Data Team Leadership

Each unnecessary leadership layer slows data team delivery and makes it less innovative. Most have 1 leader for every 3-5 ICs, and that’s way too bloated. The ideal ratio of leaders to ICs is 7-10 ICs to 1 leader. Here’s how that translates to an organizational structure. A team of 10-12 ICs needs a manager. If the team has more junior or mid-level ICs, it may be 7 ICs to a manager until the team matures. Managers are people leaders first and technical leaders second. First-time managers must have a leadership mentor at the director or VP level, even if they don’t directly report to them. 3-5 managers require a director or VP. They lead other leaders and own part of the data and AI strategy implementation, which requires more effort. Directors are in training to become leaders of other leaders and need close support. VPs are experienced leaders of leaders who are in training to implement strategy. The director or VP reports to the C-level leader. The first or interim CDO/CAIO/CDAO manages the transformation from early maturity stages to a data team that delivers over 25% of new revenue growth and cost savings. The VP is trained to assume the C-level role and manage the last mile of AI strategy implementation, leading the data team to deliver over 50% of new revenue growth and cost savings. They continue developing the team to meet new business needs and optimize delivery. Most data teams decentralize and become part of the business units and product teams they support. I teach data organizational structure and maturity progression in my AI Strategist and Data Organizational Leadership courses. Use the link under my name to drop the org chart’s muffin top. #DataScience #DataEngineering #Leadership

Trust is not something you have, but something you do. 6 proven ways to build unshakeable trust with your team, TODAY: (Sample situations and scripts are included) 1. Say what you do. Minimize surprises. ➜Why: Consistency in communication ensures everyone is on the same page, reducing uncertainties and building reliability. ➜Situation: After a meeting, promptly send out a summary of what was agreed upon, including the next steps, owners, and deadlines. ➜Script: "Thank you for the productive meeting. As discussed, here are our next steps with respective owners and deadlines. Please review and let me know if any clarifications are needed." 2. Do what you say. Deliver on commitments. ➜Why: Keeping your word demonstrates dependability and earns you respect and trust. ➜Situation: Regularly update stakeholders on the project's progress. Send out a report showing the project is on track, and proactively communicate any potential risks. ➜Script: "Here's the latest project update. We're on track with our milestones. I've also identified some potential risks and our mitigation strategies." 3. Extend the bridge of trust. Assume good intent. ➜Why: Trust grows in a culture of understanding and empathy. Giving others the benefit of the doubt fosters a supportive and trusting environment. ➜Situation: If a team member misses an important meeting, approach them with concern and understanding instead of jumping to conclusions. ➜Script: "I noticed you weren’t at today’s meeting, [Name]. I hope everything is okay. We discussed [key topics]. Let me know if you need a recap or if there's anything you want to discuss or add." 4. Be transparent in communication, decision-making, and admitting mistakes. ➜Why: Honesty in sharing information and rationale behind decisions strengthens trust. ➜Situation: Be clear about the reasoning behind key decisions, especially in high-stakes situations. ➜Script: "I want everyone to understand why we made this decision. Here are the factors we considered and how they align with our objectives..." 5. Champion inclusivity. Engage and value all voices. ➜Why: Inclusivity ensures a sense of belonging and respect, which is foundational for trust. ➜Situation: Encourage diverse viewpoints in team discussions, ensuring everyone feels their input is valued and heard. ➜Script: Example Script: "I'd really like to hear your thoughts on this, [Name]. Your perspective is important to our team." 6. Be generous. Care for others. ➜Why: Offering support and resources to others without expecting anything in return cultivates a culture of mutual trust and respect. ➜Situation: Proactively offer assistance or share insights to help your colleagues. ➜Script: "I see you’re working on [project/task]. I have some resources from a similar project I worked on that might be helpful for you." PS: Trust Is Hard-Earned, Easily Lost, Difficult To Reestablish...Yet Absolutely Foundational. Image Credit: BetterUp . com

