Building Trust With Transparent Data Practices

How transparent should we be with our people insights? This is a question I hear from leaders all the time, and, like any good social scientist, my answer is: it depends. Let’s put aside the non-negotiables—those bound by law or confidentiality. Beyond that, there’s a wide spectrum of how companies handle sharing people data insights, and there’s no one-size-fits-all approach. Here are a few key things I always consider: 1️⃣ Data Source Matters: If employees are giving feedback through engagement surveys or focus groups, it’s crucial to share those insights back with them to increase trust, accountability, and future participation. 2️⃣ Know Your Audience: Even if you’re talking about the same metric, you should communicate about it differently to different audiences. Executives often want to know about the high-level trends and strategic insights while the company at large may want to hear about how it directly affects their day-to-day work. You can also consider sharing the information with leaders first, so they are equipped with extra context. 3️⃣ Consider Sensitivity: If the data touches on delicate issues—like potential layoffs, some diversity metrics, or areas where the company isn’t performing well—it’s essential to approach these topics with care. Consider the risks and who needs to know in order to act on the information. Transparency doesn’t mean sharing everything; it means sharing thoughtfully. 4️⃣ Look at Precedent: Consistency in communication builds credibility. Suddenly withholding information you’ve shared in the past can raise red flags. On the other hand, if transparency is new to your organization, you might start by sharing smaller insights and gradually build up to more comprehensive data. 5️⃣ Clarify the Purpose: Is it to inform, to spark action, or to inspire change? Being clear on this will help you decide how much to share, with whom, and how to frame it. 6️⃣ Anchor to Usefulness: A lot of what we study in People Analytics can be helpful for employees to know. How can managers increase psychological safety? How can employees get higher quality feedback from their colleagues? How can distributed teams collaborate best? Whenever possible, I like to share these learnings with employees so they can benefit from our data-informed wisdom. My default? Lean into transparency. In the absence of data, human nature is to make up a narrative—and the stories we create are often far worse than the truth. When appropriate, sharing our knowledge broadly can empower employees and leaders alike to work with a greater shared understanding of reality. Being transparent doesn’t mean sharing everything with everyone, but it does require being purposeful, considerate, and consistent about what you share.

"We're moving forward with another vendor." Every rep's nightmare sentence. I pressed for details. "Their approach felt more open. We actually knew what we were buying into." That stung. I'd shared: ••• Exhaustive feature documentation ••• Dozens of success stories   ••• Complete pricing breakdowns Where'd I go wrong? Days later, I got access to our competitor's sales process. The difference hit instantly: They didn't preach transparency. They lived it. Their follow-up wasn't an email avalanche. It was one collaborative hub where buyers could: ••• Monitor which stakeholders engaged with what ••• See their exact position in the evaluation journey ••• Find materials curated for their unique pain points ••• Manage internal distribution seamlessly My revelation: I was buried in PDFs. They were cultivating partnership. Next prospect, new approach: I built a shared workspace exposing EVERYTHING: → Which team members on our side viewed their data → Critical docs they'd missed → Realistic implementation expectations → Where we excel AND where we don't The buyer's response: "Finally, someone not playing games." Ink on paper in 10 days. Here's what's real: Today's buyers aren't starved for data. They're starved for authenticity. Yesterday's strategy: Bombard with polished assets that sidestep weaknesses. Tomorrow's strategy: Build transparent environments that tackle doubts directly. Your buyers know when something's off. Even when nothing is. Quit running sales like a shell game. Start running it like a glass house. You with me?

