data trust issues in digital projects

If You Can't Trust Your Data, You Can't Trust Your Decisions. 𝗕𝗮𝗱 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲—𝗮𝗻𝗱 𝗶𝘁'𝘀 𝗰𝗼𝘀𝘁𝗹𝘆. Yet, many businesses don't realise the damage until too late. 🔴 𝗙𝗹𝗮𝘄𝗲𝗱 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁𝘀? Expect dire forecasts and wasted budgets. 🔴 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗿𝗲𝗰𝗼𝗿𝗱𝘀? Say goodbye to personalisation and marketing ROI. 🔴 𝗜𝗻𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗱𝗮𝘁𝗮? Prepare for delays, inefficiencies, and lost revenue. 𝘗𝘰𝘰𝘳 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘪𝘴𝘯'𝘵 𝘫𝘶𝘴𝘵 𝘢𝘯 𝘐𝘛 𝘪𝘴𝘴𝘶𝘦—𝘪𝘵'𝘴 𝘢 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘱𝘳𝘰𝘣𝘭𝘦𝘮. ❯ 𝑻𝒉𝒆 𝑺𝒊𝒙 𝑫𝒊𝒎𝒆𝒏𝒔𝒊𝒐𝒏𝒔 𝒐𝒇 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 To drive real impact, businesses must ensure their data is: ✓ 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 – Reflects reality to prevent bad decisions. ✓ 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 – No missing values that disrupt operations. ✓ 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 – Uniform across systems for reliable insights. ✓ 𝗧𝗶𝗺𝗲𝗹𝘆 – Up to date when you need it most. ✓ 𝗩𝗮𝗹𝗶𝗱 – Follows required formats, reducing compliance risks. ✓ 𝗨𝗻𝗶𝗾𝘂𝗲 – No duplicates or redundant records that waste resources. ❯ 𝑯𝒐𝒘 𝒕𝒐 𝑻𝒖𝒓𝒏 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 𝒊𝒏𝒕𝒐 𝒂 𝑪𝒐𝒎𝒑𝒆𝒕𝒊𝒕𝒊𝒗𝒆 𝑨𝒅𝒗𝒂𝒏𝒕𝒂𝒈𝒆 Rather than fixing insufficient data after the fact, organisations must 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 it: ✓ 𝗠𝗮𝗸𝗲 𝗘𝘃𝗲𝗿𝘆 𝗧𝗲𝗮𝗺 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲 – Data quality isn't just IT's job. ✓ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 – Proactive monitoring and correction reduce costly errors. ✓ 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Identify issues before they impact operations. ✓ 𝗧𝗶𝗲 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 – Measure the impact on revenue, cost, and risk. ✓ 𝗘𝗺𝗯𝗲𝗱 𝗮 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 – Treat quality as a mindset, not a project. ❯ 𝑯𝒐𝒘 𝑫𝒐 𝒀𝒐𝒖 𝑴𝒆𝒂𝒔𝒖𝒓𝒆 𝑺𝒖𝒄𝒄𝒆𝒔𝒔? The true test of data quality lies in outcomes: ✓ 𝗙𝗲𝘄𝗲𝗿 𝗲𝗿𝗿𝗼𝗿𝘀 → Higher operational efficiency ✓ 𝗙𝗮𝘀𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 → Reduced delays and disruptions ✓ 𝗟𝗼𝘄𝗲𝗿 𝗰𝗼𝘀𝘁𝘀 → Savings from automated data quality checks ✓ 𝗛𝗮𝗽𝗽𝗶𝗲𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 → Higher CSAT & NPS scores ✓ 𝗦𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 → Lower regulatory risks 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗱𝗮𝘁𝗮 𝗱𝗿𝗶𝘃𝗲𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. 𝗣𝗼𝗼𝗿 𝗱𝗮𝘁𝗮 𝗱𝗲𝘀𝘁𝗿𝗼𝘆𝘀 𝘁𝗵𝗲𝗺.

