Innovation and Data Analytics

The idea of submerging computer servers in a liquid coolant to cut data center energy consumption by 70% is a breakthrough in sustainable tech innovation. Traditional cooling systems consume significant energy, but with non-conductive liquid coolants, it's possible to safely dissipate heat while keeping electrical circuits dry and operational. This method optimizes thermal management, capturing all the generated heat and drastically reducing the need for conventional fans and chillers. Sandia National Laboratories approach could set a new standard for energy efficiency in data centers, making them greener and more cost-effective. Florian Palatini ++

Most insurance companies don’t have a product problem. They have a 𝐬𝐢𝐠𝐧𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Trouble shows up early for customers… and late for leadership. McKinsey’s 2025 analysis shows that only a small fraction of insurers capture meaningful value from AI and the reason isn’t model quality. It’s because 𝐝𝐚𝐭𝐚 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬 across underwriting, claims, support, and policy servicing. Another study highlights that predictive analytics when actually integrated can reduce loss ratios, speed up claims, and improve risk accuracy. But most insurers never reach that stage because their systems can’t surface early patterns. So what happens? A spike in confusion calls. Customers misusing features. Renewal expectations not matching policy reality. Claim friction rising quietly for weeks. By the time these signals hit dashboards, the damage is already in motion: lower NPS, rising churn, operational load, regulatory exposure. This is why insurance needs an 𝐈𝐂𝐔 - 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐧𝐢𝐭. A team that: 1. Connects disparate data into a single, queryable layer. 2. Builds early-warning models for churn, fraud, sentiment, and claims delay. 3. Flags mismatches between expectation and experience in real time. 4. Routes insights directly into underwriting, ops, and customer teams. When insights arrive early, transformation doesn’t arrive late. And in insurance, 𝐭𝐡𝐞 𝐞𝐚𝐫𝐥𝐢𝐞𝐬𝐭 𝐬𝐢𝐠𝐧𝐚𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐨 𝐰𝐢𝐧. #InsuranceIndustry #DataAnalytics #CustomerExperience #PredictiveAnalytics

Walking through our data center this week, I realized again how important it is to experience technology where it happens. Leadership starts with understanding the details, not just making decisions from the boardroom. Together with our operations team, I gained a direct impression of how our infrastructure works in practice. Data centers are more than technical facilities. They are the foundation for digital progress and economic growth. The demands on our systems are growing rapidly as AI and cloud applications expand and remote work becomes part of everyday life. One challenge stands out. Traditional AC systems have served us well, but they are reaching their limits. In our All Electric Society factory, we already use a DC grid. By connecting solar panels and battery storage directly to servers and cooling systems, we reduce conversion losses and increase energy efficiency. This means less heat, lower operating costs, and a more stable grid. For critical infrastructure, this resilience is essential. I still remember my early days on the shop floor, solving power issues late at night. That hands-on experience shapes my decisions today. I believe in walking the floor, listening to the team, and seeing challenges up close. How do you approach energy efficiency in your operations? Have you tried DC grids or other new solutions? I am interested in your experiences and look forward to your insights. If we want a sustainable digital future, we need to rethink the basics. Integrating DC grids in data centers is not just a technical upgrade. It is a step toward a more efficient, resilient, and sustainable industry. Let’s set new standards together.

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁𝗵) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

Struggling to build a data foundation that helps you deploy AI models at scale? Regulation can help. Too often in my professional life I have heard the old adage that regulation is a blocker to innovation. In my experience, what actually impedes on innovation is uncertainty; specifically when relevant rules are missing, unclear, or poorly aligned. No doubt this was true for both the GDPR and AI Act, at least in the beginning. What is often overlooked, however, is that these laws also provide notable benefits: among others, guiding organizations how to approach data-driven innovation in a structured and sensible way. ➡️ How GDPR supports data readiness Art. 5 GDPR requires, e.g., purpose limitation, data minimization, accuracy, integrity, confidentiality, and accountability. Organizations must decide which personal data they need, why, and who is responsible. This amounts not only to a responsible but also strategic approach to handling data - and not just personal data. ➡️ How the AI Act builds on this Art. 6 AI Act links an AI system’s obligations to its intended use and impact on people’s health, safety, and fundamental rights. Art. 10 then mandates data governance requirements for high-risk AI systems, e.g., that training, validation, and test datasets are relevant, representative, complete, and documented. Providers must implement measures covering provenance, cleaning, annotation, assumptions, gap analysis, bias detection, and ongoing monitoring. These rules offer a practical blueprint for AI-ready data. ➡️ Why this matters for AI strategy A strong data foundation improves model performance, but also reveals when AI is not the right tool. A rules-based system might achieve the same outcome with less risk and less complexity. The decision when not to use AI should be part of any good AI strategy too. ➡️ What organizations should do ✅ Define the purpose of processing: What are you trying to achieve? How does this improve the status quo? What tradeoffs do you need to consider? ✅ Use Art. 5 GDPR to decide what personal data you need to achieve your processing purpose in the least intrusive way. ✅ Evaluate whether you need AI - or if a rules-based system suffices. ✅ If you do need AI, leverage the AI Act’s Art. 6 intended use test and Art. 10 data governance rules as a readiness checklist. In particular, if it looks like you would be developing or deploying a high-risk AI system, make sure you have the necessary resources to do so. ✅ Create clear roles and responsibilities along the lifecycle of data processing to continuously ensure the quality, consistency, and reliability of data. ✅ Delete data when you no longer need it. This not only saves resources, but minimizes your compliance exposure. Too often, regulation is framed as a constraint. In reality, it can help organizations plan and implement data projects in a strategic and purposeful way. #DataReadiness #AIGovernance #GDPR #AIAct #ResponsibleAI

