digital backbone that keeps process context attached to every sample and step. In practice, that looks like process-centric workflows, versioning of methods and materials, automatic sample IDs and lineage, QC checks against specs, and instant creation of analysis-ready data frames. When process changes, the data structure updates with it, so your DoE stays intact and computable.     One line from my notes for leaders: aim for FAIR by design. Data should be findable, accessible, interoperable and reusable as it’s collected, not after the fact. When teams can capture experiment context, aggregate instrument and manual inputs, join data across unit operations and run real-time visualization or ML, throughput rises and transfer friction drops. This approach has shown time-to-market reductions, screening throughput increases, and major cuts in data prep effort.     In regulated work, don’t forget the guardrails. Audit trails, electronic signatures for completed experiments, and role-based access keep governance tight while letting collaborators contribute. APIs and SQL access matter too, because DoE is strongest when it connects to your analytics stack and master data.     Try this: pick one high-variance process, map the workflow end-to-end, assign permanent IDs to samples, and enforce QC ranges at data entry. Then push the resulting data frame into your DoE analysis. You’ll see clearer signals and faster iteration. 

Trust doesn’t disappear all at once. It erodes in small moments. → A report gets ignored. → A number gets questioned. → A decision gets made without the data. Nothing dramatic happens. But over time, teams start to hesitate. They double-check instead of acting. They debate instead of deciding. They rely on side spreadsheets “just to be safe.” That’s when you know the system has a problem. Not in performance. In credibility. Most teams try to fix this by improving pipelines or adding checks. But trust isn’t rebuilt through more output. It’s rebuilt when 1. Numbers stay consistent across teams 2. Data clearly support a decision 3. Issues are visible before they cause damage 4. Ownership is obvious 5. Outcomes are tracked, not assumed That’s what turns data from something people question into something they rely on. In this post, I break down where trust usually breaks first. Follow Reeves Smith for practical frameworks that help teams rebuild confidence in their data.