Data-Driven Innovation Analysis

Had to share the one prompt that has transformed how I approach AI research. 📌 Save this post. Don’t just ask for point-in-time data like a junior PM. Instead, build in more temporal context through systematic data collection over time. Use this prompt to become a superforecaster with the help of AI. Great for product ideation, competitive research, finance, investing, etc. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰ TIME MACHINE PROMPT: Execute longitudinal analysis on [TOPIC]. First, establish baseline parameters: define the standard refresh interval for this domain based on market dynamics (enterprise adoption cycles, regulatory changes, technology maturity curves). For example, AI refresh cycle may be two weeks, clothing may be 3 months, construction may be 2 years. Calculate n=3 data points spanning 2 full cycles. For each time period, collect: (1) quantitative metrics (adoption rates, market share, pricing models), (2) qualitative factors (user sentiment, competitive positioning, external catalysts), (3) ecosystem dependencies (infrastructure requirements, complementary products, capital climate, regulatory environment). Structure output as: Current State Analysis → T-1 Comparative Analysis → T-2 Historical Baseline → Delta Analysis with statistical significance → Trajectory Modeling with confidence intervals across each prediction. Include data sources. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

🚀 Now publicly available 🚀 The Data Innovation Toolkit! And Repository! (✍️ coauthored with Maria Claudia Bodino, Nathan da Silva Carvalho, Marcelo Cogo, and Arianna Dafne Fini Storchi, and commissioned by the Digital Innovation Lab (iLab) of DG DIGIT at the European Commission) 👉 Despite the growing awareness about the value of data to address societal issues, the excitement around AI, and the potential for transformative insights, many organizations struggle to translate data into actionable strategies and meaningful innovations. 🔹 How can those working in the public interest better leverage data for the public good? 🔹 What practical resources can help navigate data innovation challenges? To bridge these gaps, we developed a practical and easy-to-use toolkit designed to support decision makers and public leaders managing data-driven initiatives. 🛠️ What’s inside the first version of the Digital Innovation Toolkit (105 pages)? 👉A repository of educational materials and best practices from the public sector, academia, NGOs, and think tanks. 👉 Practical resources to enhance data innovation efforts, including: ✅Checklists to ensure key aspects of data initiatives are properly assessed. ✅Interactive exercises to engage teams and build essential data skills. ✅Canvas models for structured planning and brainstorming. ✅Workshop templates to facilitate collaboration, ideation, and problem-solving. 🔍 How was the toolkit developed? 📚 Repository: Curated literature review and a user-friendly interface for easy access. 🎤 Interviews & Workshops: Direct engagement with public sector professionals to refine relevance. 🚀 Minimum Viable Product (MVP): Iterative development of an initial set of tools. 🧪 Usability Tests & Pilots: Ensuring functionality and user-friendliness. This is just the beginning! We’re excited to continue refining and expanding this toolkit to support data innovation across public administrations. 🔗 Check it out and let us know your thoughts: 💻 Data Innovation Toolkit: https://lnkd.in/e68kqmZn 💻 Data Innovation Repository: https://lnkd.in/eU-vZqdC #DataInnovation #PublicSector #DigitalTransformation #OpenData #AIforGood #GovTech #DataForPublicGood

Fresh off press: I sat down with Jeff Baxter, NetApp to discuss about the announcements from INSIGHT to go deep on the new announcements today and why they matter for teams building with AI. We covered what this means in practice for customers. The NetApp AI Data Engine moves data to the right place at the right time, manages metadata, lineage, and versioning so work is reproducible, and adds AI powered ransomware detection so teams can ship with confidence. It runs as one platform across on premises and all major clouds, so hybrid stays simple and cost aware. Highlights from our conversation: • From data to innovation. A single data platform that reduces handoffs and cuts wait time for data scientists and engineers. • Reproducible AI by design. Metadata, lineage, and versioning are first class so you can rerun, compare, and promote models with clarity. • Security that keeps pace with AI. AI powered ransomware detection plus built in controls to protect sensitive data without slowing teams down. • One control plane. On prem and across major clouds with consistent operations, cost visibility, and policy enforcement. My take: • This is about operational discipline, not hype. Reproducibility and lineage are the difference between a demo and a dependable AI program • Security has to be native to the data platform. If it is bolted on later, teams hesitate and AI work stalls • Hybrid is the real world. A unified approach across on prem and clouds reduces complexity and keeps options open If you are scaling AI and want fewer blockers between data and outcomes, this will help. #data #ai #insight2025 #netapp #theravitshow

