Data Analysis and Decision-Making

Gartner just declared the semantic layer a "non-negotiable foundation" for AI. After years of the semantic web community being treated as a niche academic pursuit, the world's most influential analyst firm has elevated semantic layers to critical infrastructure. As my friend Juan Sequeda reported from the Gartner D&A Summit, 44% of data and analytics leaders have already implemented one. Another 48% plan to by 2027. 🔵 "Semantic" Without Semantics Unfortunately I suspect that many of these organisations still think a "semantic layer" is a set of KPI definitions used for driving BI dashboards. A cleaner interface to the same old data warehouse. That is pre-AI thinking. It is not real semantics. 🔵 Real Semantics Live in an Ontology An ontology gives you more than definitions. It gives you relationships, hierarchies, constraints, inference. A formal model of what your organisation knows and how those things connect. That is rich. That is powerful. That is grounded in first-order logic. And yes, you can absolutely generate a BI dashboard from it. But that is not the point. 🔵 AI Can Generate Your Dashboards The real point is enabling AI. A system grounded in a well-built ontology does not need a static dashboard. It can answer questions you have not thought to ask yet. It can reason across domains. It can generate the visualisation you need, on demand, from the connected knowledge underneath. The dashboard is a by-product. The ontology is the asset. 🔵 Avoiding the Next Mistake As the noise around this grows, so will the people trying to monetise it. Products with ontology-driven semantic layers will abound. But this is only the very first phase of a much deeper transformation. To prepare yourself for what comes next, your ontologies need to be based on open standards. They need to be interoperable. Portable. Vendor-independent. That is not a product. That is the semantic web. ⭕ Gartner - Rethink Semantic Layers to Support the Future of Analytics and AI: https://lnkd.in/eQFd5R-W ⭕ Juan Sequeda - Gartner D&A 2026 Takeaways: https://lnkd.in/erY3H-eh ⭕ Ontology is the Map: https://lnkd.in/em_NtDw4 🔗 Want to explore this further? Let's talk: https://lnkd.in/eDd-5hpV

Today in Nature Medicine we report that AI can predict 130 diseases from 1 night of sleep 🛌. We trained a foundation model (#SleepFM) on 585K hours of sleep recordings from 65K people—brain, heart, muscle & breathing signals combined. AI learns the language of sleep! Paper: https://lnkd.in/grpRD3Qp Open source code: https://lnkd.in/g_MqFCm6 Participants are linked to their EHR. SleepFM predicts risks for diverse diseases--including dementia, heart failure, kidney disease, and stroke--years before clinical diagnosis. It substantially outperforms using demographic features, which are strong predictors. SleepFM uses a new architecture to integrate multimodal sleep time-series data. CNNs learn local features, transformers aggregate information across time + channels, and leave-one-modality-out contrastive learning trains robust representations. This design generalizes across sites and diverse populations. We spend 1/3 of our lives sleeping but it has been underexplored with AI. Most work focuses on narrow tasks like sleep staging and apnea detection. By learning a holistic representation of sleep, SleepFM opens new doors for studying the science and medicine of sleep. Truly wonderful collaboration with Emmanuel JM Mignot's lab, led by Rahul Thapa and Magnus Ruud Kjaer! Thanks to all the awesome collaborators: Bryan He, Ian Covert, Hyatt Moore, Umaer Hanif, Gauri G., M Brandon Westover, Poul Jennum, Andreas Brink-Kjær 👏

Excel is more than just a spreadsheet tool — it’s one of the most powerful and widely used platforms for organizing, analyzing, and interpreting data. From finance and operations to HR, marketing, and data analytics, Excel remains a core skill that drives better decision-making across every industry. What makes Excel so important? 📌 Data Organization: Excel helps structure raw data into clear, understandable formats, making it easier to manage and analyze. 📌 Powerful Functions & Formulas: With formulas for calculations, lookups, text processing, financial modeling, and dynamic arrays, Excel turns complex tasks into quick, automated solutions. 📌 Data Analysis Made Simple: Functions like SUM, VLOOKUP, INDEX-MATCH, COUNTIF, FILTER, and PivotTables enable users to extract insights and solve real business problems. 📌 Automation & Productivity: Excel reduces manual work, improves accuracy, and saves countless hours through formulas, conditional formatting, and built-in tools. 📌 Universal Skill: No matter your role — student, analyst, manager, or beginner — Excel is a foundational tool that strengthens problem-solving and analytical thinking. Sharing this Excel Formula Cheatsheet to help learners, professionals, and aspiring analysts upgrade their skills and work smarter with data. Mastering Excel isn’t optional anymore — it’s a career advantage. #Excel #DataAnalytics #Productivity #LearningEveryday #CareerGrowth #MicrosoftExcel

