Using Data Analytics To Drive Scalable Innovations

GenAI has taken the world by storm and entered the boardrooms, exec suites and labs of most major firms. However the question of how to effectively enable for impact and scale these capabilities is not discussed enough and most have yet to overcome this challenge. My co-authors (Joe Caserta, Holger Harreis, Nikhil Srinidhi and Dr. Asin Tavakoli) and I have identified seven actions that data leaders should consider as they move from experimentation to scale. These include: 1) Let value be your guide. CDOs need to be clear about where the value is and what data is needed to deliver it. 2) Build specific capabilities into the data architecture to support the broadest set of use cases. Build relevant capabilities (such as vector databases and data pre- and post-processing pipelines) into the existing data architecture, particularly in support of unstructured data. 3) Focus on key points of the data life cycle to ensure high quality. Develop multiple interventions—both human and automated—into the data life cycle from source to consumption to ensure the quality of all material data, including unstructured data. 4) Protect your sensitive data, and be ready to move quickly as regulations emerge. Focus on securing the enterprise’s proprietary data and protecting personal information while actively monitoring a fluid regulatory environment. 5) Build up data engineering talent. Focus on finding the handful of people who are critical to implementing your data program, with a shift toward more data engineers and fewer data scientists. 6) Use generative AI to help you manage your own data. Generative AI can accelerate existing tasks and improve how they’re done along the entire data value chain, from data engineering to data governance and data analysis. 7) Track rigorously and intervene quickly. Invest in performance and financial measurement, and closely monitor implementations to continuously improve data performance Happy reading. #data #genai #datascience #ai #analytics #mckinsey

As we look back on the last few years of ambitious analytics initiatives in B2B, the emerging narrative isn't about the 30x ROI from analytics COEs anymore—it's about pragmatic solutions, realistic implementations, and sustainable growth. A common theme, underscored by former AI/ML executive and current hedge fund manager Pratik Kodial's insights, concerns last-mile delivery (i.e., adoption and impact, with a wide gap between strategic analytics initiatives and actual end-user uptake). Despite successful AI/ML-enabled commercial analytics deployments across functions like Pricing, Supply Chain, and Marketing, actual ROI was often negative. Many Analytics/ Data Science teams set out with a broad scope influenced by high expectations from CXOs, hoping to address various business challenges through AI/ML. However, this often leads to an overcommitment that might impress on paper and make for a good section on the Annual Report but needs to improve substantially in practice. The crucial lesson here is the importance of focused, smaller-scale projects directly influencing Revenue and Gross Profit drivers. For Analytics leaders, the challenge is dual: Balancing the pressure to engage in transformational, high-visibility projects against smaller projects they know will deliver immediate, measurable value to commercial teams. It is imperative to spearhead practical, scalable analytics solutions that stakeholders will adopt that will demonstrably impact the bottom line. For those new to leading Analytics or Data Science teams, consider this approach: 1. Narrow Focus: Select fewer initiatives with a high potential impact on key financial metrics. Work your way down the Income Statement, and those areas will be your most significant opportunities to attack with AI/ML-enabled solutions and strategies. 2. Stakeholder Engagement: Ensure that projects are supported by senior executives at all levels, fostering broader buy-in. 3. Expert Partnerships: Differentiate between what can be outsourced and what should be developed internally, leveraging experienced external firms where beneficial. 4. Collaborative Development: Engage a core team from various organizational levels to build solutions that are as much 'theirs' as 'yours.' Value-Driven Development: Delay coding until the problem and its value are fully understood and broken down into manageable parts. Areas for early focus include Price Optimization, Customer Churn Reduction, Cross-Sell Optimization, Promotion and Discount Management, and Procurement/Logistics Optimization. These areas promise immediate returns and build a strong foundation for more extensive, transformative projects. Dive deeper into this approach in our previous LinkedIn article, and subscribe to join over 3,500 revenue management and commercial analytics professionals who regularly read our content. https://lnkd.in/eszpvrp4 #revenue_growth_analytics

*𝑆𝑖𝑔ℎ* Yet again, I hear another company excitedly talking about implementing AI—integrating it, scaling it, “revolutionizing everything”—and yet they gloss over the need for a robust data strategy. It takes all my energy not to pull my hair out as I cringe, listening to the words. But instead of yelling into the void, I’ve learned a better approach: I ask questions. Good ones. The kind that make leaders pause and realize that AI without solid data foundations is just a very expensive experiment. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐥𝐢𝐤𝐞: 1) What percentage of your data is truly usable—normalized, contextualized, indexed, and properly mapped? 2) How much of your data is “dark” (produced but unused), and what’s your plan to leverage it? 3) Do you have a defined data governance and data management framework, or is it mostly ad hoc? 4) What’s your process for ensuring data accuracy, completeness, and relevance for AI models? 5) How scalable is your data infrastructure to support AI at an enterprise level? 6) If AI solutions depend on a continuous flow of clean data, how confident are you that your processes can deliver that over time? This is when the lightbulb flickers. Because here’s the reality: You already produce more data than you know what to do with. And yet, no one is asking whether your data is reliable, clean, and strategically aligned. Oh, and let’s not forget—you’re probably not even collecting the right strategic data yet to unlock AI’s full potential. AI doesn’t live in isolation. It thrives on organized, high-quality data. Your first step to scaling AI shouldn’t be building models—it should be building a foundation: ✅ 𝐃𝐚𝐭𝐚 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ✅ 𝐃𝐚𝐭𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 ✅ 𝐃𝐚𝐭𝐚 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 ✅ And, most importantly, a 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. 𝐒𝐨 𝐛𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐝𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐀𝐈, 𝐚𝐬𝐤 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟: “If AI is the engine of innovation, do we even have the fuel to power it?” (Trust me, the answer might surprise you.) ******************************************* • 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!