Data-Driven Leadership

Met a Head of Data & Analytics recently whose challenge made me pause! They had a skilled data team. Tools, dashboards, reports, all in place. But something wasn’t adding up. “We look at the data every week,” they said. “But somehow, we always end up backtracking to what already exists.” I asked them: “Are your data experts empowered to tell the truth or just expected to make the numbers look good?” That gave them pause. And in that silence, the real questions surfaced: - Do they want insight or just confirmation? - Are your data teams allies of growth or guardians of ego? Because here’s the uncomfortable truth: It’s easy to use data to back a story you want to tell. It’s harder to let data shape the story you need to hear. The bravest leaders I’ve worked with? They make space for discomfort. They allow their data teams to challenge the narrative, not just decorate it. If you're investing in data talent, ask yourself: Are they here to tell the real story, or to polish the one you've already written?

Jessica Lachs is the global head of analytics and data science at DoorDash, where she’s built one of the largest and most respected data organizations in tech. In her more than 10 years at DoorDash, she has served as the first general manager, responsible for launching new markets; the head of business ops and analytics; and the VP of analytics and data science. In our conversation, she shares: 🔸 How to structure and scale a high-impact analytics organization 🔸 Benefite of centralized data teams 🔸 How to pick the right metric and aligning incentives 🔸 Advice for data people on how and when to push back 🔸 Lessons learned from building a global data team 🔸 How to foster a culture of extreme ownership 🔸 The role of AI in improving analytics team productivity 🔸 Advice for aspiring data leaders without formal training Listen now 👇 - YouTube: https://lnkd.in/gBk5F8wn - Spotify: https://lnkd.in/g8g99PPP - Apple: https://lnkd.in/gaK4NMgF Some key takeaways: 1. While average metrics are important, it’s crucial to also focus on edge cases and fail states. These rare but significant instances, like DoorDash’s “never delivered” orders, can have profound negative impacts despite their infrequency. 2. DoorDash converts metrics into a “common currency” to make better decisions, faster, about what to prioritize. They quantify business levers (e.g. price, selection, quality) in terms of their impact on a common metric like gross order value (GOV). For example, they know the relative impact on GOV for each of these changes: a. Lowering price by a dollar b. Lowering delivery times by a minute c. Adding a new restaurant to the platform in a particular area 3. Once you understand how different metrics impact the “common currency,” you can understand the tradeoffs between different actions more accurately and prioritize more quickly for maximum impact. 4. Analytics is about driving business impact, not just providing a service to other functions when requested. Data teams should be involved in decision-making alongside engineering and product. They should not only surface insights about what is happening in the data but should have a point of view on what to do about it. 5. The best data analysts have soft skills, on top of table-stakes technical ability. Jessica loves analysts who are curious enough to dig deeper even when they’ve “answered the question.” She tests for this when hiring by including some flaws in her case studies to see if the candidates notice and/or how they respond when this is pointed out. She also loves analysts who can have a point of view with incomplete information and pivot with new information.

#PeopleAnalytics: Turning #HRMetrics into #Strategic Insights In today’s data-driven organizations, HR is evolving from a support function to a strategic powerhouse. These HR Metrics are more than just numbers; they’re lenses through which we can understand workforce dynamics, organizational health, and business impact. Let’s break it down: 🔹 Absenteeism Rate: A high rate may signal burnout, disengagement, or systemic issues in workplace culture. Tracking it helps identify patterns and intervene early. 🔹 Employee Attrition & Retention: These twin metrics reveal the stability of your workforce. High attrition can be costly and disruptive, while strong retention often reflects good leadership and employee satisfaction. 🔹 Internal Promotion Rate: A key indicator of talent mobility and succession planning. Promoting from within boosts morale and reduces hiring costs. 🔹 Cost Per Hire & Time to Hire: Efficiency metrics that reflect the effectiveness of your recruitment strategy. Long hiring cycles or high costs may point to process inefficiencies or misaligned sourcing channels. 🔹 Offer Acceptance Rate: A direct measure of your employer brand and candidate experience. Low acceptance rates might mean your value proposition isn’t resonating. 🔹 Human Capital ROI: This is the ultimate business case for HR—how much return you’re getting from your investment in people. It’s a powerful metric for aligning HR with financial performance. 🔹 Employee Engagement: Often measured through surveys, this metric captures how emotionally and cognitively invested employees are in their work. High engagement is correlated with productivity, innovation, and employee retention. 💡 Why it matters: These formulas empower HR teams to move from reactive to proactive. They help diagnose problems, forecast trends, and make evidence-based decisions that drive business value. People analytics isn’t just about tracking—it’s about transforming. #PeopleAnalytics #HRStrategy #HumanCapital #WorkforceInsights #EmployeeExperience #DataDrivenHR #Leadership #FutureOfWork #LinkedInHR #HRLeadership