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

𝐋𝐞𝐭’𝐬 𝐬𝐨𝐥𝐯𝐞 𝐚 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐚𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫, If you're preparing for Data or Product Analyst roles — this is exactly the type of case round you should practice. It’s not about jumping into queries — it’s about structured thinking. 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: You're a Data Analyst at a food delivery company like Zomato or Swiggy. In the past 15 days, there’s been a 5% drop in active customers. You’re asked: “What could be the reason behind this churn, and how would you investigate it?” 𝐒𝐭𝐞𝐩 𝟏: Clarify the Problem Before solving, ask: Does “churn” mean no orders? Or no activity at all? Is it across all users or specific cohorts (new users, Prime, etc.)? Any specific regions more impacted? These questions help you define the problem — not just guess a solution. 𝐒𝐭𝐞𝐩 𝟐: Structure Your Investigation Break down your thinking into: 🔹 Internal Factors (platform-level issues) App crashes, login issues → Check crash logs, screen exits Delivery delays → Compare SLA metrics over time Key restaurant unavailability → Partner downtime, stockouts Reduction in discounts → Drop in coupon usage or redemptions Checkout issues → Cart-to-payment funnel drop-offs 🔹 External Factors (outside control) Weather/strikes/curfews → Regional impact data Seasonality → Historical trends from previous years Competitor activity → Market-level discounts or ad campaigns 𝐒𝐭𝐞𝐩 𝟑: Go Deep on the Root Cause Let’s say the team confirms: “Yes, we reduced discount campaigns.” Now prove it with data: Analyze sessions reaching the “Apply Coupon” page Compare order completion rate before & after discount application Study cart abandonment after coupon screen Look at this metric over last 15 days vs previous months This validates the impact of discounts on churn — using real funnel data. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲? Case rounds like this aren’t about correct answers. They’re about how you think, how you structure messy problems, and how well you connect business context with data. This is the exact type of round I’ve seen in companies like Zomato, Blinkit, Flipkart, Meesho, etc. So if you’re preparing — don’t stop at SQL or dashboards. Practice thinking like a business analyst. If you want more real case problems like this — drop a “Case” in comments. I’ll share a few more from my interview experience.

Stop loading data into ChatGPT and asking for insights. It is lying to you. An LLM cannot find "truth." It does not know your business context. It does not understand your data. It fabricates plausible narratives and reinforces your confirmation bias. You don't need an insight generator. You need a sparring partner. The LLM's true power is in stress-testing your ideas. This is how you shatter bias. This is how you find the real insight, not just the one you were looking for. Use the "Challenge-Code-Verify" cycle. - The Challenge: State your hypothesis. Command the LLM to act as a skeptical statistician and find 3 ways you are wrong. - The Code: Direct the LLM to produce the exact code (Python/R) or formula (Excel/Sheets) to test its counter-argument. - The Verification: Run the code. Look at the chart. Make the call. This is how you partner with the LLM to sharpen your human abilities -- intuition, creativity, novelty. Asking your LLM for insights is like asking your sparring partner to fight for you. It will get knocked out. Its job isn't to win the match. Its job is to reveal your weaknesses, sharpen your skills, perfect your form, and force you to be better. Spar with your LLM so that when its showtime, you are the one who lands the knockout. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

📌 Why Do Data Analysts Spend So Much Time in Excel? As data analysts, we often use powerful tools like SQL and Python for data extraction and analysis. But let's be honest most of us still spend a significant amount of time in Excel. Why is that? ▪️ Simplicity & Accessibility: Everyone from entry-level analysts to top executives can work with Excel. It’s easy to share and understand. ▪️ Data Visualization: Quick charts, pivot tables, and conditional formatting make it perfect for fast insights. ▪️ Flexibility: Whether it's quick calculations, scenario modeling, or ad-hoc reports, Excel handles it all with ease. ▪️ Integration: You can pull data from multiple sources and use Excel as a final step for presentation or summary. But Excel isn't just basic! 📌 To truly master Excel as a data analyst, here are some key skills you should be familiar with: 🔆 Pivot Tables: For summarizing and analyzing large datasets. 🔆 VLOOKUP, XLOOKUP, INDEX/MATCH: Essential for merging and looking up data across sheets. 🔆 Data Cleaning: Using functions like TRIM, CLEAN, TEXT TO COLUMNS, and Remove Duplicates. 🔆 Conditional Formatting: Highlighting trends and anomalies quickly. 🔆 Advanced Formulas: Nested IFs, COUNTIF(S), SUMIF(S), and array formulas. 🔆 Power Query & Power Pivot: Handling larger datasets and complex transformations. 📌 Never Underestimate Excel! It's more than just a spreadsheet tool it’s a versatile powerhouse that complements advanced tools. ✏️ What’s your go to feature in Excel? Let’s discuss! 🌐If you found this helpful, like and repost to reach others who might need it. ✳️Follow for more daily content!

