Data-Driven Innovation Analysis

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 ]

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. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰

You've heard "garbage in, garbage out" a thousand times. But here's what that actually means: your fancy dashboard is only as good as the data behind it. Quantity is easy to measure—it's just Terabytes. But data quality? Quality is the hard part because it requires discipline, process, and ownership. Data quality and governance are no longer “nice-to-haves.” They define trust across the organization. → Growing demand due to privacy laws like GDPR and CCPA → Core skill required for roles like Data Engineer, Steward, and Architect → Tools like Collibra and Great Expectations now appear in almost every data job description Some numbers speak for themselves: → Data Quality Engineer roles growing 40%+ yearly → Governance Analysts earning around $80K–$120K → Chief Data Officers often crossing $200K+ Clean data isn’t just accuracy—it’s career growth and company credibility. What Good Data Quality Looks Like? Skip the theory. Here's what actually works: → Automated checks that catch issues before they spread → Validation rules that reject bad data at the source → Tracking where data comes from and where it goes → Alerts when something breaks (not after it's been broken for weeks) → Clear ownership so someone actually fixes problems Where in the real world it shows up? 👉This isn't abstract. Here's where data quality makes or breaks things: → Finance: Try explaining bad compliance data to auditors → Healthcare: Patient records need to be right, every time → Retail: Wrong inventory data means lost sales or wasted stock → ML projects: Your model is only as smart as your training data The Real Talk: Data quality feels boring until it's missing. Then suddenly everyone cares. It's not sexy work. Nobody celebrates when pipelines validate correctly. But it's the foundation everything else sits on. Gartner says organizations with formal data governance will see 30% higher ROI by 2026. As data engineers, that’s our call to design solutions that "𝘥𝘰𝘯’𝘵 𝘫𝘶𝘴𝘵 𝘮𝘰𝘷𝘦 𝘥𝘢𝘵𝘢, 𝘣𝘶𝘵 𝘮𝘰𝘷𝘦 𝘵𝘳𝘶𝘴𝘵." Honestly, I feel it's probably more if you count all the fires you don't have to fight. 👉 Folks I admire in this space - George Firican Dylan Anderson Piotr Czarnas 🎯 Mark Freeman II Chad Sanderson Here's a crisp guide on Data Quality & Governance for data engineers! 👇 What's the most annoying, recurring data quality issue you've had to fix lately? I'll go first: dates stored as strings. 🤦♂️

This is how Anthropic decides what to build next—and it's brilliant. Instead of endless spec documents and roadmap debates, the Claude Code team has cracked the code on feature prioritization: prototype first, decide later. Here's their process (shared by Catherine Wu, Product Lead at Anthropic): Step 1: Idea → Prototype Got a feature idea? Skip the spec. Build a working prototype using Claude Code instead. Step 2: Internal Launch Ship that prototype to all Anthropic engineers immediately. No polish required—just functionality. Step 3: Watch & Listen Track usage religiously. Collect feedback actively. Let real behavior, not opinions, guide decisions. Step 4: Data-Driven Prioritization - High usage + positive feedback → roadmap priority - Low engagement or complaints → back to iteration This "prototype-first product shaping" flips traditional product development on its head. Instead of guessing what users want, they're measuring what users actually use. The beauty? They're dogfooding their own tool to build their own tool. The feedback loop is immediate, honest, and impossible to ignore. The takeaway: Your best product decisions come from real user behavior, not theoretical frameworks. Sometimes the fastest way to validate an idea isn't a survey or interview—it's a working prototype.

#TeachMeTuesday How much innovation are we missing if we only look at the R&D line in financial statements? 👉 Result: a lot! 📄 In a newly published Research Policy article (👉 https://lnkd.in/enDMWuVA), Neophytos Lambertides, Marina Magidou, and Anna Emilia Maruska, Ph.D. , the authors ask a simple question: what if part of the innovation signal is hiding in plain sight — in what firms say, not only in what they report numerically? Evidence in the paper then shows that over 10% of firms reporting zero or missing R&D expenditures nonetheless exhibit clear signals of innovation activity. 🧠 What this implies for innovation measurement The results reinforce a broader message: innovation is increasingly intangible, distributed, and poorly aligned with traditional reporting categories. Relying narrowly on reported R&D risks underestimating innovation — especially in services, digital-intensive sectors, and firms innovating through processes rather than products. 🏛️ What could better measurement look like? Building on the paper’s contribution, three complementary directions stand out: 🔔 Systematic use of narrative disclosures to capture latent innovation activity beyond formal R&D 🔔Broader “total innovation investment” concepts, combining R&D with other innovation-relevant intangible expenditures 🔔 Richer output indicators, integrating patents with trademarks, designs, and market-based innovation signals 🌍 Link to global policy work These insights align closely with the measurement philosophy of the WIPO, which combines traditional and non-traditional indicators to better capture innovation in all its forms.