At its core, data quality is an issue of trust. As organizations scale their data operations, maintaining trust between stakeholders becomes critical to effective data governance. Three key stakeholders must align in any effective data governance framework: 1️⃣ Data consumers (analysts preparing dashboards, executives reviewing insights, and marketing teams relying on events to run campaigns) 2️⃣ Data producers (engineers instrumenting events in apps) 3️⃣ Data infrastructure teams (ones managing pipelines to move data from producers to consumers) Tools like RudderStack’s managed pipelines and data catalogs can help, but they can only go so far. Achieving true data quality depends on how these teams collaborate to build trust. Here's what we've learned working with sophisticated data teams: 🥇 Start with engineering best practices: Your data governance should mirror your engineering rigor. Version control (e.g. Git) for tracking plans, peer reviews for changes, and automated testing aren't just engineering concepts—they're foundations of reliable data. 🦾 Leverage automation: Manual processes are error-prone. Tools like RudderTyper help engineering teams maintain consistency by generating analytics library wrappers based on their tracking plans. This automation ensures events align with specifications while reducing the cognitive load of data governance. 🔗 Bridge the technical divide: Data governance can't succeed if technical and business teams operate in silos. Provide user-friendly interfaces for non-technical stakeholders to review and approve changes (e.g., they shouldn’t have to rely on Git pull requests). This isn't just about ease of use—it's about enabling true cross-functional data ownership. 👀 Track requests transparently: Changes requested by consumers (e.g., new events or properties) should be logged in a project management tool and referenced in commits. ‼️ Set circuit breakers and alerts: Infrastructure teams should implement circuit breakers for critical events to catch and resolve issues promptly. Use robust monitoring systems and alerting mechanisms to detect data anomalies in real time. ✅ Assign clear ownership: Clearly define who is responsible for events and pipelines, making it easy to address questions or issues. 📄Maintain documentation: Keep standardized, up-to-date documentation accessible to all stakeholders to ensure alignment. By bridging gaps and refining processes, we can enhance trust in data and unlock better outcomes for everyone involved. Organizations that get this right don't just improve their data quality–they transform data into a strategic asset. What are some best practices in data management that you’ve found most effective in building trust across your organization? #DataGovernance #Leadership #DataQuality #DataEngineering #RudderStack

We’re at a crossroads. AI is accelerating, but our ability to govern data responsibly isn’t keeping pace. The next big leap isn’t more AI, it’s TRUST - by design. Every week, I speak with organizations eager to “lead with AI,” convinced that more features or bigger models are the solution. But here’s the inconvenient truth: without strong foundations for data governance, all the AI in the world is just adding complexity, risk, confusion and tech debt. Real innovation doesn’t start with algorithms. It starts with clarity. It starts with accountability: • Do you know where your data lives, at every stage of its lifecycle? • Are roles and responsibilities clear, from leadership to frontline teams? • Are your processes standardized, repeatable, and provable? • When you deploy AI, can you explain its decisions, to your users, your partners, and regulators? • Are your third parties held to the same high standards as your internal teams? • Is compliance an afterthought, or is it embedded by design? This is the moment for Responsible Data Governance (RDG™), the standard created by XRSI to transform TRUST from a buzzword into an operational reality. RDG™ isn’t about compliance checklists or marketing theater. It’s a blueprint for leadership, resilience, and authentic accountability in a world defined by rapid change. Here’s my challenge to every leader: Before you chase the next big AI promise, ask: Are your data practices worthy of trust? Are you ready to certify it? not just say it? If your organization: 1. Operates #XR, #spatial computing or #digital #twins that interact with real-world user behavior; 2. Collects, generates, and/or processes personal, sensitive, or inferred data; 3. Deploys #AI / ML algorithms in decision-making, personalization, automation, or surveillance contexts; 4. If you want your customers, partners, and regulators to believe in your AI (not just take your word for it), now is the time to act. TRUST is the new competitive advantage. Let’s build it together. Message me to explore how RDG™ certification can help your organization cut through the noise and lead with confidence. Or visit www.xrsi.org/rdg to start your journey. The future of AI belongs to those who make trust a core capability - not just a slogan. Liam Coffey Ally Kaiser Radia Funna Asha Easton Amy Peck Alex Cahana, MD David W. Sime Paul Jones - MBA CMgr FCMI April Boyd-Noronha 🔐 SSAP, MBA 🥽 Luis Bravo Martins Monika Manolova, PhD Julia Scott Jaime Schwarz Joe Morgan, MD Divya Chander

🚨 Why Most Data Projects Fail Before They Even Start And what your SOW isn’t telling you… When we step in, the scenario is all too common: - The platform is up and running. - The dashboards are in place. - But the desired outcomes? Still elusive. What went wrong? A flawlessly executed data project with minimal business impact. The Statement of Work prioritized deliverables over decisions. Here’s what senior leaders often realize too late: 🔸 “We planned the platform, but overlooked user interaction.” The tools exist, but user adoption is lacking. 🔸 “We assumed data would seamlessly flow.” Yet, aspects like quality, governance, and ownership were neglected. 🔸 “We have a detailed architecture diagram.” But a roadmap to tangible value was missing. 🔸 “We reached the go-live milestone.” Yet, no celebration ensued—nothing truly changed. 💡 Opting for Snowflake, Databricks, Fabric, or Power BI isn’t the real risk. The true risk lies in assuming technology alone drives transformation. 🔍 If your SOW fails to: - Define success in business terms - Address data trust and access - Include change management - Align teams beyond IT Chances are, you're investing in infrastructure, not insights. At Royal Cyber, we've salvaged numerous projects that began with misguided approaches. If you're on the verge of finalizing a data or analytics SOW, let’s connect. Because success should not commence at go-live—it should stem from strategic planning. #DataLeadership #CIO #CDO #Analytics #Snowflake #Databricks #MicrosoftFabric #BI #DigitalTransformation #DataTrust #ModernDataStack #ExecutiveAlignment #RoyalCyber #SOW #ProjectRecovery