Data-Driven Leadership

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.

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

𝓡𝓮𝓬𝓮𝓼𝓼𝓲𝓸𝓷 𝓕𝓮𝓪𝓻𝓼? 𝓦𝓱𝔂 𝓓𝓪𝓽𝓪-𝓓𝓻𝓲𝓿𝓮𝓷 𝓒𝓸𝓶𝓹𝓪𝓷𝓲𝓮𝓼 𝓐𝓻𝓮 𝓜𝓸𝓻𝓮 𝓛𝓲𝓴𝓮𝓵𝔂 𝓽𝓸 𝓢𝓾𝓻𝓿𝓲𝓿𝓮 (𝓪𝓷𝓭 𝓣𝓱𝓻𝓲𝓿𝓮) Economic slowdowns test every business—but some not only survive the storm, they come out stronger. 𝑾𝒉𝒂𝒕’𝒔 𝒕𝒉𝒆𝒊𝒓 𝒆𝒅𝒈𝒆? 𝐃𝐚𝐭𝐚. Companies that embed data analytics into their decision-making DNA are more agile, more resilient, and more customer-focused. 𝐻𝑒𝑟𝑒’𝑠 ℎ𝑜𝑤: ✅ Smarter Resource Allocation: Instead of broad cost-cutting, data-driven companies pinpoint exactly which products, geographies, or segments are underperforming—and redirect efforts where the ROI is clear. ✅ Better Customer Retention: In downturns, acquiring new customers becomes expensive. Analytics helps businesses identify at-risk customers and craft targeted retention strategies. ✅ Faster Strategic Pivots: Whether it’s shifting to e-commerce, tweaking pricing models, or realigning supply chains—real-time data enables rapid, confident decision-making. 🔍 𝑇ℎ𝑒 𝑙𝑒𝑠𝑠𝑜𝑛: In times of uncertainty, 𝐠𝐮𝐭-𝐟𝐞𝐞𝐥 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. Companies that rely on structured data analysis outperform those that rely solely on instinct. If you’re not already building a data-first culture, now’s the time. Recessions don't wait. But neither does opportunity. 💬 𝑾𝒉𝒂𝒕’𝒔 𝒐𝒏𝒆 𝒅𝒂𝒕𝒂-𝒅𝒓𝒊𝒗𝒆𝒏 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏 𝒚𝒐𝒖𝒓 𝒄𝒐𝒎𝒑𝒂𝒏𝒚 𝒎𝒂𝒅𝒆 𝒕𝒉𝒂𝒕 𝒉𝒆𝒍𝒑𝒆𝒅 𝒅𝒖𝒓𝒊𝒏𝒈 𝒕𝒐𝒖𝒈𝒉 𝒕𝒊𝒎𝒆𝒔? Would love to hear your story below! #DataAnalytics #RecessionProof #StrategicPlanning #DataDrivenDecisionMaking

The same AI failure repeats across 127 companies in 23 countries. Different industries. Different budgets. Identical organizational problem. A Singapore financial services company deployed an AI platform after eighteen months of development. Investment: $4.2 million. The technology executed flawlessly. Six percent of employees used it. The project lead explained that the teams never confirmed whether this solved actual problems. They built what leadership wanted, not what operations needed. On the other hand, an Ohio manufacturing company launched crude AI with rough integration but adoption reached seventy-one percent in the first quarter. Their approach: three months of listening before building anything. Teams described bottlenecks. Frustrations. Actual workflow gaps. AI projects fail because organizations skip the uncomfortable step of validating real problems. Companies that succeed don't start with model selection. They start with systematic problem discovery. Your AI will function exactly as designed. Whether anyone adopts it depends entirely on whether you asked the right people the right questions before you built it. Most organizations get this backwards. They design solutions, then try to find problems those solutions can address. The pattern shows up consistently: impressive technology and minimal adoption followed by leadership confusion about why teams resist. You cannot engineer your way out of a listening problem. #AITransformation #TrustInAI #AIAdoption #LeadershipInAI #HumanCenteredAI