86% of executives believe employee trust is soaring. (Yet only 67% of employees actually trust their leaders.) I remember confidently walking into our quarterly review. Our metrics were up. Our strategy was clear. I thought trust was high. I was wrong. Here's what was really happening: → Top talent quietly updating their LinkedIn. → Real feedback staying buried in private chats. → Innovation dying in "yes" meetings. → Engagement surveys hiding hard truths. After losing three star employees in one month, I realized: Trust isn't built in fancy workshops or team events. It's cultivated through consistent moments that matter. 10 science-backed trust builders that transformed my team: (And won us an award!): 1/ Kill Information Hoarding (It's Hurting You) ↳ 85% trust transparent communicators. ↳ WHY: In the absence of clarity, fear fills the gap. ↳ HOW: Share board meeting notes company-wide. ↳ Pro Tip: Share bad news faster than good news. 2/ Own Your Mistakes (Like Your Career Depends On It) ↳ Leaders who admit errors gain 4x more trust. ↳ WHY: Perfect leaders are feared, not trusted. ↳ HOW: Share mistakes in weekly all-hands. ↳ Pro Tip: Add what you learned and your fix. 3/ Master Active Listening (Beyond The Basics) ↳ 62% trust leaders who truly hear them. ↳ WHY: Everyone knows fake listening from real attention. ↳ HOW: Block "listening hours." No phone, no laptop. ↳ Pro Tip: Summarize what you heard before responding. 4/ Show Real Empathy (It's A Skill, Not A Trait) ↳ 76% trust leaders who understand their challenges. ↳ WHY: People don't care what you know until they know you care. ↳ HOW: Start meetings with "What's challenging you?." ↳ Pro Tip: Follow up on personal matters they share. 5/ Invest In Their Growth (Play The Long Game) ↳ 70% trust leaders who develop their people. ↳ WHY: Investment in them is an investment in trust. ↳ HOW: Give every team member a growth budget. ↳ Pro Tip: Help them grow, even if they might leave. The Results? Our trust scores jumped 43% in six months. Retention hit an all-time high. Real conversations replaced surface-level meetings. Your Next Move: 1. Pick ONE trust builder. 2. Practice it for 7 days. 3. Come back and share what changed. Remember: In a world of AI and automation, trust is your ultimate competitive advantage. ↓ Which trust builder will you start with? Share below. ♻️ Share this with a leader who needs this wake-up call 🔔 Follow me (@Loren) for more evidence-based leadership insights [Sources: HBR, Forbes, Gallup]

You're the new data lead for a $100M brand. Here's your 90-day playbook. I used to think the first 90 days was about proving technical expertise. But it's not about the tools. Instead, It's about building trust. Days 0-30: Get a lay of the land. Start with people, not platforms. Schedule coffee chats with every sr. leader across: - Finance - Marketing - Merchandising - Operations - Customer Service Ask them: - What data challenges keep you up at night? - Which KPIs drive your team's success? - Where are your biggest blind spots? While you're doing this, go figure out what data and tools are available. Get a list every tool and report. If this doesn't exist, creating it is an easy win. Get access to everything. Start digging in and exploring. Days 31-60: Quick wins. By now you've got a list of pain points. Pick 3 high-impact, low-complexity problems like: - GA4 cleanup - CAC payback analysis - Return reason analysis - Run an incrementality test - Identify the most profitable promos - Basic customer analytics (RFM, LTV) - Post-purchase survey implementation - Profitability analysis by product/category - Marketing spend dashboard consolidation Pro tip: Make the data accessible while you're at it. - Set up a basic data warehouse (BigQuery/Snowflake) - Start using no-code ETL tools like Fivetran - Focus on commonly used data sources first Days 61-90: Building Momentum By now, you've gained trust... ...Now scale it. - Keep those leadership conversations going - Automate manual reporting processes - Make data self-service where possible - Train teams on analysis best practices - Start plotting your long-term roadmap Most new data leads try to fix everything at once. But true success comes from: - Building trust with leadership (and your peers) - Solving tangible problems quickly - Making data accessible to everyone - Having a clear vision for what's next What would you add to this 90-day plan? What quick wins worked for you? ♻️ Share this with a data lead who needs it 🔔 Follow me for more rants on data + marketing