Small variations in prompts can lead to very different LLM responses. Research that measures LLM prompt sensitivity uncovers what matters, and the strategies to get the best outcomes. A new framework for prompt sensitivity, ProSA, shows that response robustness increases with factors including higher model confidence, few-shot examples, and larger model size. Some strategies you should consider given these findings: 💡 Understand Prompt Sensitivity and Test Variability: LLMs can produce different responses with minor rephrasings of the same prompt. Testing multiple prompt versions is essential, as even small wording adjustments can significantly impact the outcome. Organizations may benefit from creating a library of proven prompts, noting which styles perform best for different types of queries. 🧩 Integrate Few-Shot Examples for Consistency: Including few-shot examples (demonstrative samples within prompts) enhances the stability of responses, especially in larger models. For complex or high-priority tasks, adding a few-shot structure can reduce prompt sensitivity. Standardizing few-shot examples in key prompts across the organization helps ensure consistent output. 🧠 Match Prompt Style to Task Complexity: Different tasks benefit from different prompt strategies. Knowledge-based tasks like basic Q&A are generally less sensitive to prompt variations than complex, reasoning-heavy tasks, such as coding or creative requests. For these complex tasks, using structured, example-rich prompts can improve response reliability. 📈 Use Decoding Confidence as a Quality Check: High decoding confidence—the model’s level of certainty in its responses—indicates robustness against prompt variations. Organizations can track confidence scores to flag low-confidence responses and identify prompts that might need adjustment, enhancing the overall quality of outputs. 📜 Standardize Prompt Templates for Reliability: Simple, standardized templates reduce prompt sensitivity across users and tasks. For frequent or critical applications, well-designed, straightforward prompt templates minimize variability in responses. Organizations should consider a “best-practices” prompt set that can be shared across teams to ensure reliable outcomes. 🔄 Regularly Review and Optimize Prompts: As LLMs evolve, so may prompt performance. Routine prompt evaluations help organizations adapt to model changes and maintain high-quality, reliable responses over time. Regularly revisiting and refining key prompts ensures they stay aligned with the latest LLM behavior. Link to paper in comments.

I don’t care if you’re a Data Scientist, Marketer, Business Analyst, Financial Analyst, Project Manager, or even in HR—if you work in the corporate world, you need to know Excel. Too many people brush it off as “just a spreadsheet tool” while underestimating how much of the business world still runs on it. It’s Everywhere – Every company uses Excel in some way. It’s the backbone of reporting, data analysis, financial planning, project tracking, and decision-making. It Makes You Self-Sufficient – Instead of waiting for a data team to pull numbers for you, Excel lets you analyze and visualize data yourself - Being able to manipulate data, create reports, and visualize insights sets you apart in any role. And yes, there's different Levels of mastery (Depending on Your Job) -> Basic: Formatting tables, sorting/filtering, basic formulas for planning, tracking, and reporting. -> Intermediate: Pivot tables, VLOOKUP/XLOOKUP, INDEX/MATCH, basic data visualization, scenario planning. -> Advanced: Macros, automation, Power Query, complex modeling, and integrating with other data tools. Who Needs It? (Basically, Everyone) ✅ Data Scientists – Sometimes, your stakeholders don’t want Python notebooks—they want an Excel report. ✅ Marketers – Analyzing campaign performance, budgeting, forecasting trends. ✅ Business Analysts – KPI tracking, business case modeling, dashboard creation. ✅ Financial Analysts – Budgeting, financial modeling, variance analysis. ✅ Project Managers – Timeline planning, resource allocation, risk assessment. You don’t need to be an Excel wizard, but knowing how to use it effectively will make you more valuable—regardless of your industry.

Linking health data to location data sounds straightforward. It took years of specialist work to make it possible without compromising either the data or the people behind it. We were brought in to work on one of the most ambitious data integration programmes in the UK public sector. The platform was designed to help researchers and analysts discover, join, and analyse data. Previously, that data existed in separate silos across government departments. The challenge was not a shortage of data. The UK holds extraordinary datasets covering health, labour markets, demographics, and geography. The challenge was that each dataset had been built with different definitions, geographies, and privacy requirements. Linking them without careful architecture risked exposing personal information. It also produced analysis that was fundamentally unreliable. Neither was acceptable. Here's what we delivered. We built privacy-preserving anonymisation workflows for every dataset ingested into the platform. Each workflow included differential risk controls and automated disclosure checks. Not as a compliance layer applied afterwards. As a core architectural component built into the ingestion process from the start. We implemented a reference data hub that unified geospatial codes, health lookups, labour market data, and demographic classifications. Everything was brought into a single governed catalogue. This solved a problem that had prevented meaningful cross dataset analysis for years. Every dataset now carries a common location spine. This allowed health outcomes to be examined alongside labour market data and census boundaries. The analysis could be performed using consistent geographies that did not drift between sources. We built APIs enabling analysts to combine datasets in ways that were previously manual, error prone, and slow. The platform was designed to scale to billions of records as participation from additional departments grows. The outcomes. Researchers can now discover and analyse previously siloed data to accelerate evidence based policy design. Robust anonymisation and governance frameworks reduced the risks associated with data sharing. As a result, departments that previously held back are now participating. Geospatial alignment means every analysis carries consistent national and regional context rather than fragmentary local snapshots. The hardest data problems are rarely about storage or processing power. They are about the invisible barriers between datasets. Different codings, different boundary definitions, different privacy thresholds. Building the infrastructure that lets disparate data speak a common language is painstaking, specialist work. But it is what transforms individual datasets into genuine analytical capability. What siloed data in your organisation could generate transformative insight if it could reliably connect to other sources? #DataIntegration #PrivacyPreserving #PublicSector