Data or Gut Feelings. Whenever I’ve made strategic decisions while neglecting my gut feelings, I have felt a tinge of regret. Leaders are often urged to make data-driven decisions in this age of abundant data. Data is significant; it offers valuable insights by revealing past trends and providing predictive analytics, yet I believe it has limitations. Data alone will not always account for individual circumstances, unexpected challenges, or the essential human elements crucial to effective leadership. On the other hand, intuition - rooted in experience, judgment, and the ability to recognise patterns - can be incredibly powerful, especially in uncertain or quickly changing environments. Still, we must acknowledge that biases and narrow perspectives can sway intuition. Today’s leaders face the interesting challenge of blending analytical skills with intuitive wisdom rather than choosing one over the other. For example, while data may highlight an emerging market trend, intuition empowers leaders to assess whether the timing, cultural relevance, or team readiness aligns with taking action. A potent way to bridge this gap is by asking lots of critical questions during decision-making: Cultivating a habit of evaluating choices from numerical and descriptive angles ensures a more robust approach. The essence of future leadership lies in mastering the art of merging analytics with intuition. We can achieve this by fostering critical thinking to evaluate data accuracy, employing scenario planning, evaluating multiple alternatives to juxtapose gut feelings with measurable insights, and building diverse team thinking to challenge assumptions. Practical steps, such as conducting post-mortems to reflect on decision-making processes, help bring this balance to life. When data and intuition unite, leaders can make much more impactful decisions. So, I vote for a harmonious combination.

𝗪𝗲 𝘁𝗮𝗹𝗸 𝗮 𝗹𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 – 𝗯𝘂𝘁 𝗳𝗮𝗿 𝘁𝗼𝗼 𝗹𝗶𝘁𝘁𝗹𝗲 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗱𝗲𝗰𝗶𝗱𝗲𝘀 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗶𝘁 𝘀𝘂𝗰𝗰𝗲𝗲𝗱𝘀 𝗼𝗿 𝘀𝘁𝗮𝗹𝗹𝘀: 𝘁𝗵𝗲 𝘀𝘁𝗮𝘁𝗲 𝗼𝗳 𝗼𝘂𝗿 𝗱𝗮𝘁𝗮. Over the past months, I noticed a pattern across industries. Teams have the ideas, the talent, the ambition. But then reality hits: 𝘯𝘰 𝘰𝘯𝘦 𝘳𝘦𝘢𝘭𝘭𝘺 𝘬𝘯𝘰𝘸𝘴 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢 𝘤𝘢𝘯 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘸𝘩𝘢𝘵 𝘵𝘩𝘦𝘺 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥. 𝗔𝗻𝗱 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗼𝗳𝘁𝗲𝗻 𝗱𝗶𝗲𝘀. In one organisation, the leadership team asked a simple question: “𝘈𝘳𝘦 𝘸𝘦 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘳𝘦𝘢𝘥𝘺 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘸𝘩𝘢𝘵 𝘸𝘦 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥?” Surprisingly, no one had a clear answer. Different tools, different owners, different definitions of quality, different evidence trails. Not a technology problem. A coordination problem. So we created a structured way to break through this. 𝗔 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝘁𝗲𝗹𝗹𝘀 𝘆𝗼𝘂, 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁: ✔️ 𝗵𝗼𝘄 𝗿𝗲𝗮𝗱𝘆 𝗶𝘁 𝗶𝘀, ✔️ 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗴𝗮𝗽𝘀 𝗮𝗿𝗲, ✔️ 𝘄𝗵𝗮𝘁 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗯𝗲 𝗳𝗶𝘅𝗲𝗱, 𝗮𝗻𝗱 ✔️ 𝗵𝗼𝘄 𝗹𝗼𝗻𝗴 𝗶𝘁 𝘄𝗶𝗹𝗹 𝘁𝗮𝗸𝗲. Nothing theoretical. A live artefact – updated, tracked, reviewed between Data roles and Product teams. It shortens decisions from months to weeks. It removes friction between IT and business. It focuses investment where impact is real. Most importantly: 𝗜𝘁 𝗴𝗶𝘃𝗲𝘀 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗺𝗺𝗶𝘁𝘁𝗶𝗻𝗴 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀. We now run these workshops with several organisations. Every time, teams tell us the same thing: “𝘍𝘪𝘯𝘢𝘭𝘭𝘺, 𝘸𝘦 𝘤𝘢𝘯 𝘮𝘢𝘬𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴 𝘸𝘪𝘵𝘩 𝘧𝘢𝘤𝘵𝘴 𝘪𝘯𝘴𝘵𝘦𝘢𝘥 𝘰𝘧 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴.” If more companies had this level of transparency, far fewer programs would get stuck halfway. 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 – 𝗵𝗼𝘄 𝗺𝗮𝘁𝘂𝗿𝗲 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗼𝗳 𝗶𝘁𝘀 𝗱𝗮𝘁𝗮? 𝘞𝘰𝘶𝘭𝘥 𝘴𝘶𝘤𝘩 𝘵𝘳𝘢𝘯𝘴𝘱𝘢𝘳𝘦𝘯𝘤𝘺 𝘤𝘩𝘢𝘯𝘨𝘦 𝘩𝘰𝘸 𝘺𝘰𝘶 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘴𝘦 𝘢𝘯𝘥 𝘴𝘵𝘦𝘦𝘳 𝘺𝘰𝘶𝘳 𝘪𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦𝘴? #Data #Leadership #Transformation #Governance #Enterprise 𝘝𝘪𝘥𝘦𝘰 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘫𝘰𝘴𝘪𝘦𝘭𝘦𝘸𝘪𝘴𝘢𝘳𝘵

