Using small wins to gain trust in analytics

I violated data best practices to deliver a $40K ROI. (The client renewed. Here's why.) For 4 years, I've preached data best practices: Build proper data models. Minimize tech debt. Do it right the first time. Then reality hits. A mid-sized healthcare company hires us. They need a manual report automated. Fast. Your offer as a consultant is speed-centric. Their "source of truth" is 400 stored procedures written by a DBA who left 2 years ago. Zero documentation. Spaghetti SQL everywhere. 30+ Power BI reports querying directly off the transactional database. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱 𝘁𝗼 𝗱𝗼: Build a clean data warehouse from scratch. Proper dimensional modeling. Governed metrics. Best practices. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗶𝗱: Replicated their messy legacy logic in the cloud. Matched their numbers exactly—even the parts I knew were questionable. Automated the manual report in 6 weeks. Delivered the $40K ROI we guaranteed. 𝗪𝗵𝘆? Because many executives don't care about best practices. They care about results. Now. You don't get 3-6 months to "do it right." You get 6 weeks to prove you're worth keeping. 𝗧𝗵𝗲 𝘁𝗿𝘂𝘀𝘁-𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: If you show up and tell them their legacy logic is wrong, they won't trust you. If you replicate it perfectly first, they do. Once trust is built? Then you can challenge the legacy logic. Then you can propose the proper data model. Then you can start fixing the mess. But not before. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗾𝘂𝗶𝗰𝗸 𝘄𝗶𝗻𝘀 𝘁𝗵𝗮𝘁 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝘁𝗿𝘂𝘀𝘁 Automate one critical report. Match legacy numbers. Show ROI fast. 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝘁𝗵𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 "This works, but it creates tech debt. Here's the plan to fix it long-term." 𝗖𝗮𝗿𝘃𝗲 𝗼𝘂𝘁 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗲𝗯𝘂𝗶𝗹𝗱 Once trust is established, allocate hours to build the proper foundation. 𝗞𝗲𝗲𝗽 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝘄𝗵𝗶𝗹𝗲 𝘆𝗼𝘂 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 Don't stop showing ROI while you refactor. Balance both. 𝗧𝗟;𝗗𝗥: Best practices are the North Star. But speed to value is survival. Deliver quick wins. Build trust. Then improve the foundation. Perfection kills consulting businesses. Progress builds them. Agree or Disagree? ♻️ Share this if you've ever had to choose between doing it "right" and doing it "fast." Follow me for real talk on what data consulting actually looks like in the wild.

“We spent millions on AI and have nothing to show for it.” That’s what the CEO told me. And they weren’t wrong… The results were underwhelming. Deadlines kept slipping. The board was asking tough questions. But instead of agreeing to pull the plug, I said something that surprised them: "Before you give up, let's take three steps back." I emphasized that AI can deliver exceptional outcomes, but only when you're rooted in what's actually achievable. Here's what I mean: STEP ONE: Know exactly what you're dealing with - The current state of your data quality - How prepared your infrastructure really is - What capabilities your team actually possesses STEP TWO: Balance your aspirational AI goals (what could be possible) with the reality of what you can deliver today (what is practical). Success in AI comes from marrying honest evaluation with executable strategy. So that’s exactly what we did: we stepped back, rethought the goal, and simplified the approach. We kept their ambitious vision but completely changed the execution: → Redefined success metrics to be measurable and achievable. → Broke their "moonshot" goal into 6 smaller milestones. → Started with one use case in a smaller capacity that could demonstrate clear ROI Six weeks later, they had their first AI success story. Not the revolutionary transformation they originally envisioned, but something better: proof that AI could work in their environment. - That early win gave the team confidence.  - The board renewed their commitment.  - And now they're scaling systematically. So the lesson here isn't about scaling back your vision. It's about finding the right path forward. Sometimes that means starting smaller to eventually go bigger. Big AI transformations don't happen overnight. They happen when you break them into manageable pieces and prove value incrementally. Start practical. Then scale ambitious. Have you ever had to shift from moonshot thinking to practical execution in AI? How did it go?

Stop asking executives what they want in their dashboards. It's the fastest way to build something they'll never use. Here's what actually works for executive Power BI adoption: 1. Start With Decisions, Not Designs Wrong question: "What reports do you want?" Right question: "Which decisions need better data?" Focus on enabling better decisions, not prettier charts. 2. Build Trust Through Small Wins Perfect dashboards mean nothing if no one believes the numbers. What we've seen work well: • Week 1: Simple table visual (verify the numbers) • Week 1-2: Basic automation (show consistency) • Week 2-3: Added insights (demonstrate value) • Week 4+: New features (expand impact) Consistency builds confidence more than complexity. 3. Design for Quick Consumption Most-used reports follow these rules: • Readable in 90 seconds • Key metrics front and center • Clear visual flow and storytelling • Works on mobile If it takes too long to understand, it won't get used. 4. Start Small, Grow Smart Our most successful dashboards usually start with: • 3-5 must-have metrics • 1-2 clear visualizations • Daily refreshes • No complex features We evolved based on actual usage, not assumed needs. Success came when we stopped thinking like technical experts and started thinking like executive assistants. What's worked in your experience with executive dashboards? — ♻️ Repost if your network needs to see this, and follow Austin Levine for more.