When I first started as a data analyst, I thought earning trust meant being right all the time. But over the years, I learned something much more important: Trust isn’t built from being perfect or from having all the answers. It’s actually built from clarity, consistency, and communication. The best analysts don’t just know the data. They know how to frame it, simplify it, and make others feel confident acting on it. That non-technical stakeholder you’re working with? They don’t care about which window function you used or all the details behind the 12 segments you analyzed. What matters to them is: a) Can they trust you and the data? b) Can they act on your insights with confidence? Here’s a list of 20 habits that will help you build trust with people beyond the numbers: 1. Ask why before asking what data do we have? 2. Anchor every analysis to a clear business question. 3. Clarify what “success” means before measuring anything. 4. Translate metrics into what they mean for the business. 5. Separate facts from interpretations - name both. 6. Write a one-sentence summary for every chart you make. 7. Check assumptions out loud with stakeholders early. 8. Track every decision made because of your analysis. 9. Add a “so what?” statement under every insight. 10. Choose simplicity over sophistication when explaining results. 11. Keep a running list of common stakeholder questions. 12. Document your data sources and what’s missing. 13. Reuse your best slides and phrasing to build consistency. 14. Create a personal “insight vault” of past wins and learnings. 15. Summarize meetings in 3 bullets: decision, data, next steps. 16. Flag uncertainty - it builds trust, not doubt. 17. Learn one new business concept for every technical skill. 18. Revisit your old analyses and ask, “Would I frame this differently now?” 19. Test if a non-analyst could follow your logic. 20. Always end with a question that moves the conversation forward. Which of these do you already practice? And which one do you want to strengthen next? PS: I write a free weekly newsletter for aspiring and early career analysts where I talk more in depth about leveraging communication and trust. Link is in the comments

🧭 “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

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

The trust economy is replacing the attention economy.✅ Marketers have long treated data as their superpower- the more you collect, the sharper your targeting. But as privacy laws evolve, that mindset is hitting a wall. New regulations are redrawing the boundaries of what’s fair, ethical, and legal in data use. Hyper-personalisation still matters. It drives relevance, loyalty, and conversion. Yet creating these experiences while respecting privacy has become the new balancing act. The line between helpful and invasive is thinner than ever. The smartest brands are already adapting. They’re moving from surveillance to service - collecting less, but using it better. They’re making consent experiences simple, data use transparent, and value exchange visible. Instead of chasing clicks, they’re building credibility. Here’s what that looks like in practice: 👉🏻 Audit every data point you collect. If it doesn’t add clear value to the customer, drop it. 👉🏻 Be upfront about how and why you use data. Transparency builds confidence. 👉🏻 Trade access for value - early previews, useful insights, or improved recommendations. Privacy is no longer just about compliance. It’s the foundation of modern marketing trust. The brands that will thrive aren’t those who know the most about their customers but those whose customers choose to share more with them. #futureofmarketing

Building Trust in Healthcare AI: A Conversation with Brian Anderson Had an energizing conversation with Brian Anderson, MD on the latest #TurnOnTheLights podcast. Brian's journey—from frustrated pediatrician battling clunky EHRs to Chief Digital Health Officer at MITRE—gives him a unique lens on what healthcare technology should actually do: help clinicians care for patients, not just optimize billing cycles. That spirit led to the Coalition for Healthcare AI (CHAI) The challenge? We didn't have consensus on what "good, responsible AI" actually means. Not at 50,000 feet—at the level of specificity that matters for implementation. So CHAI brought together 3,000+ organizations to define what good, responsible AI is. Here are the core principles: 🔹 Fairness – equitable performance across populations 🔹 Transparency – the foundation of trust (Brian's pick for most critical) 🔹 Robustness – reliable, consistent results 🔹 Safety – do no harm 🔹 Privacy – protect what matters most In the episode describes how these principles need to get applied to very specific use cases. Which means, the threshold for a sepsis prediction model is going to be different than for administrative tasks. That nuance matters. CHAI is now building an AI registry with "model cards"—think nutrition labels for AI. Independent evaluation. Transparent performance data. Why participate? Health systems have an obligation not to harm. Vendors benefit from independent validation. And together, we could very well create competitive markets that drive both quality up and costs down. As a bonus...Brian will take us into Operation Warp Speed (Trump administration initiative that created the COVID vaccines)...super interesting!! Watching private sector partners step up—figuring out how to keep people alive longer, coordinating therapeutic transport via Amazon aircraft. Shows what's possible when we align around a common mission! 🎧 Listen to the full conversation at #TurnOnTheLights wherever you get your podcasts! #HealthcareAI #DigitalHealth #HealthEquity #QualityImprovement

