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.

Stop Being the Data Butler! Your data team is drowning in “can-you-quickly-pull-this-data” requests and becoming a dashboard factory? This reactive approach is killing your potential to drive real value. 🚫 Master the art of saying “no”. Prioritize and free up room for your team to tackle projects that really move the needle. 🛠️ Stop building on quicksand. Focus on a rock-solid data foundation and make quality non-negotiable to enable high-impact use cases. 🤝 Partner, don’t just serve. Align data initiatives with business objectives and translate insights into concrete actions. Time to evolve from service desk to strategic partner. Your business needs it, your team deserves 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

Your data problems aren't actually about data—they're X-rays revealing deeper organizational issues. Data struggles are not just broken dashboards or fragmented databases—they're revelations about how teams collaborate, how decisions flow, and how leadership shapes priorities.  👉 If Finance's spreadsheets can't talk to Marketing's dashboards, it's because Finance and Marketing aren't talking enough.  👉 Overengineered analytics pipelines emerge from fear of making bold decisions.  👉 Meaningless KPIs come from avoiding tough alignment conversations. Think of data health as an organizational early warning system—the cultural canary revealing hidden fault lines. When leadership ignores anomalies or fails to invest in proper governance, what looks like neglected data is actually a mirror of neglected organizational health. If you can't measure customer retention, that's not a data gap—it's a priorities crisis. Here's the kicker: This creates a vicious feedback loop. Poor data drives flawed decisions, which reinforces the problems that created the poor data. Take a marketing department working with unreliable lead attribution—they'll inevitably misallocate resources, deepening organizational inefficiencies and eroding trust in decision-making. When no one trusts the numbers, "the data is broken" becomes a convenient excuse for "We'd rather not face our internal misalignments." Teams retreat to gut instincts and outdated heuristics, further distancing themselves from reliable insights. Left unchecked, this pattern breeds a culture where finger-pointing trumps progress. The path forward requires treating data issues as leadership imperatives: 👉 First, create unified goals that demand cross-functional collaboration—shared KPIs that break down territorial walls. 👉 Second, elevate data literacy to the same level as financial fluency across your organization. 👉 Third, and most crucially, simplify. Complexity isn't sophistication—it's a tax on your organization's agility. The organizations that thrive won't be the ones with the most advanced tech stacks or the biggest data teams. They'll be the ones who recognize that data health and organizational health are two sides of the same coin. You can’t fix organizational issues by fixing the data.

🧭 The role of the Data Protection Officer (DPO) is undergoing a profound transformation. Once viewed primarily as a compliance steward for the General Data Protection Regulation (#GDPR), the DPO is now emerging as a central #architect of digital governance. This evolution is driven by the convergence of multiple EU regulatory frameworks: namely the #NIS2 Directive, the Digital Operational Resilience Act (#DORA), and the #AIAct, just to name the most relevant, and each introducing new layers of accountability, risk management, data governance and ethical oversight. Together, these instruments form a complex regulatory ecosystem that demands a multidisciplinary approach. The modern DPOs are no longer just legal compliance officers, they now operate at the dynamic crossroads of #law, #cybersecurity, operational #resilience, and AI #ethics. As digital ecosystems grow more complex, the DPO is evolving into a true #DataProtectionEngineer, equipped not only to interpret regulations but to architect privacy-aware systems. 📌This role demands a deep understanding of how emerging technologies such as AI, #IoT, #cloudinfrastructure, which affect the fundamental rights and freedoms of individuals. It’s not just about safeguarding data; it’s about safeguarding dignity, autonomy, and #trust in the digital age. ⚠️ Key Challenges for Organisations As regulatory expectations intensify, organisations face a series of strategic and operational hurdles that underscore the importance of a well-educated and experienced DPO. 1️⃣ Regulatory Fragmentation and Overlap Multiple frameworks introduce overlapping obligations, definitions, and enforcement mechanisms. Without centralised coordination, organisations risk inconsistent compliance and exposure to regulatory sanctions. The DPO serves as the 'central figure' for harmonising these requirements across legal, technical, and operational domains. 2️⃣Accountability and Demonstrable Compliance Supervisory authorities increasingly demand evidence-based compliance. Organisations must maintain detailed records of data flows, AI development processes, and incident responses. The DPO must champion a culture of #accountability, supported by robust governance structures and documentation protocols. 3️⃣ Technical and Organisational Complexity DORA mandates rigorous digital resilience testing and ICT risk assessments. The AI Act imposes strict data quality, explainability, and human oversight requirements. These obligations require cross-functional collaboration and significant investment in infrastructure, training, and tooling. At the end of the day, the DPO must act as a change agent, fostering alignment between compliance, innovation, and business objectives. The challenge is formidable, but so is the opportunity to redefine the role as a cornerstone of ethical, secure, and forward-looking digital governance.

