Data Team Leadership

I've got bad news you're not going to want to hear: Adding more people won't solve your problem. Chances are, it'll make things worse. Most teams fall into this pattern: ❌ Problem → ❌ Add People → ❌ More Complexity → ❌ Bigger Problems → ❌ Add Even More People In their effort to add capacity, They drown themselves in complexity. They've fallen into a trap: The 'More People' Paradox. Capacity grows linearly.  Complexity grows exponentially. High-performing teams know this.  They choose a different path: ❌ Problem → ✅ Diagnose → ✅ Delete, Simplify, or Automate → ✅ Refocus & Execute → ✅ Scale They know pruning supports healthy growth. They fix the model to scale.  They don't hope scale fixes the model. Ask these 3 questions before you hire: 1️⃣ What could we stop doing? • Nice-to-have projects • Low-impact meetings • Redundant reports 2️⃣ What are we overcomplicating? • Communication channels • Project workflows • Decision making 3️⃣ What are we avoiding? • Technology improvements • Difficult conversations • Priority decisions BONUS: What could AI handle? • Document processing • Standard responses • Data aggregation • Routine analysis ✅ Remember: Our optimal path to greater success... Doing half as much, twice as well. If this post resonated with you... 🔔 Follow Dave Kline for more ♻️ Share to help others go big by thinking small What complexity are you ready to eliminate?

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

👀 I've now talked to dozens of teams implementing a data platform, across startups to enterprises, and these are the patterns I'm seeing... 🚀 Launching a data platform is a monumental task—not because of the technology requirements (which are still hard) but because of the necessary cultural change. ✌🏽 Another way to think of a data platform is as if it's a startup within a company where they have a two-sided market: ▪ 1. How do you convince teams that produce data to a) change their workflows to put data into the platform and b) make updates to align the data with the platform requirements? ▪ 2. How do you convince teams that consume data to adopt the data platform as their source of truth rather than going straight to the raw source data, especially when they already have established dashboards and reports? 🤝 Teams that I've seen find success have buy-in from engineering leadership to enable top-down decisions about where data producers will emit their data. 📉 I've noticed an enormous trap when the data team is siloed to only the downstream data organization (especially centralized teams) and doesn't have the means to build a strong relationship upstream with engineers. 🔎 This is especially apparent in large enterprises, where two vastly different organizations, sometimes in different countries, can exist within the enterprise. 🙌🏽 Regarding the consuming team, successful data platforms are ones where either a) there is a high level of trust in the data, or b) both sides are aware of the problem and have a clear plan to improve data trust where the data platform is a key piece. 🌀 I think this is where many data platform teams falter, as not creating the "business-wide story of a data platform enabling data trust" results in a vicious cycle of consumers skipping the data platform due to low trust, getting the wrong data from the source, and having bad data that then reduces data trust. 👇🏽 Does this align with your experience? What am I missing?

How do you build a Power Ranger data team 💪 🚀 Start with these roles in this order: 𝐃𝐚𝐭𝐚 𝐋𝐞𝐚𝐝 – Scopes the data landscape, figure out business requirements, makes initial recommendations on tech and use cases, leads dev and team build 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 – Assess the data sources & quality, organize some important data for business use, start building key pipelines 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (similar time as Eng) – Start building use cases with clean data, draw insights from data, automate reports 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 (if it makes sense) – Do additional data analysis, build relevant ML products, explore art of the possible 𝐃𝐚𝐭𝐚 & 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 – Build data models to support strategic needs, necessary for scale, advises on enterprise architecture & tech investment And don’t forget... 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 – Should exist in the organization already, draw a dotted line to the data team, helps provide requirements for data projects Of course, this order and these requirements all depend on the type/ size of business, use cases and team skills, but this is an idea of how to get started Other things you have to factor in (but may not be full-time positions at the beginning) are Data Strategy, Data Governance Lead, Product Manager, MDM specialist, solutions architect/ technology lead, CDO Thoughts and opinions? Honestly please push back and set me straight, as this differs by experience and organization

