Building client trust with data translation

At Merrill Lynch, I learned trust starts with clear data. LOs, here's how to use it. Most loan officers are sitting on a gold mine of untapped data. But 99% don't know how to leverage it to build trust with clients. At Merrill Lynch, I watched top financial advisors transform raw numbers into client relationships that spanned decades and generations. Advisor quarterly meetings weren't just market updates. They were personalized scorecards showing families' progress toward specific goals. Clients could see advancement toward college funds, retirement targets, and legacy planning. This goal-tracking appeared alongside investment performance metrics. The approach kept wealthy families engaged through market volatility. Clients stayed engaged regardless of market conditions. This insight offers powerful lessons for loan officers: • Data doesn't just inform decisions • Data transforms transactions into meaningful milestones • Data builds unshakeable trust Here's the opportunity most LOs are missing: While financial advisors use data to track progress toward financial goals, loan officers can do the same with real estate: • Local home price trends that cut through media noise • Mortgage rate context that spans beyond today's headlines • Historical housing patterns that give perspective This positions you as a trusted guide through the largest purchase of your client's life—not just a transaction processor looking for the next commission. The best part? It's surprisingly simple to implement. Invest just 30 minutes quarterly for lasting client value: 1. Gather free market data from Zillow or Freddie Mac 2. Create your template once, then simply refresh the numbers 3. Deliver automatically through your CRM or Mailchimp 4. Include personalized life-event milestones that might trigger refinancing For example, one LO using this approach saw a 40% increase in referrals after just two quarterly updates. These simple analyses transform client relationships: • Create 7-year milestone check-ins that align with typical homeownership cycles • Target life events like children's graduations that often trigger equity needs • Optimize contact timing based on which emails get opened and calls answered These insights emerge from data you already have—no fancy tech required. The question is: Which metrics are you already tracking that could reveal hidden opportunities in your business?

Even accurate data can be wrong. Here’s why CXOs trust spreadsheets more than a $250K data stack. I learned this the hard way. We delivered a data product that was perfect. We had done everything by the book: - Conducted dozens of stakeholder interviews. - Agreed on every single definition. - Aligned all metrics with their business objectives. - Unified and implemented a scalable data foundation. - Rolled out a beautiful self-service environment. - Started rolling out a data governance process and program. We did it in weeks because our way of working is to get feedback as fast as possible and iterate based on value and input. That way, you're building around value and adoption. A few weeks later, we discovered no one trusted it; the same old questions about "this data is wrong" appeared. What happened? They all reverted to their old, broken, "completely inaccurate" spreadsheets even though all the bells and whistles were in place. Why? Because we never asked the most important question: "What would it take for you to trust this?" We assumed logic would win. We assumed "correct" was the same as "trustworthy." It isn't. Every time someone looks at data, they ask in their head, "Where did this come from?" Your technically perfect data was an alien. So, what did we do? Here’s the playbook we now follow with every client (leaning on some practices that actually work in governance and quality): 1. Uncover trust triggers early: Ask every stakeholder, “What would it take for you to trust this number?” 2. Surface and reconcile shadow systems: We gather all those Excel sheets and dashboards; they aren’t "wrong." Bring them into the light, compare with warehouse outputs, and explain differences. Kill myths openly and remove them. 3. Assign a single owner per KPI: Governance fails without accountability. One name next to each KPI and each business area. Provide data marts to the most data-driven user; this helps establish a self-service environment. 4. Make logic transparent: Document lineage of where this data is coming from. Executives want clarity on this, not just "it's from 'raw_crm_v24_final' table" (jk about the name). 5. Prioritize ruthlessly: Don’t “fix all data.” Fix only what’s tied to business impact and what’s agreed upon in the data strategy; focus on the low-hanging fruits. Treat quality as iterative, business-first. 6. Feedback over policies: Weekly truth checks. Weekly feedback calls are non-negotiable. Get feedback, align on priorities, ask tough questions. Governance isn’t about documents; it’s about adoption. The lesson? Even the most accurate data will fail if no one trusts it. But all of that is irrelevant if the data isn’t in check. 🧬 Repost if you think data quality is a major issue. → Comment below 'DQ' and I'll send a roadmap to fix your data quality in weeks.