When I was at Goldman Sachs, I learned quickly that what gets measured often gets valued. But I also learned that what truly matters like character, potential and resilience can be harder to see. Today, we’re sitting on more data than ever. Inside our organizations, we can trace pay decisions, promotion paths, and roles that consistently lead to upward mobility. We can see whose work is being rewarded … and whose isn’t. It’s rarely about who earned a 4.0 GPA or followed the “right” path. It’s often about who delivers, who grows, and who creates impact. AI gives us new ways to surface this information. But the real opportunity is illumination, not automation. We can use data to challenge assumptions, broaden our definition of excellence, and build cultures that truly reward performance and potential. For leaders, this moment calls for urgency and honesty.  We need to be cautious of relying too heavily on historical data in decision-making, especially since the markers of success today are not the same as in the past. The opportunity is to use that data as a baseline, but not as a blueprint. From there, we can evolve our thinking. I want to start more conversations about this: Are we using the data we already have to reflect the values we claim to hold?

Most “data-driven” cultures aren’t driven. They’re dragged. You see it everywhere: → Analysts drowning in “quick questions” → Dashboards built for no one, maintained by everyone → Dashboards get skimmed and then spark 6 follow-up requests → And it always leads to: "Can you export that to Excel?" → And in the end: “Let’s buy another tool.” Here’s the hard truth: You don’t have a tooling problem. You have a leadership void disguised as a data backlog. Data teams aren’t supposed to just build things. They’re meant to guide. That means: - Saying “no” to noise - Asking “why” like a toddler with a mission - Designing for decisions, not just reports - Coaching teams on how to think, not just what to click Stop dragging people with dashboards. Start pulling them forward with clarity. That’s when your data culture starts to drive itself. Want to make the shift from dashboard factory 🏭 to strategic partner♟️ ? Join 1500+ data pros who read my free newsletter for weekly tips on building impactful data teams in the AI-age: https://lnkd.in/gqQ728mA

Want to know a company's true commitment to data? Find out who their data leader reports to. If the answer isn't "the CEO," it often signals a missed opportunity. Organizations that have reached the critical mass to appoint a senior data leader—let's call them the Chief Data Officer (CDO)—generally choose one of four reporting lines: to the top executive (CEO), finance (CFO), operations (COO), or IT (CTO/CIO). While each of these may seem logical, the choice profoundly impacts data's strategic potential. A seemingly obvious option might be to place the CDO within IT, given their alignment with technology. But this setup can easily limit data's transformative capacity. IT's core mandate is typically stability, security, and efficiency—not driving business innovation through data. This isn't to diminish IT's importance; collaboration between IT and data is essential. But this collaboration works best as a partnership, not a hierarchy where one reports to the other. What about finance or operations? These setups often emerge from either historical precedent or the company's leadership views data primarily through a cost or process lens. But these structures risk confining data to optimization of existing functions rather than reshaping business models. For maximum impact, the CDO should therefore report directly to the CEO. This ensures that data has a voice where the strategies are shaped—not just where they're executed. Direct access to senior decision-making isn't just about organizational status; it's about enabling data to reshape fundamental choices—from product development to market entry to customer relationships—that no single function owns. Beware though that even with CEO reporting, companies can falter by treating the CDO role as a staff function with limited resources. A CDO expected to "prove value first" without proper funding might deliver isolated improvements in efficiency or customer insight, but will struggle to fundamentally reshape how the business operates and competes as a whole. Successful data-driven companies understand this. For them, data transcends technology and operations. It shapes the decisions that define a company's future, such as what products to build, what customers are served and how value is delivered. These organizations elevate data leadership to the top, ensuring they don't just predict the future with data—they shape it.