New streaming data sources and AI’s use of them have revitalized the real-time event stream processing market and boosted revenue. Product leaders can use this research to assess how real-time data, analytics and AI can enhance and differentiate their offerings and adjust their roadmaps to leverage this potential. Gartner recommends that product leaders: 🔵 Allocate a portion of the engineering budget to evaluate the accessibility and applicability of real-time data and analytics that can impact desired business outcomes. Do so by experimenting with new data streams and event logs to understand their ability to inform and adapt products and services. 🔵 Work with engineering teams to design an architecture that can leverage real-time event stream data by identifying technology and requisite technology partnerships to consume the data within the reasonable confines of your product’s existing architecture. 🔵 Demonstrate the positive effect on decision quality and outcomes that result from including real-time contextual data in your products and services. Do so by measuring the accuracy of models that either predict outcomes or recommend actions, as well as embedding the best models in decision workflows. I asked Kevin R. Quinn, Vice President, Analyst - Technical Product Management, Gartner why he believe this research matters: 💡 "AI is accelerating every aspect of business. Decisions can’t just be based on what happened, but need to account for what is happening right now." 💡"Real-time data enables timely decision-making, enhances responsiveness, improves operational efficiency, and provides a competitive edge in rapidly changing environments." Our research shows how the market for real-time streaming data is changing, and how it is more accessible and relevant for providers and end-users, than ever before. Check out the insights from Kevin R. Quinn and myself (David Pidsley) which is exclusively available to Gartner clients who are product leaders subscribed to our "Emerging Technologies and Trends Impact on Products and Services" research. ▶️ "Emerging Tech: Revolutionize Your Products With Real-Time Data and AI" [Published 31 January 2025] 🔗 https://lnkd.in/ev7nk82R (requires client login) #DecisionIntelligence #RealTime #Data #AI #RealTimeData #StreamingData #StreamingAnalytics #StreamAnalytics #EventStream #EventStreamProcessing

𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐭𝐡𝐚𝐭 𝐠𝐥𝐨𝐛𝐚𝐥 𝐦𝐨𝐛𝐢𝐥𝐞 𝐝𝐚𝐭𝐚 𝐭𝐫𝐚𝐟𝐟𝐢𝐜 𝐢𝐬 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐭𝐨 𝐫𝐞𝐚𝐜𝐡 𝐚 𝐬𝐭𝐚𝐠𝐠𝐞𝐫𝐢𝐧𝐠 77.5 𝐞𝐱𝐚𝐛𝐲𝐭𝐞𝐬 𝐩𝐞𝐫 𝐦𝐨𝐧𝐭𝐡 𝐛𝐲 2027? This explosion of data presents both a challenge and a massive opportunity for telecommunication companies. But are they equipped to handle it? The telecommunications industry is undergoing a seismic shift. Why should you care? Because this transformation impacts how we connect, communicate, and experience the digital world. A recent study showed that poor network performance can lead to a 30% increase in customer churn. 👉 In today's hyper-connected world, customer expectations are higher than ever, and telcos need to leverage data to stay ahead of the curve. 👉 Traditional data management systems struggle to keep pace with the sheer volume, velocity, and variety of data generated by modern telecom networks. Sifting through massive datasets to gain actionable insights is like finding a needle in a haystack. 👉 This makes it difficult to optimize network performance, personalize customer experiences, and develop innovative new services. Telcos need a new approach to data management to unlock the true potential of their data. 𝐓𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧? 👉 Deutsche Telekom, one of the world's leading telecommunications providers, is leading the charge by designing the telco of tomorrow with BigQuery. 👉 By leveraging BigQuery's powerful data warehousing and analytics capabilities, Deutsche Telekom is able to ingest and analyze massive datasets in real time. This enables them to gain valuable insights into network performance, customer behavior, and market trends. 👉 They can now proactively identify and resolve network issues, personalize offers and services for individual customers, and develop new revenue streams. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 👉 Real-time Insights: BigQuery enables real-time analysis of massive datasets, allowing telcos to react quickly to changing network conditions & customer needs. 👉 Improved Customer Experience: By understanding customer behavior and preferences, telcos can personalize services and offers, leading to increased customer satisfaction and loyalty. 👉 Innovation & Growth: Access to rich data insights empowers telcos to develop innovative new services & explore new business models. 👉 Scalability & Flexibility: Cloud-based solutions like BigQuery offer the scalability and flexibility needed to handle the ever-growing data demands of the telecommunications industry. This journey highlights the transformative power of data in the telecommunications industry. By embracing cloud-based data solutions, telcos can unlock valuable insights, improve customer experiences & drive innovation. The future of telecom is data-driven, and companies that embrace this reality will be the leaders of tomorrow. Follow Omkar Sawant for more. #telecommunications #bigdata #cloud #digitaltransformation #datanalytics