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?

𝗢𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝘄𝗶𝘁𝗵 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀 Early in my career, I thought being a great researcher meant delivering perfect insights. I spent hours polishing slides, crafting the clearest recommendations, thinking that’s how I would gain influence and drive impact. But over the years, I’ve learned: 𝗧𝗿𝘂𝘀𝘁 𝗶𝘀𝗻’𝘁 𝗯𝘂𝗶𝗹𝘁 𝗶𝗻 𝗳𝗶𝗻𝗱𝗶𝗻𝗴𝘀 𝗮𝗹𝗼𝗻𝗲. 𝗜𝘁’𝘀 𝗯𝘂𝗶𝗹𝘁 𝗶𝗻 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘀𝗵𝗼𝘄 𝘂𝗽. Looking back, some of the most trust-building moments weren’t in research readouts, but in smaller and ongoing interactions like chats, 1:1s, tech reviews and roadmap meetings. At first, these deeply technical discussions about model architectures, system tradeoffs, and backend constraints felt daunting. But I leaned in with deep curiosity to learn their world – their language, their constraints, how they define success. I began asking questions that brought a different lens – questions about user experience implications, hidden assumptions in metrics, and whether definitions of success truly aligned with user value. Over time, I noticed a shift. Partners began pulling me into more of these conversations. They valued not only the different perspective I brought but also that I was designing research grounded in their reality. The closer I got to their world, the more they trusted me to help them navigate complexity with users in mind. Here are a few lessons that have guided me: 💡 𝗟𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝗰𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆, 𝗻𝗼𝘁 𝗰𝗿𝗶𝘁𝗶𝗾𝘂𝗲. It’s easy to point out flaws. It’s harder – and far more powerful – to ask questions that unlock better thinking. 💡 𝗚𝗲𝘁 𝗰𝗹𝗼𝘀𝗲 𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝘄𝗼𝗿𝗹𝗱. Sit in their reviews and participate in their discussions. Learn the tradeoffs they’re wrestling with. Empathy is the foundation of trust. 💡 𝗦𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻𝘀. When partners see how you approach a problem, they begin to trust your intuition and judgment, not just your final results. 💡 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝘂𝗽𝗹𝗲𝘃𝗲𝗹𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘄𝗼𝗿𝗸. Research isn’t just about answering questions; it’s about reframing them to drive better decisions. When partners see that your involvement helps them achieve goals faster, better, and with greater user impact, trust accelerates. 💡 𝗖𝗲𝗹𝗲𝗯𝗿𝗮𝘁𝗲 𝘁𝗵𝗲𝗶𝗿 𝘄𝗶𝗻𝘀. Research insights are powerful, but it’s the engineers, PMs, and designers who build and ship. Recognizing their contributions creates shared ownership and success. At the end of the day partnership is built in 𝘀𝗺𝗮𝗹𝗹 𝗺𝗼𝗺𝗲𝗻𝘁𝘀 – asking a clarifying question that reframes priorities, acknowledging a tough tradeoff, or staying a bit longer to align on next steps. Trust grows when partners see you’re not just doing your job, but actively working to strengthen their efforts and amplify their impact.

"Does it really make a difference?" "Will anyone even notice?" "Is 2 minutes really worth the effort?" I hear these questions every time someone improves a small process. The doubt is real: → Saving 2 minutes feels pointless → One small fix seems insignificant → Nobody celebrates tiny improvements → Big problems need big solutions But here's what actually happens: 2 minutes saved per cycle. 20 cycles per day. 40 minutes saved daily. 40 minutes × 250 working days = 167 hours per year. That's 4 full work weeks. From one person. Making one small improvement. Now multiply that by: → 50 people doing the same thing → 10 different small improvements → Every department finding their 2 minutes Suddenly you're talking about real impact. Small wins add up because: - They're easy to implement - People actually do them - They build confidence for bigger changes - They create momentum - They compound over time The math is simple: 1% better every day = 37x better in a year. 1% worse every day = nearly zero in a year. Those 2 minutes matter because: → Your people see that improvement is possible → Problems start feeling solvable → Teams begin looking for more opportunities → Culture shifts one small win at a time The biggest improvements I've seen started with: Moving a tool 3 feet closer Adding one simple checklist Eliminating one unnecessary step Asking one better question Don't underestimate the power of small. Small wins create big believers. Big believers create lasting change.