Here's a very simple thing you can do to increase trust in your data. Add a comparison column to your business glossary. Consider this common scenario: A user asks, “𝘞𝘩𝘺 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘵𝘩𝘦 𝘳𝘦𝘷𝘦𝘯𝘶𝘦 𝘪𝘯 𝘚𝘩𝘰𝘱𝘪𝘧𝘺 𝘮𝘢𝘵𝘤𝘩 𝘸𝘩𝘢𝘵 𝘐 𝘴𝘦𝘦 𝘪𝘯 𝘰𝘶𝘳 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥?” The data team explains the difference, but in the process, a little trust in the data is lost. Sound familiar? So, how can data teams get ahead of these questions and maintain trust in the data? Add a comparison column to your business glossary. If you’re not familiar with a business glossary, it lists important business terms and their meanings that everyone in a company agrees on. The comparison column takes this a step further. Its goal is to explain: 1. Why a metric from your custom-built dashboard differs from its source platform. 2. How much difference to expect. 3. What the appropriate comparison should be. Example: Let’s say your dashboard includes a “Revenue” metric. Users will inevitably compare it to “Total Sales” in Shopify. These values will almost always differ, leading to confusion and distrust. In your business glossary’s comparison column for “Revenue,” you could include something like this: • 𝘙𝘦𝘷𝘦𝘯𝘶𝘦 𝘶𝘴𝘦𝘴 𝘵𝘩𝘦 𝘥𝘢𝘵𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴 𝘸𝘦𝘳𝘦 𝘧𝘶𝘭𝘧𝘪𝘭𝘭𝘦𝘥, 𝘸𝘩𝘪𝘭𝘦 𝘚𝘩𝘰𝘱𝘪𝘧𝘺 𝘶𝘴𝘦𝘴 𝘵𝘩𝘦 𝘵𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯 𝘥𝘢𝘵𝘦. 𝘏𝘪𝘴𝘵𝘰𝘳𝘪𝘤𝘢𝘭𝘭𝘺, 𝘵𝘩𝘪𝘴 𝘭𝘦𝘢𝘥𝘴 𝘵𝘰 𝘢 ±5% 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘵𝘩𝘦 𝘵𝘸𝘰 𝘮𝘦𝘵𝘳𝘪𝘤𝘴. 𝘍𝘰𝘳 𝘣𝘦𝘵𝘵𝘦𝘳 𝘢𝘭𝘪𝘨𝘯𝘮𝘦𝘯𝘵, 𝘤𝘰𝘮𝘱𝘢𝘳𝘦 𝘚𝘩𝘰𝘱𝘪𝘧𝘺’𝘴 𝘳𝘦𝘱𝘰𝘳𝘵𝘦𝘥 𝘳𝘦𝘷𝘦𝘯𝘶𝘦 𝘵𝘰 𝘵𝘩𝘦 “𝘊𝘢𝘴𝘩 𝘊𝘰𝘭𝘭𝘦𝘤𝘵𝘦𝘥” 𝘮𝘦𝘵𝘳𝘪𝘤 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥. By proactively addressing these differences, you: • Reduce the workload for your data team. • Help users better understand the data. • Solidify trust in your dashboards and metrics by showing that you are aware and one step ahead. That's it for today. If you'd like to learn more about building a business glossary, check out my profile and download my free data strategy guide.

I froze for a minute when a client asked me “How do I know my data is safe with you?” Not because I didn’t have an answer But because I knew words alone wouldn’t be enough. After all, trust isn’t built with promises. It’s built with systems. Instead of just saying, “Don’t worry, your data is safe,” I did something different. I showed them: 👉 NDAs that legally protected their information 👉 Strict access controls (only essential team members could ) 👉 Encrypted storage and regular security audits 👉 A proactive approach—addressing risks before they became problems Then, I flipped the script. I told them- “You’re not just trusting me, you’re trusting the systems I’ve built to protect you” That changed everything. → Clients didn’t just feel comfortable—they became loyal. → Referrals skyrocketed because trust isn’t something people keep to themselves. → My business became more credible. And the biggest lesson? 👉 Security isn’t just a checkbox. It’s an experience. Most businesses treat data protection as a technical issue. But it’s an emotional one. When clients feel their information is safe, they don’t just stay. They become your biggest advocates. PS: How do you build trust with your clients?