If you're losing brilliant women at the final stages of hiring - this might be why... Let me talk you through a recent example where a company had a disproportionately high number of women dropping out at late interview and offer stage for their tech roles: They were offering great salaries. Flexible working. A decent benefits package. So what was going wrong? We took a look at the data. Out of 2 billion data points, a few things stood out: → Diversity is non-negotiable. Women in tech rank it 31% higher than the average candidate. If they don’t see representation in leadership, they won’t apply → Flexible hybrid work wins, because structure matters. Demand for remote-only roles is 11% below average, while core hours and in-office collaboration rank higher → Family-friendly policies trump flashy perks. Fertility leave (+41%), job sharing (+33%), and parental leave (+19%) are the real differentiators But then we dug deeper; and that's where it got really interesting: → Women in data roles showed a higher demand for in-office work - mentorship and access to resources mattered → Women in engineering & development wanted mission-driven work and career progression above all else → Women in product roles prioritised culture and flexibility more than any other group The company checked their employer brand. Their careers page talked about “great culture” and “exciting opportunities.” But it said nothing about what actually mattered to the people they were trying to hire. They weren’t losing candidates because of the salary or the benefits. They were losing them because they don't know what their target talent groups actually want. The companies getting this right aren’t guessing. They’re using data to shape their employer brand - so they attract the right people, with the right message. Download our women in tech report to access more of these insights: https://lnkd.in/enYcGpeW And tell me if you've turned down a job offer for similar reasons? #WomenInTech #Hiring #EmployerBranding #FutureOfWork #DiversityMatters

𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝘀 𝗻𝗼𝘁 𝗯𝗼𝗿𝗻 𝗳𝗿𝗼𝗺 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗮𝗹𝗼𝗻𝗲 — 𝗶𝘁’𝘀 𝘀𝗵𝗮𝗽𝗲𝗱 𝗯𝘆 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗔𝗴𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗥𝗶𝘀𝗸 ! 🔑 Students who scored higher on Presence and Agility (linked to Extraversion and Openness to Experience) were more likely to step into leadership roles. 🤝 Sociability and intellectual curiosity — long studied as drivers of emerging leadership — remain powerful predictors of who rises to lead. ⚡ Personality‑based agility measures show that comfort with switching gears under pressure, even at the risk of mistakes, reflects the adaptability leaders need most. 🎯 Interestingly, those who spread their effort across multiple smaller tasks (rather than focusing only on high‑reward ones) showed a stronger propensity for leadership. 🧭 And at its core, leadership is about making decisions with limited information, balancing potential rewards with unknown consequences. Understanding how someone approaches risk—strategically, emotionally, and cognitively—can offer valuable clues about their leadership approach, according to a fascinating research published by a team of researchers from Korn Ferry Wharton Neuroscience Initiative Student Society, and Lazul.ai using data from students at the University of Pennsylvania. ✅ 𝙈𝙮 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡 𝙫𝙞𝙚𝙬:  I believe these latest amazing findings could mark a real turning point for organizations striving to build stronger leadership pipelines. If we can identify leadership potential, early before years of experience accumulate, it opens up entirely new ways to nurture and support future leaders from the very beginning of their journey. It also means we may discover high‑potential talent in places we’ve overlooked. Someone who doesn’t fit the “traditional mold” might still carry the adaptability, curiosity, and resilience that great leaders need to thrive. What excites me most is the shift from relying solely on résumés (CVs) or past achievements to looking at real‑time behaviors and mindsets: 🔍 How people adapt under pressure 🔍 How they balance risk and reward 🔍 How they stay engaged across multiple priorities 🔍 How they bring presence, agility, and curiosity into the moment By combining personality insights with behavioral data, we gain a fuller, richer picture of how leadership takes shape often long before it’s made official with a title. Thank you 🙏 Korn Ferry Wharton Neuroscience Initiative Student Society, and Lazul.ai researchers team for these insightful findings: Amelia Haynes Sarah Hezlett Elizabeth Johnson, PhD Jean-Marc Laouchez James Luria Lewis, PhD Michael Platt, PhD, PMP Winston Sieck 🔑Are we overvaluing experience and undervaluing adaptability when identifying future leaders? #Leadership #Agility #Adaptability #PeopleDevelopment

Data jobs didn’t disappear — the value did. A decade ago, Harvard Business Review called the Data Scientist “the sexiest job of the 21st century.” Everyone rushed in — bootcamps, certificates, “transition to data” programs exploded. Fast forward: hiring freezes, layoffs, disillusionment. What happened? Most data teams failed to deliver business value. -They built dashboards that no one used. -Models that never left Jupyter notebooks. -Reports that didn’t drive decisions. As one study found, only ~32% of companies actually realize measurable value from data investments. The rest? Busywork disguised as insight. The hard truth: We trained a generation of “data tool users,” not business problem solvers. Here’s what the next generation of data professionals must do differently: 1. Define business problems first. If you can’t articulate the “why,” your model is useless. 2. Run experiments, deploy solutions, measure results. Rigor beats fancy titles. 3. Deliver outcomes, not outputs. Dashboards and models don’t matter — impact does. Stop chasing influencers and certificates. Start chasing value creation. In this market, the sexiest skill isn’t Python - it’s critical thinking. #datascience #business #analytics