DMAIC–KEY TOOLS AND FORMATS: 1. DEFINE Goal: Define the problem, project goals, and scope. Key Activities: Create a Project Charter Identify Voice of Customer (VOC) Define CTQs (Critical to Quality elements) Create SIPOC Diagram (Suppliers, Inputs, Process, Outputs, Customers) Tools & Formats: SIPOC diagram Project Charter Problem Statement Goal Statement VOC Analysis Stakeholder Analysis Example: Problem: Customers unhappy with 5-day delivery time Goal: Reduce delivery time to 3 days Scope: Only domestic shipping, not international 2. MEASURE Goal: Understand the current performance and gather baseline data. Key Activities: Identify key performance indicators (KPIs) Collect data on process performance Validate measurement system (MSA) Develop data collection plan Tools & Formats: Data Collection Plan Control Charts Process Flow Diagrams Measurement System Analysis (MSA) Histogram, Run Charts Example: Measured average delivery time = 5 days 20% orders delayed beyond promised date 3. ANALYZE Goal: Identify root causes of the problem using data analysis. Key Activities: Analyze collected data Identify patterns, variations, and causes Validate root causes Tools & Formats: Root Cause Analysis (5 Whys) Fishbone Diagram (Ishikawa) Pareto Chart (80/20 rule) Regression Analysis Cause and Effect Matrix Scatter Plot Example: Found issues: Poor inventory control Manual order entry Departmental miscommunication 4. IMPROVE Goal: Implement and test solutions to eliminate root causes. Key Activities: Brainstorm improvement ideas Conduct pilot tests Implement best solutions Assess risk (FMEA) Tools & Formats: Brainstorming Sessions FMEA (Failure Mode and Effects Analysis) Poka-Yoke (Error Proofing) DOE (Design of Experiments) Process Simulation Before & After Comparisons Example: Actions taken: Automated inventory system Integrated order tracking Real-time communication tools Result: Delivery time reduced to 3.5 days 5. CONTROL Goal: Sustain improvements and monitor long-term performance. Key Activities: Develop control plans Standardize improved processes Monitor KPIs Provide training and documentation Tools & Formats: Control Charts Control Plan Document Standard Operating Procedures (SOPs) Process Audit Checklists Visual Management Tools (dashboards) Example: Monthly delivery performance review Dashboard showing real-time shipment status Staff trained on new SOPs

AI is only as powerful as the data that fuels it. We often celebrate breakthrough models, but the real transformation comes when trusted, well-governed, and accessible data meets intelligent systems. As NetApp insight-fully puts it: “AI is built on a foundation of data… the AI capabilities of your organisation are only as competent as the data that fuels it.” (NetApp) 🔎 Think about it: Data without AI is underused potential, sitting idle in silos. AI without quality data is unreliable, creating hallucinations or flawed insights. Together, high-quality data and AI can redefine decision-making, customer experience, and innovation. 📊 A recent study published on arXiv  highlights that improving data quality, governance, and metadata management has a bigger impact on the accuracy of AI outputs than simply training larger models. It’s proof that smarter foundations beat bigger models. 🏗️ Organisations that invest in: -- Robust data governance (standards, compliance, trust) -- Modern data architectures (graphs, catalogues, vector stores) -- Data democratisation (secure but easy access for teams) …are the ones that will unlock AI’s full potential. ✨ AI is not just about models; it’s about building trustworthy, human-centric systems powered by reliable data. The real question isn’t which LLM to use, but how we build the right governance and context around our data. #AI #Data #DataQuality #DataGovernance #DigitalTransformation #Innovation