Pharmaceutical and medical device companies face unique challenges in connecting with HCPs, patients, and stakeholders. As traditional marketing methods become less effective and privacy concerns grow, first-party data emerges as a game-changer for our industry. First-party data—information collected directly from customers with their consent—is becoming increasingly crucial for success in digital marketing. With the impending phase-out of third-party cookies, leveraging your own data will be more important than ever. But how can pharma and medical device companies harness the full potential of first-party data? A study by Boston Consulting Group (BCG) and Google revealed that while data-driven marketing can double revenue and increase cost savings by 1.6 times, only about 30% of companies are creating a single customer view across channels. Even more striking, just 1-2% are using data to deliver a full cross-channel experience for their customers. To bridge this gap and gain a competitive edge, industry leaders need to focus on three key actions: 1. Develop a Comprehensive Data Strategy - Instead of collecting data indiscriminately. This might involve prioritizing data from healthcare provider interactions, patient support programs, or clinical trial participants. Assess the value of your first-party data rigorously. Calculate associated costs and risks and develop a clear implementation roadmap. This approach not only streamlines your efforts but also helps secure buy-in from executives—crucial for successful implementation. 2. Test, Learn, and Measure - Start with a specific business case for your data. For instance, you might aim to improve adherence to a particular treatment or increase adoption of a new medical device. Define what needs to be personalized to achieve this goal. While one-to-one personalization might seem ideal, it requires significant investment and time. Focus on a narrow use case—perhaps a specific physician specialty or patient segment—and invest only in the data and technology required to test that particular case. 3. Build Robust In-House Tech Capabilities - Traditionally, pharma and medical device companies have heavily relied on agencies for marketing efforts. However, a hybrid approach may be more effective in the age of first-party data. Consider insourcing your technology stack and capabilities related to data analysis and activation. At the same time, leverage agencies for their strategic perspective, creative content, and media buying expertise. Many agencies are evolving to meet these changing needs, offering everything from à la carte services for mature brands to turnkey solutions for those just starting their data journey. By focusing on these three areas, pharmaceutical and medical device companies can unlock the full potential of their first-party data. This not only improves the customer experience, but also boosts business results. #CXStrategy #pharmaceuticals #medicaldevices #DataStrategy

Crafting a Data and Analytics Strategy That Really Resonates For many organizations, articulating the tangible value of a data strategy can be a significant challenge. It's common to default to a technology-centric approach, leading to skepticism about solving a "problem" with a "hammer". 🔵 Strategy First, Technology Second Gaining buy-in for your data and analytics vision before diving into the technical details of the operating model. This prevents stakeholders from questioning the need for proposed technology solutions. Communication is key, and it must be segmented based on your audience – whether you're educating or informing (sideways; business partners), persuading (upwards; sponsors), or instructing (downwards; D&A teams). Each approach demands different content, length, and emphasis in your presentations. 🔵 Concise, Outcome-Led Vision Your vision statement should be remarkably concise, ideally 20-40 words, deliverable as an "elevator pitch". It should clearly state how your data and analytics team contributes to the top three organizational goals, identifies the specific stakeholders you aim to help, and outlines three mechanisms for delivering value. This also includes explicitly stating what you won't focus on, ensuring clarity and preventing dilution of effort. 🔵 Align with Business Transformations and Culture To ensure relevance, your strategy must connect with ongoing major business transformations within the organization. Furthermore, addressing cultural barriers to data-driven decision-making is paramount. I suggest framing the culture as "outcome-led" / "value-driven" and "decision-centric" rather than merely "data-driven". 🔵 Broaden The Appeal and Resonate, Wider Incorporate contemporary drivers and trends (e.g. how DA& teams are responding to Generative and Agentic AI), categorizing them as technology, internal, or market/societal factors, to demonstrate your strategy's forward-looking nature. 🔵 Defining Value and Measurable Impact Prioritize your primary stakeholders (ideally three), and for each, define the top three goals your team will help them achieve. For each goal, identify three measurable metrics, creating a "metrics tree" that clearly tracks your contribution to their success. Gartner defines three core value propositions for data and analytics: 1️⃣ Utility: Providing enterprise reporting as a service for common questions. Central team, allocated budget, data warehouse, etc. 2️⃣ Enabler: Facilitating business outcomes through self-service analytics, coaching, and projects based on business cases. 3️⃣ Innovation: Driving new initiatives like AI for decision making and prescriptive analytics. Each value prop requires a different delivery model, from service desks for utility to portfolio management for innovation, and these should be aligned. Collaborating with leaders like CIO, CISO, CAIO is also crucial for innovation efforts. Develop a D&A strategy that demonstrates tangible business value.