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

What does it really take to scale analytics? My time at 𝐅𝐫𝐚𝐜𝐭𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬, working alongside teams solving complex problems for Fortune 500 companies, taught me something I still believe today: 𝐆𝐫𝐞𝐚𝐭 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞𝐧’𝐭 𝐛𝐮𝐢𝐥𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐭𝐨𝐨𝐥𝐬. 𝐓𝐡𝐞𝐲’𝐫𝐞 𝐛𝐮𝐢𝐥𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬. The difference becomes clear when you look at how the best teams operate. Here are 5 lessons that stood out to me: 1️⃣ 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐳𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬, 𝐧𝐨𝐭 𝐫𝐞𝐩𝐨𝐫𝐭𝐬. The goal isn’t another dashboard or PowerPoint. The real deliverable is a 𝐛𝐞𝐭𝐭𝐞𝐫 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧. What happened? Why did it happen? And most importantly — 𝐰𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐝𝐨 𝐧𝐞𝐱𝐭? 2️⃣ 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐨 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬. The strongest analytics teams don’t stop at metrics. They connect their work to revenue, cost, customer retention, productivity, or other measurable business outcomes. 3️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐟𝐨𝐫 𝐭𝐡𝐞 𝟖𝟎%, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐞𝐱𝐩𝐞𝐫𝐭𝐬. Analytics creates value only when people can understand and use it. The best solutions make complex insights simple enough for non-technical teams to act on. 4️⃣ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐟𝐨𝐫 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲. A highly accurate model that nobody uses creates little value. A slightly less accurate model that teams trust and use every day can create far greater impact. 5️⃣ 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬, 𝐧𝐨𝐭 𝐨𝐮𝐭𝐩𝐮𝐭𝐬. “We built 10 models” is an output. “Our analytics helped increase revenue by X%” is an outcome. That shift in thinking changes how teams prioritize, build, and measure success. And these principles aren't limited to large analytics organizations. Whether you're building your 𝐟𝐢𝐫𝐬𝐭 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐞𝐚𝐦 or scaling one across multiple markets, the fundamentals remain the same: Analytics becomes powerful when it changes decisions. Which of these 5 principles would make the biggest difference in your organization today? Share your insights! #Analytics #DataScience #DataDriven #AI #AnalyticsLeadership #BusinessIntelligence #DigitalTransformation

Building a Great Data Team? 🏆 It’s Not About Unicorns, It’s About Balance! One of the biggest myths in data leadership is that one person can do it all. Sorry to break it to you, this person is not available The truth? No single individual will ever tick every box: Data science + data engineering + storytelling + governance + product thinking + stakeholder management…in one person? Unrealistic. Instead, what truly drives performance is a team built on complementary strengths. Here’s what I’ve found matters most when building a high-performing data team: 🔹 The Visionaries: Those who can see the big picture and align data with business strategy 🔹 The Builders: Engineers who ensure scalable, reliable infrastructure and pipelines 🔹 The Translators: Analysts and communicators who turn insights into action and make data human 🔹 The Guardians: Experts in governance, quality, and ethics who ensure we do things the right way 🔹 The Innovators: Those who experiment with AI, ML, and emerging tech to keep us future-ready It’s not about having every skill in every person. It’s about creating a puzzle where each piece fits perfectly. In my experience, the most effective teams are diverse in background, thinking style, and skills but united by curiosity, business impact, and a shared mission. I’d love to hear from you How do you build balance into your data or digital teams? What roles have made the biggest difference in your success? #DataStrategy #Leadership #Teamwork #DigitalTransformation #Analytics #EmergingTech #AI #DataCulture #DataTeams

How to Build a Data Analytics Team Over the years, I’ve had the opportunity to build and scale data analytics teams across different business sizes—from startups to large enterprises. One thing I’ve learned? There’s no universal formula. The structure of a data team should align with business maturity, data needs, and available resources. Here’s my take on how to do it right at different stages: A) Startups & Small Firms (0-50 employees) When you're just starting, you don’t need a massive data team—you need quick insights and automation to make fast decisions. I’ve seen many early-stage companies over-hire or invest in complex tech stacks before they even have solid data pipelines. What works best: 🔹 1-3 core data professionals who can wear multiple hats 🔹 Focus on BI & reporting before diving into ML/AI 🔹 Use lightweight tools like Google Sheets, Power BI, or third-party analytics Why? Small firms should first focus on tracking essential KPIs and automating repetitive tasks before scaling. B) Medium-Sized Companies (50-500 employees) As a company grows, so does its data complexity. I’ve helped mid-sized firms transition from fragmented data systems to structured data warehouses, enabling predictive analytics and customer insights. What’s needed at this stage: 🔹 5-15 members across analytics, engineering, and data science 🔹 A solid data infrastructure (warehouse, ETL, reporting automation) 🔹 A head of Data to align data strategy with business goals 🔹 A balance of speed & accuracy—teams should deliver insights while building scalable models Why? At this stage, businesses need better decision-making, not just reports—so investments in predictive modeling and self-serve analytics become critical. C) Large Enterprises (500+ employees) For large organizations, data is no longer a support function—it’s a competitive advantage. I’ve worked with enterprises where real-time analytics, AI-driven insights, and fraud detection were game-changers. What makes a difference: 🔹 Specialized teams (BI, Data Engineering, AI, Governance) 🔹 A Chief Data Officer (CDO) to cover data from 360* 🔹 Self-serve analytics & data governance to democratize insights while ensuring compliance Why? Scaling data operations in large firms isn’t just about hiring more analysts—it’s about creating a data-driven culture where every decision is backed by insights. Key Takeaways from My Experience: ✔️ Start small and focused, then scale as business needs grow ✔️ Don’t rush into AI/ML without strong data foundations ✔️ Foster a data culture—the best data teams enable decision-making, not just reporting Would love to hear from others—how have you structured your data teams? #DataAnalytics #Leadership #DataStrategy #AI #BigData #BusinessGrowth

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?