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

The real gap between digital leaders and laggards isn’t just in technology—it's in mindset. The 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐃𝐢𝐯𝐢𝐝𝐞 isn’t about who has the best tools; it’s about who knows how to wield them. The difference between average and excellent isn’t in the number of systems implemented but in the strategic intent behind them. True digital transformation isn’t just an IT initiative—it’s a company-wide movement, a reimagining of what’s possible when leadership, innovation, and agility align. 𝐖𝐡𝐚𝐭 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐋𝐨𝐨𝐤𝐬 𝐋𝐢𝐤𝐞: • 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲-𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: CIOs and CTOs leading the charge, with an inward focus on IT infrastructure. • 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐎𝐯𝐞𝐫 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Tracking efficiency and business performance without a broader view towards future capabilities. • 𝐂𝐚𝐮𝐭𝐢𝐨𝐮𝐬 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬: Proceeding with digital steps without the urgency to outpace the evolving market demands. • 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Maintaining the status quo in operations, favoring predictability over agility. • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐓𝐨𝐨𝐥 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧: Providing employees with collaboration tools without fostering a culture of digital innovation. • 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Concentrating on backend upgrades before considering the customer-facing aspects of the business. • 𝐒𝐢𝐥𝐨𝐞𝐝 𝐃𝐚𝐭𝐚 𝐔𝐭𝐢𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Using data for routine business operations rather than as a cornerstone for transformation and innovation. 𝐖𝐡𝐚𝐭 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐭 𝐋𝐨𝐨𝐤𝐬 𝐋𝐢𝐤𝐞: • 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐓𝐨𝐩: Transformation championed by CEOs, integrating digital priorities within the company’s vision. • 𝐂𝐨𝐦𝐦𝐢𝐭𝐦𝐞𝐧𝐭 𝐭𝐨 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Measuring success through the lens of innovation and digital proficiency. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Not merely adapting but actively advancing digital initiatives, even in challenging economic climates. • 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐠𝐢𝐥𝐢𝐭𝐲: A culture that embraces operational efficiency as a path to competitive advantage. • 𝐏𝐞𝐨𝐩𝐥𝐞 𝐚𝐬 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐲: Investing in employee engagement and digital literacy, recognizing that technology amplifies human potential. • 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Prioritizing the customer experience with a strategy that adapts proactively to their needs and behaviors. • 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬: Leveraging AI and data analytics not only to inform decisions but to foster a culture of continuous improvement. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/eU_Cc3ga ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

One of the biggest threats to data-driven leadership isn’t technology-related—it’s overconfidence. That’s why the 🚨 𝐃𝐮𝐧𝐧𝐢𝐧𝐠-𝐊𝐫𝐮𝐠𝐞𝐫 𝐄𝐟𝐟𝐞𝐜𝐭 🚨 is so dangerous: Those with limited knowledge think they know it all, while experts second-guess themselves. William Shakespeare summarized this bias more than 400 years ago when he said, “The fool thinks himself to be wise, while a wise man knows himself to be a fool.” 𝐇𝐨𝐰 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐟𝐚𝐥𝐥 𝐢𝐧𝐭𝐨 𝐭𝐡𝐢𝐬 𝐭𝐫𝐚𝐩 (𝐥𝐢𝐦𝐢𝐭𝐞𝐝 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 + 𝐨𝐯𝐞𝐫𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞) ❌ Trust their gut over data instead of questioning assumptions ❌ Make decisive decisions based on misinterpretations ❌ Dismiss expert advice and oversimplify complex issues ❌ Overestimate the data maturity of their teams ❌ Resist upskilling efforts, assuming they already “get” data 𝐖𝐡𝐲 𝐞𝐱𝐩𝐞𝐫𝐭𝐬 𝐬𝐭𝐮𝐦𝐛𝐥𝐞 (𝐝𝐞𝐞𝐩 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 + 𝐥𝐞𝐬𝐬 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐭) ❌ Undervalue their contributions to informing decisions ❌ Hesitate to challenge flawed interpretations or decisions ❌ Overcomplicate explanations, making insights harder to follow and act on ❌ Assume the data speaks for itself and the right course of action is obvious ❌ Struggle to communicate insights effectively (data storytelling!) You won’t be able to fix this problem with more AI, analytics, or dashboards. To overcome this trap, you need a cultural shift. It starts with humble leaders who know they don't have all the answers and empowered experts who trust their knowledge enough to speak up. Here are some other steps you should consider: ✅ 𝐏𝐫𝐨𝐦𝐨𝐭𝐞 𝐝𝐚𝐭𝐚 𝐥𝐢𝐭𝐞𝐫𝐚𝐜𝐲: Make it a priority for all decision-makers. ✅ 𝐄𝐥𝐞𝐯𝐚𝐭𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐯𝐨𝐢𝐜𝐞𝐬: Give data teams a seat at the table. ✅ 𝐅𝐨𝐬𝐭𝐞𝐫 𝐚 𝐭𝐞𝐬𝐭-𝐚𝐧𝐝-𝐥𝐞𝐚𝐫𝐧 𝐜𝐮𝐥𝐭𝐮𝐫𝐞: Encourage leaders to test assumptions with data. ✅ 𝐂𝐫𝐞𝐚𝐭𝐞 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐥𝐨𝐨𝐩𝐬: Evaluate decisions against real-world outcomes. What else would you add to this list to overcome this trap and help foster healthy data-driven leadership? 🔽 🔽 🔽 🔽 🔽 📬 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7 📚Check out my new data storytelling masterclass: https://lnkd.in/gy5Mr5ky ��️ Need a virtual or onsite data storytelling workshop or speaker? Let's talk. https://lnkd.in/gNpR9g_K