Wondering how to prove you're ready for that promotion as a data analyst? Here’s how you can show you're ready to take the next step. 1. 𝗧𝗮𝗸𝗲 𝘁𝗵𝗲 𝗜𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝗼𝗻 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Don’t wait for assignments, but be proactive about identifying opportunities where you can improve business decisions with data. Leading these projects shows you’re ready to take on more responsibility. 2. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Promotions are about more than just technical skills. Improve your ability to communicate complex insights to non-technical stakeholders and build strong relationships across team borders. With every step you take on the career ladder, the focus shifts more and more from technical to soft skills. 3. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗖𝘂𝗹𝘁𝘂𝗿𝗲: Help your organization make better decisions by supporting data-driven practices. Lead training sessions or workshops to enable your team and business to use data effectively. 4. 𝗢𝘄𝗻 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Track and document the impact of your work. Whether it’s improving processes, increasing efficiency, or driving revenue, it helps you to show how your contributions have made a measurable difference. 5. 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝗦𝗵𝗮𝗿𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲: Continually develop your skills in advanced analytics, machine learning, or new tools. Share your learnings with your team, positioning yourself as a go-to expert and thought leader. 6. 𝗠𝗲𝗻𝘁𝗼𝗿 𝗝𝘂𝗻𝗶𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Show that you’re not just focused on your growth but on the growth of the team. Mentoring others to demonstrate leadership potential and a commitment to the success of the whole team. 7. 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆 𝗘𝘅𝗰𝗲𝗲𝗱 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀: Consistently delivering more than what’s expected of you signals that you’re ready for the challenges that come with a higher role. To secure the next promotion you need to prove that you’re ready to take your impact to the next level. Show your value to the business, and the recognition will follow. How are you positioning yourself for your next career move? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #promotion #careeradvice #careergrowth

