Data-Driven Innovation Analysis

Many companies think they're set if they have product usage metrics and can track user engagement. But unfortunately, that's only part of the picture. The real value comes from connecting that usage data to actual business impact. The best product ops teams create the vision and ability to connect those data points. They help relate user behavior metrics to critical business outcomes like revenue, churn, and more. Imagine seeing a feature with rising usage month-over-month. Seems great, right? But what if you found that the usage spike was mainly from a customer segment you're looking to phase out... while adoption from your strategic focus segment had dropped 20%? Yikes. Having that analytical power to map product metrics to business metrics is the secret sauce. With product ops, you can scale those capabilities across the entire product org and executive team, guiding decision-making in the right direction. As Aniel Sud, CTO of Optimizely, puts it: "Product ops becomes data-driven over time, turning data into actual value." And according to Joe Peake of Featurespace, the goal is analyzing each product's revenue opportunity and ROI - not just relying on gut feelings about the market. True product insight means bringing all data together - from product usage to customer feedback to financial impacts. As Shira Bauman of Zapier notes, "Learning about the data that people care about, and partnering across data teams, is so important." With product ops connecting those dots, we get out of the "build trap" and can optimize for real outcomes. The path to successful products lies in combining engagement metrics with business performance. What's your experience been in tying product usage data to business metrics? Share your insights and lessons learned in the comments!

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

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

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

Association Rule Mining (ARM) is a widely used data mining technique to uncover relationships or patterns between items in large datasets. It is often applied in market basket analysis to identify products frequently purchased together, which aids marketing strategies. ARM algorithms were initially designed for categorical data, however they become inefficient when applied to large numerical datasets with higher dimensions. Despite Deep Learning's (DL) broad success, including its ability to learn logical rules from graph data, applying DL methods directly to ARM on transactional datasets remains largely unexplored. ARM faces inefficiency and the challenge of generating numerous rules that are difficult to interpret. Evaluating and selecting useful rules is computationally demanding and time-consuming. Explainability is key, especially for validation by human experts or automated systems. Mining association rules (ARs) from high-dimensional numerical data, such time series data from a large number of sensors in a smart environment for example, is a computationally intensive task. Despite DL's broad success, including its ability to learn logical rules from graph data, applying DL methods directly to ARM on transactional datasets remains largely unexplored. ARM faces inefficiencies and the challenge of generating numerous rules that are difficult to interpret. Evaluating and selecting useful rules is computationally demanding and time-consuming. Explainability is crucial, especially for validation by human experts or automated systems. Mining ARs from high-dimensional numerical data, such as time series data from numerous sensors in a smart environment, is a computationally intensive task. To address the challenges of rule quantity and explainability, the authors of [1] proposed an Autoencoder-based approach, 'AE SemRL,' for learning and extracting ARs from time series data using semantics. The inclusion of semantic information related to time series data sources helps facilitate the learning of generalizable and explainable ARs. By enriching time series data with additional semantic features, AE SemRL makes learning ARs from high-dimensional data more feasible. Their experiments show that semantic ARs can be extracted from a latent representation created by an Autoencoder where the proposed SemRL method has in the order of hundreds of times faster execution time than state-of-the-art ARM approaches in many scenarios. The links to the paper [1] and #Python code [2] are shared in the first comment.

