Using Existing Data to Boost Trust

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

Businesses struggle with fragmented customer data. Across different departments or systems, customer records are often incomplete or duplicated. But what if the customer was the master data record? In our recent live podcast with Jamie Smith💡, he presented how digital ID wallets can transform customer engagement by shifting control of verified data to the individual. Jamie's thesis is that through the use of digital wallets, businesses can start treating the customer as the master data record. With a digital wallet, customers store their digital verifiable credentials, including identity documents and account preferences. And businesses can request that verified information when needed, directly from the customer's wallet, thereby reducing operational complexity, improving trust, and creating frictionless digital experiences. Here's how: 1) Faster onboarding and higher conversion rates: Instead of requiring customers to repeatedly enter personal details and submit ID documents, businesses can request pre-verified data from their digital wallet in a single step. 2) Fraud prevention and risk reduction: Businesses can authenticate customers instantly without relying on insecure, stored identity data. 3) Creating a new revenue opportunity in the form of a verified data exchange: This model enables businesses to act as credential issuers rather than data warehouses. For example, a financial institution (FI) could issue a verified creditworthiness credential, allowing a customer to share a trusted record with a lender instead of submitting sensitive documents manually. The FI could then charge for making that verified data available for verification. 4) Personalization without privacy risks: Instead of businesses storing and analyzing vast amounts of customer data, individuals can store and share their verified preferences directly from their digital wallet. Because businesses receive information directly from customers in real-time, they can offer personalization without relying on invasive tracking methods. I believe that businesses that continue relying on outdated customer databases and traditional authentication methods will face growing challenges, including higher fraud risks, increased regulatory pressures, and declining customer trust. Those that embrace digital ID wallets now will gain a competitive advantage in a more secure, privacy-first digital economy.

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.

The Dark Truth About Data Most Won't Admit: Most organizations treat data like a forbidden vault: • Collect everything blindly • Obscure the mechanics • Pray for trust But here's the reality - trust isn't given, it's earned through visibility. When you embrace data transparency, three powerful forces emerge: 1. Confidence Cascade Clear practices lead to deeper trust Deeper trust enables richer engagement Richer engagement yields superior insights 2. Protection Multiplier Visible processes catch issues early Early detection enables rapid response Rapid response prevents major breaches 3. Innovation Accelerator Open systems encourage bold experiments Experiments generate fast learning Learning creates breakthrough solutions The winners in today's landscape? Not those hoarding the most data. It's those who handle it with radical transparency. DFFT (Data Free Flow with Trust) isn't just another acronym. It's the bedrock of sustainable data operations in our connected world. First introduced at the 2019 G20 Osaka Summit, DFFT established the blueprint for trustworthy cross-border data flow while unlocking unprecedented economic potential. Because in our complex reality: • Secrecy breeds doubt • Transparency cultivates trust • Trust fuels exponential growth The mandate is clear: Build systems that explain themselves. Create processes that invite scrutiny. Establish practices that build confidence. Your data will flourish. Your users will commit. Your business will thrive. The future belongs to those who embrace transparency not as a burden, but as a competitive advantage. Are you ready to build systems that last through trust? Share if you believe in creating transparent data ecosystems that endure. #DataTransparency #DFFT #DataTrust #Innovation

Trust is the foundation of every high-performing organization. But many leaders wonder: How do we measure trust? The good news is you may already have a tool in place—your employee engagement survey. We’ve found three key questions provide a direct connection to workplace trust. We call them the “Trust Trilogy”:   1️⃣ Is the information received from top leaders open and honest? Senior leaders have good intentions. The key is in how they share information—it’s about clarity, consistency, and timing. 2️⃣ Do you feel you have organizational support? Asking, “Do you have what you need today?” can lead to engagement and problem-solving. 3️⃣ Does the organization care about your well-being? From benefits to career development, employees who feel invested in are far more likely to stay.   Before spending time and money on a new way to measure trust, look at the data you already have. The answers are already there—acting on them is one of the best investments you can make.

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