Trust is essential when it comes to data. How you communicate data is as important as the data you are communicating. The words you choose, the context you provide, and even the language you use make a big difference in how your audience perceives, interprets, and interacts with your data. Here are five tips to help you build trust with your audience and communicate your data in a responsible way.   1/ Question the Source. Who collected the data, why, and when? Was it collected ethically? Are particular perspectives missing? The answers to these questions will influence how you present your data and help you decide whether the information you intend to use can be trusted. 2/ Be Transparent. Document the assumptions, steps, and methods you use to clean and analyze the data. This will allow others to verify, replicate, or challenge your findings, fostering accountability and trust. 3/ Be Intentional. Apply design principles (e.g., contrast, repetition, alignment, proximity) strategically to help your audience explore the data or follow a narrative. Each choice will influence how your audience perceives and interacts with your design. 4/ Embrace Empathy. Accurately and empathetically capture the knowledge, behaviors, and people behind the data. Find ways to help your audience understand and connect with the topic(s) depicted in your designs. 5/ Show Respect. Design with your audience's needs and experiences in mind. Include your audience in the design process and ask for feedback to address possible barriers to understanding or engagement. -- The new year is right around the corner, and my 2025 calendar is open and filling up fast. Let's work together to enhance the accessibility of your data and harness its power responsibly. Reach out via message or use the contact link in the comments. #AccessibleAnalytics #DataEquity #VizResponsibly #Charts #DataVisualization #Graphs #DataEthics #DataPresentation #DataAnalytics #DataCommunication #Dataviz

Recent client meetings have left me a bit stumped! Because I keep hearing the following: “We don’t trust our data.” It's not the first time I've heard it, and I bet it won’t be the last. The irony? Those same businesses were using data every single day to pay invoices, run supply chains, and make strategic calls. So it’s not really the data they mistrusted. It must be something deeper. So where does this mistrust come from? Sometimes it’s a cover for not liking what the numbers say (because numbers don’t bend to opinion). Other times, it’s really about trust in the data team rather than the data itself. Occasionally, it’s just become a lazy throwaway line. If organisations want to break this cycle, both leaders and data teams need to change the way they work together. Here’s a 5 point playbook that stops “data mistrust” in its tracks: 1. Define Once, Use Everywhere: agree common definitions for key metrics. Document them, make them visible, and hold teams accountable for sticking to them. Consistency builds confidence. 2. Show the Journey: make data lineage transparent. Leaders should see where a number originates, how it’s transformed, and why it ends up in a dashboard etc. Traceability removes suspicion. 3. Shared Accountability: data isn’t an “IT product.” It’s a joint effort. Business leaders must own the accuracy of inputs; data teams must own the quality of models and outputs. Co-ownership prevents finger-pointing. 4. Resolve Issues Quickly: don’t let data concerns fester. Implement visible feedback channels, track issues openly, and close them with clear communication. The faster issues are addressed, the stronger trust becomes. 5. Normalise Hard Truths: not all insights will be comfortable. That’s the point. Leaders must be ready to hear what the numbers say, and data teams must present them clearly. Data itself isn’t untrustworthy. It’s the behaviours, mindset, and responses around it that determine whether people believe it. So let’s stop hiding behind the lazy phrase “we don’t trust our data.” 👉 Business leaders are you really questioning the data, or just avoiding what it’s telling you? 👉 Data teams are you giving the business clarity, speed, and confidence, or just more numbers to argue over? Because until both sides stop passing the blame, “data mistrust” won’t go away, it will just keep undermining decisions. Mark Stouse Bill Schmarzo Malcolm Hawker Eddie Short Kyle Winterbottom Edosa Odaro Joe Reis Matthew Small Dan Everett