𝗙𝗿𝗼𝗺 𝗥𝗼𝘄𝘀 𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗧𝗵𝗿𝗲𝗲 𝗔𝗰𝘁𝘀 Most enterprises don’t fail at collecting data. They fail at turning it into impact. Confusion between data sets, data models, and data products is one of the biggest hidden taxes on transformation programs. Let’s break it down. 𝗧𝗵𝗲 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀, 𝗥𝗲𝗰𝗶𝗽𝗲, 𝗮𝗻𝗱 𝗦𝗮𝘂𝗰𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 Data Set (The Ingredient): Rows, columns, logs, and transactions. They provide visibility but are meaningless without context. Data Model (The Recipe): Structures data into meaning - predicting churn, segmenting customers, optimizing supply chains. Intelligence, but abstract unless operationalized. Data Product (The Sauce): What users consume - a pricing dashboard, fraud detection tool, or recommendation engine. It drives action by solving business problems. Taking an example of revenue growth management - The data set has outlet details, shipments, price lists, and promotions. The model translates this into elasticity curves, promo effectiveness, and pack architecture. The product delivers actionable guidance: which packs to push, discounts to drop, promotions to double down on. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟭: 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗨𝗻𝗹𝗼𝗰𝗸𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 Data products need dedicated owners like product managers who bridge business and technical teams. They validate use cases, ensure business alignment, and champion adoption. Ownership accelerates decisions and keeps products impactful. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟮: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝗲𝗿-𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗠𝗼𝗱𝗲𝗹 Treat data products like commercial offerings. Producers focus on quality, documentation, and compliance; consumers discover and use products independently. Catalogs, self-service tools, and governance enable delivery at business velocity without sacrificing standards. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟯: 𝗖𝗿𝗼𝘀𝘀-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 Components like models, platforms, and APIs often sit in siloed teams. Leading companies form cross-functional teams that own data products end-to-end, reducing friction, accelerating innovation, and balancing enterprise consistency with business agility. 𝗧𝗵𝗲 𝗧𝗿𝘂𝗲 𝗨𝗻𝗹𝗼𝗰𝗸 When raw data, robust models, impactful products, and analytics align, data stops being a cost center and becomes a growth engine. What’s your view? Does your organization clearly differentiate between data sets, models, products, and analytics? Where are the biggest gaps or opportunities today? #DataStrategy #DataProducts #AI #Analytics #Transformation

AI & Innovation Thursday: The Hidden Heroes of AI - Data Quality When we talk about #AI in radiology, most of the spotlight shines on the algorithms: their accuracy, speed, and clinical performance. But behind every great model is something less glamorous yet absolutely essential: data quality. Poor-quality data leads to poor-quality AI. It’s as simple as that. Incomplete or mislabeled datasets can create blind spots. Lack of diversity can lead to bias and inequities in care. Inconsistent imaging protocols can limit reproducibility across sites. On the other hand, when we invest in high-quality, diverse, and well-curated data, we build AI that is: More reliable, more generalizable, more trusted by clinicians. At GE HealthCare, we often say: AI is only as good as the data it learns from. That makes radiologists, technologists, and data stewards the hidden heroes of AI innovation. The technology may be cutting-edge but its foundation is built on something timeless: doing the basics well. For my colleagues: What’s your experience: is the biggest challenge for AI in radiology today the algorithm, or the data it depends on? #AIInnovationThursday #Radiology #ArtificialIntelligence #DataQuality #Leadership #GEHealthcare

My new white paper is now available on SSRN: 𝑻𝒉𝒆 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 𝑮𝒂𝒑: 𝑯𝒐𝒘 𝑩𝒂𝒅 𝑫𝒂𝒕𝒂 𝑩𝒓𝒆𝒂𝒌𝒔 𝑩𝑰 𝒂𝒏𝒅 𝑨𝑰 𝒂𝒏𝒅 𝑯𝒐𝒘 𝒕𝒐 𝑭𝒊𝒙 𝑰𝒕 I’ve been seeing this for some time, and I wanted to formally highlight an important issue: bad data can lead to misleading BI insights and unreliable AI outputs. That risk becomes especially important in today’s AI gold rush. In this paper, I examine (with examples) how challenges in data conversion, validation, and governance limit the effectiveness of analytics and how these issues compound in practice requiring added resources and costs to fix or rework data and resolve inconsistent results. I also show how even simple analyses of a single data source can produce materially different conclusions depending on data quality and how LLMs may misinterpret inputs. I provide a practical framework for building AI-ready data pipelines and addressing these issues, with a focus on aligning with the organization’s KPIs and value-creation priorities. 📄 Read it here: https://lnkd.in/g5vzuvfy Libra Analytics Pepperdine Graziadio Business School #DataQuality #AI #BusinessIntelligence #DataAnalytics #DataGovernance #GenerativeAI #LLMs