On a client call last week, an advocacy org client asked me what to do with their member survey results. They had heard from 650 out of 1500 members in the annual-ish survey. I could almost sense this temptation to check the box: "Survey done. Report written. Move on." But their report sitting in a PDF isn't impact. It's just decent graphs with deep potential. So, here is what I told them: ● Start small. Set up an analysis plan with your team members - and sit with the data to go beyond the jargon charts and obvious insights. ● Pick one thing — one thing! — you will change because of what people told you. Name it publicly. Give it a timeline. ● At some point within the next six months, please circle back. Tell them what shifted because they spoke up. Invite them to tell you if it landed the way they hoped. That's how a survey stops being an extractive exercise and becomes part of a feedback loop. That's how data builds trust instead of fatigue. That's how people stop seeing surveys as yet another task and start seeing them as a conversation. #nonprofits #nonprofitleadership

Why “Small Wins” Matter More Than Big Projects when it comes to AI and Automation? When most people think about AI transformation, they picture a massive project that touches every part of the business. The reality is much simpler, and much more achievable. Real change doesn’t happen overnight through one huge investment. It happens through small, strategic wins that build confidence and momentum. -> Automating a manual reporting process. -> Predicting customer churn just a little better. -> Eliminating one approval bottleneck. At SparkBrains, we always recommend starting with a few low-complexity, high-impact problems. Because when teams see immediate value, when they save hours, reduce errors, or unlock new insights, it shifts the entire conversation from “Will this work?” to “Where else can we apply this?” Momentum compounds. And businesses that master small wins first are the ones that lead big shifts later. The smartest move you can make right now? Start small. Scale smart.

If you want the whale, first earn the minnow’s trust. At my former company, we rarely walked into a boardroom and landed the big deal off the bat. We started small and made damn sure we crushed it. Why? Because trust is usually earned in increments. Not in pitch decks, but in performance. Here’s the 4-step approach I used to turn small wins into major accounts: #1 Start with something you can absolutely deliver. This isn’t the time to experiment.  Pick a scope you can dominate. Then OVER deliver on that smaller scope. Do it faster, do more than you agreed to, over over-communicate within the client organization. #2 Overdeliver and then highlight what you did. - Remind them of their good decision to trust you.  Go beyond the scope. Don’t ask for more money, just deliver more value and remind them of it. That’s how trust compounds. That’s how you are able to get more of their spend, or even add more value in terms of additional products and services. #3 Use that trust to expand the conversation. Now you’ve got permission to ask: “What else can we help with?” “What other divisions could benefit from this?” “Have you thought about shifting more of your spend to US, vs them?” BOTTOM LINE: When you over-deliver and the client acknowledges it, you now have a reason for your next suggestion. #4 Have a predefined account growth plan. The best account developers already know the next 4 places they can add value. They don’t wait for the client to suggest it; they bring the plan with clear paths to added value. That’s how you go from pilot project to being a strategic partner. If you’re sitting on a “small opportunity,” treat it like your Super Bowl: Overdeliver, expand, scale. If you’ve landed big wins off the back of small ones, let’s exchange notes... #bigwins #whalehunting #salesedge #salesweapons

𝐃𝐚𝐭𝐚 𝐭𝐫𝐮𝐬𝐭 𝐢𝐬 𝐚𝐬𝐲𝐦𝐦𝐞𝐭𝐫𝐢𝐜. 𝐘𝐨𝐮 𝐥𝐨𝐬𝐞 𝐢𝐭 𝐟𝐚𝐬𝐭. 𝐘𝐨𝐮 𝐞𝐚𝐫𝐧 𝐢𝐭 𝐬𝐥𝐨𝐰𝐥𝐲. According to Deloitte, 67% of executives say they're not comfortable accessing or using data from their analytics systems. Even in companies with strong data cultures, 37% still express discomfort. This creates a strange reality. Companies invest millions in data infrastructure. They build dashboards. They hire analysts. Then decision-makers ignore the outputs and trust their gut instead. KPMG found that 67% of CEOs prefer intuition over data-driven insights. Not because they're anti-data. Because they've been burned by unreliable numbers before. The trust gap has real causes: broken dashboards, siloed departments, alert fatigue, metrics that don't match reality. Great Expectations found that 77% of organizations have data quality issues, and 91% say it impacts company performance. Trust isn't rebuilt with better tools. It's rebuilt with consistency. Every time a number is wrong, trust drops. Every time a number is right, trust barely moves. One thing that works: pick your five most-used metrics. Run automated checks on them daily. When something breaks, fix it before anyone asks. Do this for three months. That's how trust compounds. 𝐖𝐡𝐞𝐧 𝐝𝐢𝐝 𝐬𝐨𝐦𝐞𝐨𝐧𝐞 𝐥𝐚𝐬𝐭 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐚 𝐧𝐮𝐦𝐛𝐞𝐫 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠?