Pranjali Dashpute

In US hospitals dont get paid on the number of days a patients stay in the hospital but based on the cause of admission. this can be a good use case for hospitals and even add Gen AI to do data anaylsis what type of patients stayed longer and what measures can be taken to provide those service early

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Short-termism in Marketing is a growth killer. When advertising and marketing is overly focused on short-run efficiency, growth suffers. It leads to prioritizing immediate response and insufficiently valuing demand creation, which is critical to business growth. The key strategic challenge is to balance investment across advertising that both creates demand and converts demand. And, to do both in a provable and measurable way, so that we can invest with confidence and optimize towards both. #Google-Ads is developing new innovations in this area. Our approach is two fold. We measure how advertising creates demand by moving consumers along their path to purchase, and eventually to incremental new conversions in the long run. And, we leverage AI, which has been trained on this measurement, to optimize towards growth. To see how we are thinking about this, see our latest #Think-With-Google piece here: https://lnkd.in/gSB9bMU5 We welcome collaboration with industry and partners to push this important agenda forward. And stay tuned for more from us on this!

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It is hard to understand Fabric's long-term strategy. On one hand, it was smart to build it around PBI, the leading BI platform. On the other hand, the key to Power BI's success in dethroning Tableau was its' extremely affordable licensing. Competitive licensing is not an element that Fabric's always-on pricing model can boast of at current rates, especially when the likes of Databricks, Snowflake, and other rising platforms are around. Perhaps Microsoft will be content with Fabric always being the 3rd/4th best option?

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We’re learning a lot at Snowflake, very quickly, about what it actually takes to deliver high quality, high-performance agentic analytics in the real world. Rajhans Samdani and I collaborated to write this latest post, which captures some of those learnings, especially around the emerging “context layer” and why it is becoming foundational for trustworthy data agents. https://lnkd.in/g-8TMcmD What’s been most interesting is how consistently the same themes show up across customer engagements. Whether it’s Semantic Views, Cortex Analyst, our Agent Framework, or Snowflake Intelligence, the same pattern keeps reinforcing itself. You can’t get to reliable, scalable outcomes without a strong, well-defined layer of context that connects data, business meaning, and user intent. This is one of those moments where the pace of change is hard to overstate. The space is evolving quickly, and we’re seeing ideas move from early experimentation into real customer impact in a matter of weeks. Being able to take what we’re learning directly from customers, feed that back into the platform, and then see it improve results is what makes this work so exciting right now. If you’re thinking about how to build or scale agentic analytics, I hope you’ll find this perspective useful.

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In the CPG space, too many conversations around data stop at reporting what happened. Units, dollars, distribution, we can all pull those charts. But retailers don’t need partners who just tell them the past. They need partners who help shape the future. That’s where the real value of investing in data comes in. Not to check a box, but to: -Support smarter sales strategies by identifying where incremental growth is possible. -Anticipate retailer needs instead of reacting to gaps. -Become a trusted advisor who can connect brand performance to category growth. When brands show up with insights that align to a retailer’s priorities, they prove their worth. It’s not about more numbers, it’s about better answers. Answers that position you as a partner, not a vendor. The CPG brands that win are the ones that use data to tell a compelling story: here’s where we are, here’s what’s next, and here’s why it grows your category. That’s how you move from presenting data to driving strategy. And that’s where brands earn their place at the table.

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Unlocking the Power of Change Data Capture (CDC) in Snowflake 🚀 In today’s data-driven world, speed and accuracy are everything. Businesses no longer have the luxury of waiting hours—or even minutes—for their data pipelines to refresh. Real-time insights are the new competitive edge, and that’s where Change Data Capture (CDC) in Snowflake comes into play. 🌐 What is CDC in Snowflake? Change Data Capture (CDC) is a technique that tracks and captures changes—INSERTS, UPDATES, and DELETES—in your data sources. Instead of reloading entire datasets, CDC ensures that only the changed rows are processed. In Snowflake, CDC is powered by Streams and Tasks: Streams: Record row-level changes in a source table. Tasks: Automate the execution of SQL statements to process those changes on a schedule.Together, they create a seamless pipeline for incremental data processing. ⚡ Why Does CDC Matter? Without CDC: You reload entire tables, which is costly and time-consuming. With CDC: You only process the delta (the changed data), making pipelines faster, cheaper, and more efficient. This efficiency is critical for: ETL/ELT pipelines Data replication across systems Real-time analytics and dashboards AI/ML model training with fresh data 🔑 Key Benefits of Snowflake CDC Scalability: Handles massive datasets without performance bottlenecks. Cost Optimization: Reduces compute and storage costs by avoiding full reloads. Flexibility: Works across diverse data sources and use cases. Reliability: Ensures data integrity with transactional consistency. 🛠️ Example Use Case Imagine a retail company tracking customer orders. Instead of reprocessing millions of rows daily, Snowflake CDC captures only the new or updated orders. This enables near real-time dashboards for sales teams and accurate demand forecasting for supply chain managers. 🚀 The Future of Data is Incremental As organizations scale, the need for real-time, cost-effective, and reliable data pipelines will only grow. Snowflake’s CDC capabilities are not just a technical feature—they’re a strategic enabler for modern data-driven enterprises.