𝗧𝗵𝗲 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 🚀 Building an AI product is more than just training a model and somehow rolling it out—it’s about integrating it into an experience that has a (validated) purpose. I kick off my AI PM 101 course with the AI Product Development Lifecycle concept, here’s an (overly simplified) breakdown of this process for my followers: ✨ 1. Ideation: It all starts with mapping out AI’s unique capabilities to the right problem. What pain points can AI uniquely solve? Use AI where it adds the most value, and avoid using it for problems that simpler solutions can handle. ✨ 2. Opportunity: Assess the market and determine if the idea is an experience your target persona & market need, and whether it supports a scalable business model. This is where you evaluate not just the experience, but also how AI’s ability to personalize, predict, or automate can create differentiation. Also consider AI’s infrastructure demands—will you need specialized hardware, or can it scale? ✨ 3. Concept & Prototype: Work with your xfn partners to scope out the details.. data requirements, the right model(s), architecture, experiments, even design and map out the end-to-end experience. This step also involves assessing trade-offs i.e. balancing technical feasibility vs delivering early user value. ✨ 4. Training & Development: Model training! Train the model and integrate it onto an experience to assess whether it meets the MVQ. Think about performance & product metrics, and dogfood to validate if the experience adds immediate value from day 1. ✨ 5. Testing & Analysis: Testing in AI involves continuous learning/evaluating model outputs. Validate against real-world data, monitor for bias or drift, and use this feedback to iterate. ✨ 6. Roll-out: If everything looks good, launch by making the experience accessible to users. The models might require ongoing tuning and monitoring post-launch to adapt to new data so you may need to retrain or tweak the model as you gather more real-world usage data, and this might involve further iterations before finding the right product-market fit. #aiproductmanagement <><><><><><><><><><><><><><><><> Follow Marily Nika, Ph.D for AI Product Management & Leadership education/certifications. Best way to support my work is if you like & share 🔄 my content.

Why do so many #AI projects fall short of expectations? A recent MIT report highlights a critical factor: #data quality. Even the most sophisticated AI models can’t outperform the data they’re trained on. Incomplete, biased, or outdated datasets can undermine progress, while accurate, representative, and well-governed data unlocks transformative innovation. At Merck Group, we understand that data quality isn’t just a technical necessity – it’s a strategic imperative. Across Life Science, Healthcare, and Electronics, we’re committed to developing data-driven solutions that meet the highest standards of accuracy and reliability. By prioritizing this foundation, we ensure our AI initiatives deliver meaningful value for patients, partners, and society. As we continue to harness the power of AI, one truth remains clear: better data drives better outcomes. via Forbes https://lnkd.in/dNb3pJRY

(Part 3 of my series: The Boardroom Guide to AI-Ready Data Strategy) Traditional Data Governance was built for an era of static reports and predictable workflows. But the moment you introduce Generative AI and autonomous agents, the entire risk landscape shifts. In this new world, bad data isn’t just a quality issue, it is a reputational, regulatory, and financial threat. If your governance model is still focused on locking down access and enforcing compliance checklists, you are operating as the Department of No. Modern AI Governance requires a different philosophy: The Two-Sided Governance Model 🛡 Defensive (The Shield): • Regulatory compliance • PII masking & privacy • Access control (RBAC/ABAC) • Model risk assessments This keeps us safe and compliant. ⚔ Offensive (The Sword): • Real-time data lineage • Data quality scoring • Metadata enrichment • Policy versioning and model attribution This gives AI the context it needs to behave reliably. Why Metadata Matters More Than Ever: LLMs reason on context. If your metadata is outdated or missing, your AI will confidently generate wrong answers, outdated policies, or biased decisions. RAG without metadata is just a search engine wearing a suit. This is no longer governance as a cost centre. This is governance as a business enabler, the safety harness that lets us move fast without falling off the cliff. As CAIOs and CDOs, the responsibility is to build governance systems that accelerate innovation, not block it. #AIGovernance #ResponsibleAI #RiskManagement #DataPrivacy #EnterpriseRisk #GenAI #DataLeadership