Leadership confession: Our team just shared some brutally honest (and bad) data about a specific product challenge. And I couldn’t be more grateful. What the data revealed wasn’t pretty… It highlighted a challenge a customer was facing. But here’s what struck me: - Our team didn’t hide it - They didn’t sugar-coat it - They didn’t bury it in positive metrics They brought it directly to an executive with a plan to address it and they did, fast. This moment reinforced something crucial about leadership: The hardest part of solving problems isn’t the solution... it’s the discovery. When your team feels safe to surface uncomfortable truths, that’s when you know your culture is working. As leaders, we celebrate wins, launches, and growth. But perhaps we should be equally (if not more) celebrating those moments when someone says: “𝘛𝘩𝘪𝘴 𝘥𝘢𝘵𝘢 𝘴𝘩𝘰𝘸𝘴 𝘸𝘦’𝘳𝘦 𝘮𝘪𝘴𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘢𝘳𝘬.” “𝘐’𝘮 𝘯𝘰𝘵 𝘴𝘶𝘳𝘦 𝘸𝘦’𝘳𝘦 𝘩𝘦𝘢𝘥𝘦𝘥 𝘪𝘯 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘥𝘪𝘳𝘦𝘤𝘵𝘪𝘰𝘯.” “𝘔𝘺 𝘪𝘯𝘪𝘵𝘪𝘢𝘭 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯𝘴 𝘸𝘦𝘳𝘦 𝘸𝘳𝘰𝘯𝘨.” How can we create cultures where people feel supported and are rewarded for bringing the “bad” data? 

Data teams are becoming software engineering teams. On December 14th we welcomed Philip Zelitchenko, VP of Data from ZoomInfo, to talk about how he has built this discipline within his team & it was fascinating. The video is here : https://lnkd.in/gBFwkTqq Like the Devops movement, the Dataops movement aims to scale the use of data within companies without increasing the headcount of the data team. To do that, Philip defines data products using DPRDs, structures his data team with five key roles, & defines clear roles between the data team & others in the company. DPRDs, or Data Product Requirements Documents, contain the key information about a data product: what it will provide, how it will produce value, how the data will be governed including data quality alerting. Unlike code, data is stochastic or unpredictable. Data may change in size, shape, distribution, or format. This adds an additional dimension of complexity to the DPRDs. In addition to the DPRD, the ZoomInfo data team employs TEP or technical execution plan that aligns the internal technical teams on architecture & governance. The data team has five key roles: 1. Data PMs : quarterback the DPRDs. They gather feedback from users, define the value, solicit feedback from the rest of the team, then manage the execution of the plan. 2. Business logic : the data engineering team build the ETL pipelines while the data science team researches & implements machine learning algorithms for ML\DS driven data products. 3. Data analysts : embedded/seconded to the different operating teams, analysts analyze the data each team needs using the infrastructure provided by the data platform. 4. Data governance : ensures data quality/accuracy, defines the access control policies for security, sets the operating procedure for alerting & monitoring, and help define data contracts between producers, processors, and consumers. 5. Data platform : builds the universal data infrastructure for the company. Last, the ZoomInfo team is building an internal product called Heartbeat that measures usage across the main data products, evaluate the priority, SOPs for impact on SLAs and communication with data practinioers across the org in an automated way. For Philip, leading the data team is about focusing on the data products that drive meaningful value to the company. I learned a tremendous amount about the way modern data teams, who leverage software engineering disciplines, operate. Thank you, Philip!

Too many teams accept data chaos as normal. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North take a different path - eliminating silos, reducing wasted effort, and unlocking real business value. Here’s the playbook they’ve used to break down silos and build a scalable data strategy: 1️⃣ Empower domain teams - but with a strong foundation. A central data group ensures governance while teams take ownership of their data. 2️⃣ Create a clear governance structure. When ownership, documentation, and accountability are defined, teams stop duplicating work. 3️⃣ Standardize data practices. Naming conventions, documentation, and validation eliminate confusion and prevent teams from second-guessing reports. 4️⃣ Build a unified discovery layer. A single “Google for your data” ensures teams can find, understand, and use the right datasets instantly. 5️⃣ Automate governance. Policies aren’t just guidelines - they’re enforced in real-time, reducing manual effort and ensuring compliance at scale. 6️⃣ Integrate tools and workflows. When governance, discovery, and collaboration work together, data flows instead of getting stuck in silos. We’ve seen this shift transform how teams work with data - eliminating friction, increasing trust, and making data truly operational. So if your team still spends more time searching for data than analyzing it, what’s stopping you from changing that?