Just wrapped up a talk at MIT Sloan School of Management on "India Class Problems" - a concept I've been deeply engaged with throughout my career. Here is the nugget 𝗗𝗲𝗳𝗶𝗻𝗶𝗻𝗴 "𝗜𝗻𝗱𝗶𝗮 𝗖𝗹𝗮𝘀𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀" India class problems are characterized by: - Vast amounts of unstructured or incomplete private data - Evolving consumer behavior with frequent changes - Expectations for free or low-cost services - Limited availability of public data on demographics and infrastructure While daunting, these challenges present opportunities for innovative solutions with global applicability. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝗥𝘂𝗿𝗮𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 At a major Indian telecommunications company serving over 300 million customers, we encountered a quintessential "India Class Problem": how to optimize telecom network deployment in rural areas with where public data on population, income etc. are limited Our innovative approach utilized AI and Google satellite imagery - the key hypothesis being, a place is as prosperous as it appears from space. We created over 100 types of labeled data, from count and size of house, types and width of the roads, vegetation, forests, water bodies, proximity to highways etc. to develop an AI model to estimate population density and prosperity levels. Such information, combined with other 3rd party data, can create a high-quality, synthetic "Prosperity Index" in Emerging Markets where income data, especially from rural areas, are almost impossible to get. The outcome was huge, we automated what previously was largely a manual process and improved our customer predictions significantly. 𝗕𝗿𝗼𝗮𝗱𝗲𝗿 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗙𝘂𝘁𝘂𝗿𝗲 𝗢𝘂𝘁𝗹𝗼𝗼𝗸 The post-COVID era is characterized by digital, remote, and automated solutions. The emerging "K-economy" favors businesses that are customer-centric, digitally adept, and data-driven. To effectively find find and utilise data, businesses should understand that there is "Intelligence Everywhere". Focusing on innovative data discovery and interpretation is critical. In this context, the role of what I fondly call a “data detective” e.g., someone who really understands the data and finds hidden clues in them, becomes as crucial as that of a data scientist. Addressing "India Class Problems" extends beyond technological innovation. It's about developing solutions that can serve billions of individuals entering the digital economy, potentially revolutionizing sectors such as digital health, climate change mitigation, and mobility. My sincere gratitude to Rob Blaine, Chloe Fang, Honey Pamnani and many others for inspiring me to discuss these ideas, especially critical for Emerging Markets. Ping me if you think such ideas inspire you! Ramesh Raskar Ayush Chopra Abhishek Singh Raj Simhan Chris Pease Chenyu Zhang Rohan Khanna MIT Media Lab Anshul Joshi #DataScience #AI #Innovation #GlobalImpact #MITMediaLab

Being data-driven is often viewed as mastering measurement and optimization—but don't leave discovery and innovation on the table! When it comes to data, an organization's first impulse is to chase certainty, relying on dashboards, precision KPIs, and refined datasets. This is an important efficiency boost, but it's important to keep in mind that breakthroughs and new business models rarely result from meticulous planning. They emerge when someone recognizes an unusual pattern or an overlooked anomaly. This accidental brilliance is precisely what modern data-driven organizations must foster in addition to their hunt for efficiency. When it comes to their use of data, most companies aren't structured for serendipity. They operate in cycles of predictability, continuously refining data to meet expectations. While this optimization generates immediate efficiency gains, it often follows the economic principle of diminishing returns—each incremental improvement costs a bit more and delivers a bit less. Genuine data-driven innovation requires spaces for "curated chaos": environments intentionally designed to surface unexpected findings. Perhaps paradoxically, this demands a high level of data maturity—robust capabilities that create a stable foundation from which exploration can safely occur. Innovation and a data-driven mindset build on the same foundation. Both require intellectual bravery, eye-to-eye interaction across hierarchies, and patience to detect subtle signals. Curated chaos isn't a call to abandon rigor; it's creating spaces where overlooked connections can naturally emerge. It means deploying analytics not merely for measurements and predictions, but as exploratory instruments—provoking questions and challenging assumptions. The most innovative data-driven companies embody such structured curiosity. They balance analytical discipline with openness to surprise. They reward thoughtful questioning as vigorously as decisive answers and recognize that breakthroughs often appear quietly within noise. While optimization often provides the comfort of predictability and quantifiable returns, discovery operates on a different economic model where small investments in exploration can yield disproportionate value. While your competitors perfect their dashboards, consider what they might be missing—the next crucial insight might not be hiding in the cleanest dataset, but in the anomalies you've initially aimed to get rid of. Don’t just optimize with your data—explore it!