Data Quality isn't boring, its the backbone to data outcomes! Let's dive into some real-world examples that highlight why these six dimensions of data quality are crucial in our day-to-day work. 1. Accuracy:  I once worked on a retail system where a misplaced minus sign in the ETL process led to inventory levels being subtracted instead of added. The result? A dashboard showing negative inventory, causing chaos in the supply chain and a very confused warehouse team. This small error highlighted how critical accuracy is in data processing. 2. Consistency: In a multi-cloud environment, we had customer data stored in AWS and GCP. The AWS system used 'customer_id' while GCP used 'cust_id'. This inconsistency led to mismatched records and duplicate customer entries. Standardizing field names across platforms saved us countless hours of data reconciliation and improved our data integrity significantly. 3. Completeness: At a financial services company, we were building a credit risk assessment model. We noticed the model was unexpectedly approving high-risk applicants. Upon investigation, we found that many customer profiles had incomplete income data exposing the company to significant financial losses. 4. Timeliness: Consider a real-time fraud detection system for a large bank. Every transaction is analyzed for potential fraud within milliseconds. One day, we noticed a spike in fraudulent transactions slipping through our defenses. We discovered that our real-time data stream was experiencing intermittent delays of up to 2 minutes. By the time some transactions were analyzed, the fraudsters had already moved on to their next target. 5. Uniqueness: A healthcare system I worked on had duplicate patient records due to slight variations in name spelling or date format. This not only wasted storage but, more critically, could have led to dangerous situations like conflicting medical histories. Ensuring data uniqueness was not just about efficiency; it was a matter of patient safety. 6. Validity: In a financial reporting system, we once had a rogue data entry that put a company's revenue in billions instead of millions. The invalid data passed through several layers before causing a major scare in the quarterly report. Implementing strict data validation rules at ingestion saved us from potential regulatory issues. Remember, as data engineers, we're not just moving data from A to B. We're the guardians of data integrity. So next time someone calls data quality boring, remind them: without it, we'd be building castles on quicksand. It's not just about clean data; it's about trust, efficiency, and ultimately, the success of every data-driven decision our organizations make. It's the invisible force keeping our data-driven world from descending into chaos, as well depicted by Dylan Anderson #data #engineering #dataquality #datastrategy

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

The impact of AI on research & development & innovation could well be the big story. An excellent report from Arthur D. Little, "Eureka! On Steriods", explores the potential in detail. A summary of some key insights: 🤖 AI complements researchers, acting as a knowledge manager, hypothesis generator, and decision assistant. It works best as an orchestrator, integrating simulations, Bayesian models, and generative AI while keeping humans in the loop. Companies leveraging AI effectively have seen up to 10x productivity gains, proving its transformative impact in R&D&I. 📊 In AI-driven R&D&I, well-structured, high-quality data is the true competitive advantage, as algorithms are becoming commoditized. Preparing and cleaning data may take 18-24 months initially, but each iteration accelerates future progress, making robust data management the key to unlocking AI’s full potential. 🧠 AI augments rather than replaces researchers, freeing time for higher-value tasks. It enables breakthroughs by tackling problems once deemed unsolvable, like optimizing nutrition plans or predicting protein structures. As AI evolves, it is shifting from a mere assistant to a "planner-thinker", helping make complex strategic decisions based on weak signals. ⚡ Fast, iterative deployment trumps waiting for perfection, while high-quality, structured data remains the foundation for AI impact. Organizations must prioritize AI investments wisely—choosing to buy, fine-tune, or build models based on needs—while balancing trade-offs like data acquisition vs. synthesis and precision vs. recall. Upskilling teams, embedding AI talent, and aligning with IT ensure smoother adoption, while early wins and continuous monitoring keep AI models effective and trusted. 🔮 The trajectory of AI in R&D&I depends on technical reliability, public and researcher trust, and cost-effectiveness. Six future scenarios range from AI revolutionizing every aspect of innovation ("Blockbuster") to limited, low-risk applications ("Cheap & Nasty"). Organizations must prepare for uncertainty by investing in compute power, data sharing, governance, and workforce training, ensuring resilience no matter how AI evolves. There's a lot more and a lot more detail in the report, link in comments. AI in innovation is a core theme in my work, I'll be sharing more insights coming up.

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.