Too many leaders operate their teams through a dashboard. Yes—metrics matter. But they don’t tell the whole story. Dashboards show you what happened. They don’t tell you why. Here are some reasons to get more hands-on: • You’re leading a new team and learning the people and process.  • Your team isn’t hitting goals and you need to understand why.  • You’re in the middle of a critical transformation or growth phase. Metrics are often lagging indicators. By the time something shows up in the data, it may already be too late. Leadership isn’t static. You have to adapt—to the moment, the team, and the work. Sometimes that means zooming out. Other times, it means zooming in. Dashboards are great tools. But they don’t build trust. They don’t build clarity. They don’t build teams. Leaders do that.

The future of leadership isn’t louder. It’s more emotionally intelligent. AI and data don’t make decisions. Leaders do. And the best ones know when to pause, question, and take a breath. Here’s the thing → ✗ It’s not about how fast your model trains. ✓ It’s about how well your team trusts you when it breaks. Real AI/Data leaders: ⤷ Pause before the fire drill. ⤷ Explain data like they’re talking to their mom. ⤷ Admit bias before Twitter does it for them. ⤷ Know AI needs humans in the loop. ⤷ Celebrate people out loud, not just in Jira tickets. ✗ Because leading AI isn't just science. ✓ It's soul. ✗ And empathy isn’t extra. ✓ It’s essential. Teams led by emotionally intelligent leaders see 76% higher engagement and performance. (Source: Harvard Business Review, 2024) You advocate for change before you're asked. That’s not soft, that’s brave. ✔️ Startups love AI for speed. ✔️ Entrepreneurs love it for scale. ✔️ Investors love it for returns. But the smartest founders? They know AI without EQ is just fast failure. OpenAI, LinkedIn, CSAIL MIT, DeepMind, Microsoft AI, One Autism Health What’s one emotionally intelligent trait you wish more tech leaders had? Drop it in the comments. Let’s build better together. 👇 ♻️ Tag a data leader who doesn’t just code the future, they care about it. 📌 Save this for later. 👉🏻 Follow Glenda Carnate for more on leadership and growth! #founder #leadership #innovation #data #ai #entrepreneurship #emotionallyintelligence #dataleadership 

Why Most Leaders Get This One Thing Completely Backward (and It’s Costing Them Performance) Here’s the brutal truth: you’re not responsible for the numbers. You’re responsible for the people who are responsible for the numbers.– Simon Sinek That shift in mindset changed everything for me. When I first stepped into a leadership role, I focused on reports, spreadsheets, and bottom-line results. But despite the long hours and tracking KPIs, performance plateaued. Why? Because I was managing data, not leading people. Then I learned this core leadership truth: Healthy teams drive healthy numbers. That’s when I made the shift—from pressure to presence. From metrics to mentorship. And the results? • Turnover dropped by 34% in 6 months • Team productivity increased by 47% in 90 days • We exceeded our quarterly goals—without burning out the team Why? Because people who feel seen, heard, and empowered take ownership of results. So if you’re a new manager, executive, or business owner and feel stuck chasing metrics, ask yourself: “Am I leading people, or just managing performance?” Here’s what to do: 1. Prioritize one-on-ones (people before performance reports) 2. Develop trust through active listening 3. Coach instead of control This isn’t just feel-good leadership—it’s measurable. It’s strategic. It’s sustainable. Want a real-world framework to make this shift? DM me DRIVE for my leadership white paper: “3 Ways Great Leaders Drive Results WITHOUT Chasing Numbers” you’ll: • Learn the people-first principle that top-performing leaders use • Hear a real story of how I turned around a stagnant team • Get a repeatable framework you can use this week If you lead people—this message is for you. You can’t control the numbers, but you can inspire the people who do. Lead smarter. Lead better. Lead human. #LeadershipDevelopment #CoachingCulture #PeopleFirstLeadership