10 learnings for the Chief Data Officer to understand about building a strategy that supports their org's AI ambitions— —synthesised from moderating numerous roundtable C-suite debates on the topic across 2025. It's an in-exhaustive list, naturally, but without these tenets it sounds like any investments made in AI will likely (continue to) disappoint. As I wrote earlier this year: ✍ "AI challenges are actually legacy data challenges." Here are the 10 lessons. Any you would add or remove? 1. Data strategies fail when they are not grounded in business value, in other words, alignment is essential, not optional. 2. Technology should follow the business strategy, not lead it. The right posture is: business problem first, data solution second. 3. Trusted, high-quality data is the foundation for AI and advanced analytics— flawed data upstream leads to flawed decisions downstream. 4. A Shift Left architecture is critical: pushing data validation, governance, and business logic earlier in the pipeline improves trust, reduces rework, and enables real-time responsiveness. 5. Shifting left also demands stronger cross-functional collaboration: source owners, engineers, analysts, and business users must work in lockstep. 6. Many organisations overinvest in emerging tech while underinvesting in data culture, literacy, and adoption. 7. Quick experimentation (e.g. hackathons, small pilots) helps reveal the “art of the possible” and build confidence without heavy upfront investment. 8. Clear, consistent data storytelling is essential for communicating value—especially to non-technical stakeholders. 9. Executive buy-in is most effective when aligned to personal objectives (e.g. cost reduction, efficiency, growth), not just IT priorities. 10. A successful data strategy must evolve with the business; it’s not a one-off initiative but a living framework that adapts over time. For a deeper analysis, take a read of the latest Data Visionaries Food for Thought roundtable article. All in partnership with Confluent. https://lnkd.in/evN74SQ5 #AI #csuite #roundtable #data #chiefdataofficer

𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐂𝐨𝐦𝐞𝐛𝐚𝐜𝐤: 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐬𝐭𝐬 𝐚𝐧𝐝 𝐕𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 This comprehensive analysis of Netflix's journey from its 2022 downturn to its current dominance offers valuable lessons. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: [1] Resilience and Adaptation are King: Netflix's turnaround demonstrates the importance of being able to adapt and evolve in a rapidly changing market. The company wasn't afraid to make bold moves, even reversing long-held positions, to stay ahead. [2] Data-Driven Decision Making: Netflix's use of data to personalize recommendations and inform content decisions highlights the power of data-driven insights. This approach is crucial for any business looking to stay competitive. [3] Strategic Foresight: The company's ability to anticipate market trends and challenges allowed it to navigate the complex streaming landscape and maintain its leadership position. Strategic planning is key to long-term success. [4] Execution Excellence: Netflix's flawless execution of its strategies, particularly the password crackdown and ad-supported tier launch, demonstrates the importance of effective implementation. [5] Customer Focus: By prioritizing understanding its customers and delivering value, Netflix built a loyal and engaged subscriber base. This customer-centric approach is essential for any business. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐀𝐫𝐭𝐢𝐜𝐥𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: The article provides a deep dive into Netflix's strategic decision-making during a critical period. It reveals the challenges and opportunities the company faced, and the specific actions it took to overcome obstacles and achieve success. This narrative is invaluable for understanding the dynamics of the entertainment industry and the broader business landscape. 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐬𝐭𝐬 𝐚𝐧𝐝 𝐕𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬: [1] Strategic Agility: The analysis showcases the importance of being adaptable and willing to make bold moves in response to market changes. [2] Data-Driven Insights: The article emphasizes the value of data in informing strategic decisions and driving business growth. [3] Valuation Considerations: By illustrating Netflix's strategic shifts and their impact on its performance, the analysis provides valuable insights for #valuation professionals. [4] Industry Analysis: The article offers a deep understanding of the streaming landscape and the challenges and opportunities faced by legacy media companies. In conclusion, this article serves as a valuable resource for anyone seeking to understand the dynamics of a successful business turnaround. It provides actionable insights and strategic lessons that can be applied across industries, making it an essential read for business strategists and valuation professionals alike. #streaming #streamingwars