"Won't such transparency create problems?" This was the question posed to me by a leadership team years ago when I proposed publishing our annual increment process in detail – from performance ratings to its linkage the increment % to market corrections. The organization had never done this before, nor had they heard of others being so open. But my rationale was simple: if we're confident that our compensation practices are fair, objective, and the best we can do within our constraints, why hide them? We only hide things we're unsure of or lack authenticity in. This argument resonated. We went ahead and published the entire process, and the results were remarkable: ✅ Zero compensation grievances that year. ✅ Engagement scores on trust and transparency soared to all-time highs. ✅ The organization has continued this practice for over a decade. Transparency isn't just about openness; it's about building trust. When employees understand the 'why' behind decisions, it fosters a sense of fairness and respect. How do you drive trust and transparency in your organization? I'm eager to hear your thoughts and experiences. Feel free to connect if you'd like to explore how to implement similar practices in your workplace! #transparency #trust #compensation #HR #leadership #employeengagement #organizationalculture

Imagine buying a box of cereal, yogurt, or sauce, but none of them have nutrition labels. No ingredients, no context, no information. Would you trust what is inside and buy them anyway? Probably not.    Transparency influences trust in every part of our lives, including the AI we use each day. Knowing how a feature is built, what data it is trained on, and what safeguards guide its behavior helps people make informed and confident choices.    That is why we created the first version of the Autodesk AI Transparency Cards. Inspired by nutrition labels, the cards explain what the feature does, the model used, how it was trained, the protections in place, and the limitations to consider.    But information alone is not enough. Just as consumers learned to read nutrition labels, we also need education around AI. To support this, we published an e-book on Autodesk’s Trusted AI practices and added detailed explanations of each part of the Transparency Cards on the Autodesk Trust Center. We are proud of this first version, and we are already working on version 2 to make it even easier for customers to find the information they need.    Below is a closer look at one of our Transparency Cards. You can find more on: https://lnkd.in/gTkCBceP   What would make AI clearer and more trustworthy for you? Share your thoughts! 

I was just speaking with the L&D Leader of a multi-billion dollar business who shared their journey to securing the business data needed to prove L&D's impact, a common struggle for many of us. They’d been on both ends of the spectrum: the Fortune 500 company where a high-ranking person refused to share business data and their current role where stakeholders are willing to hand over the data. For L&D professionals, getting access to those business metrics is half the battle. Here is the strategic approach they used to build an indispensable L&D function: 1. Focus on the business's biggest pain points (quantified with data) They targeted major, quantifiable business risks. Their first focus was fixing a massive problem: Ridiculously high turnover in one of the business units. They were also intensely interested in attrition, seeing the correlation between how they were preparing people and the number of people leaving. 2. Deliver wins before asking for the keys They built trust by showing immediate, quantifiable value first, offering to help with no questions asked. This resulted in: - Increasing the production output of new starters by focusing more on the actual work during training - Then shaving weeks off of a multi-month training program for new starters due to greater focus on performance and impact and then asking whether there was a more efficient way of achieving the same results - Which all resulted in business partners sharing more data with them because they saw such a huge impact on their day-to-day work. 3. Mirror the metrics that matter Their team now formally aligns L&D goals with business-driven outcomes. They write goals based on the same business metrics their stakeholders use when meeting with their own teams. Their future goals include things like: - Reduce x amount of time in the classroom - See x amount of proficiency on calls - Achieve x amount of billing 4. Provide proactive visibility (report out constantly) They don't wait for stakeholders to ask for updates. They report out L&D's impact quarterly, transparently and proactively, putting it in the hands of stakeholders. This strategic visibility ensures L&D is never overlooked. This transformation has shifted L&D from a service line that could be cut to a strategic partner that the business says, "We can't live without you". There’s so much to learn from and admire about this L&D leader’s approach, but in a nutshell: You must be married to the business's challenges, not just delivering learning in the hope of affecting them. We're rarely going to be invited to the conversations we want to be in and so we need to take our opportunities, deliver impact, use successes as leverage and reinforce - via our actions - that we are a crucial factor when it comes to driving performance and results.