Supporting a community-led data infrastructure is crucial for fostering local and equitable governance, which directly impacts healthcare outcomes. In my work as a healthcare provider, I have seen how data-driven decisions can significantly improve patient care and community health. Community-led data initiatives empower local stakeholders by providing them with the information necessary to advocate for their health needs and priorities. This empowerment is vital for fostering more inclusive and responsive healthcare systems. When communities control their data, they can highlight specific health issues and push for policies that address their unique challenges. Traditional data collection methods often overlook the nuanced realities of different communities, leading to healthcare policies that do not fully address local needs. By contrast, community-led data initiatives capture a more accurate and comprehensive picture of local health conditions. This detailed understanding allows for the creation of more effective and targeted healthcare policies. Moreover, building local capacity for data management and analysis is essential. Investing in community members' skills and infrastructure not only improves data quality but also ensures that data-driven healthcare decisions reflect the true needs and aspirations of the community. This capacity building is critical for sustainable and equitable healthcare development. Additionally, community-led data initiatives can enhance transparency and trust between communities and healthcare providers. When health data is collected and shared openly, it builds trust and fosters a collaborative environment where stakeholders are more likely to work together towards common health goals. In conclusion, supporting a community-led data infrastructure is vital for advancing local and equitable healthcare governance. This approach empowers communities, improves policy effectiveness, and fosters trust and collaboration. By investing in these initiatives, we can create more responsive and inclusive healthcare systems that better serve all members of the community. Read more: https://buff.ly/3AO5M2I #doctors #hospitals #healthcare #primarycare

I chatted with Khalifeh, 𝘋𝘪𝘳𝘦𝘤𝘵𝘰𝘳 𝘰𝘧 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦, at Google. Here's how AI is transforming the Data Science industry: 𝘛𝘩𝘦𝘴𝘦 𝘢𝘳𝘦 𝘒𝘩𝘢𝘭𝘪𝘧𝘦𝘩'𝘴 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴, 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘎𝘰𝘰𝘨𝘭𝘦'𝘴 𝘰𝘧𝘧𝘪𝘤𝘪𝘢𝘭 𝘷𝘪𝘦𝘸𝘴. #1 𝗧𝗵𝗲 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀 There's a difference between being AI-assisted and being AI-first. 1. AI-assisted means you're using AI tools in your existing workflows. 2. AI-first means you're designing, implementing, and evaluating AI workflows from scratch. And Data Science teams naturally progress from AI assistance to implementation. #2 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴.. 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝘀𝗼𝗳𝘁 𝘀𝗸𝗶𝗹𝗹𝘀 Coding is no longer your competitive edge. AI can do that now. Data scientists and engineers are shifting from code writers to strategic thinkers. Your competitive edge is being able to use the output of that code to drive business strategy. Data Scientists are now architects, not a coder. And there's a clear movement towards softer skills: • storytelling • creative thinking • strategic thinking #3 𝗗𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗮𝗿𝗲 𝘄𝗲𝗹𝗹-𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗱 𝗔𝗜 Data science is gaining more influence, not less. Why? Because there's an AI knowledge gap between technical teams and business stakeholders. And Data Scientists can bridge that gap, because we understand both the technical side of AI and the business side. This combination is rare. Data scientists can see where AI fits into a business process, understand the data it needs, evaluate whether it's actually working, and communicate the results. #4 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗳𝗼𝗿 𝘂𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘁𝗲𝗮𝗺𝘀 𝗶𝗻𝘁𝗼 𝗯𝗲𝗶𝗻𝗴 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 Khalifeh's team has been able to upskill quickly & effectively into become AI-first. Here is his advice on doing the same ↴ 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴. Managers go first and lead by example. They demonstrate AI usage in their own work: prep docs, 1:1 notes, agents for management tasks. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗲𝗱 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿 𝘁𝗶𝗺𝗲. Weekly and monthly blocked time on the entire team's calendar, managers and ICs, for learning and experimenting with AI tools. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Team members are expected to their managers how they used their protected time. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘀𝗵𝗮𝗿𝗶𝗻𝗴. Monthly sessions where team members show creative ways they've used AI in their day-to-day work. 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗮𝘁𝘁𝗲𝗻𝗱𝗮𝗻𝗰𝗲. Every team member gets budget to attend at least one AI conference per year, during business hours. Then they share what they learned with the team. TLDR: ↳ Your role is changing. But you're in a good spot. ↳ Focus on developing your soft skills ↳ Lead your team on AI design + implementation ♻️ Repost if you found this useful!