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In the past couple weeks, I read a post on Avinash Kaushik's blog about the shifting from SEO to AEO (Answer Engine Optimization). I was really curious about this, so I investigated a bit.   Google affirms that the AI Overviews are driving higher quality clicks to websites - https://lnkd.in/dZty8T_q. Although they make this claim, media and publishers are going through really tough times regarding the demonetization of their pages. The traffic volume is going down and they attribute this to AI completely taking the traffic awayy from their pages - https://lnkd.in/dr3tE77F - Pew Research confirms this, although Google claimed that the base of research done by Pew isn't sufficient to draw ant conclusion - https://lnkd.in/dyTUhA_a. The past week, I had the opportunity to hear firsthand from a publisher based in Portugal that they are facing difficult times. Their traffic levels are decreasing, and producing content is becoming more difficult because of this. But if we don’t have content, we don’t have access to information and therefore, no AI 🧐   There must be a change in this matter so that media and publishers can thrive by writing their content but this content must be correctly handled by AI. Some changes are also being made on Channel Grouping in GA4 data. But it's not enough. What can we do now? Follow Carl Fernandes's guidelines - https://lnkd.in/dDbpTCMv and build strong measurement governance so we can track our business and make the right decisions. We are facing a fast-paced environment, and the best we can do is have solid foundations that can handle fast changing times 🤠

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AI won't replace analysts. But analysts who use AI as a thinking partner will replace those who don't. Three of the sharpest data people I know just launched a course to help you become that analyst. Sravya (Senior Manager, Data Science @ Grammarly), Hai Guan (Head of Data @ Ontra), and Shane Butler (Principal Data Scientist @ Ontra) created AI Analytics for Builders on Maven. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐜𝐨𝐮𝐫𝐬𝐞 𝐭𝐞𝐚𝐜𝐡𝐞𝐬: → Translate vague product questions into clear, testable hypotheses → Define primary, secondary, and guardrail metrics with AI → Break down KPI movements through segmentation and trend analysis → Pressure-test your assumptions and edge cases → Turn analysis into clear, actionable recommendations 𝐓𝐡𝐞 𝐝𝐞𝐚𝐥: 🔥 5 early bird spots at $180 (normally $1,200) 🎟️ Everyone else who applies gets 40% off ⏰ Applications close in 24 hours I've worked with these folks. They're sharp, practical, and actually care about teaching skills that stick. If you're in data, product, or analytics, this is worth a look. 👉 𝐀𝐩𝐩𝐥𝐲 𝐡𝐞𝐫𝐞: https://lnkd.in/g7AHwSYY ♻️ Repost to help someone in your network level up their analytics game

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A Seasoned #DataScience Professional (Retail) | Simplifying #Data #AI #ML through real retail use cases | Host of “Truth wit Tuesdays 🚀” (Goes live Every Tues 9:30 AM IST) Last time we spoke of #Datalineage and why is it important to have mind map for every organization imbibed in their SOP's. Today we speak of other aspect of Data governance called "#DataAudit trail" Lets understand the Why's Why is Data audit required in first place? 1.To ensure Truth , Transparency & Accountability in how data is handled within an org 2. Enables Data Scientists to work on reliable data , enable faster debugging, support reproducibility & build trust with Stakeholders 3. Business can make better decision, have faster issue resolution & spread trust in analytics to executives & on field How does it help? 1.Data integrity & Accuracy: Keeps a track of all changes, who made them , when & what was modified 2.Compliance & Regulatory: Keeps Highly sensitive data or not in check to pass through some industry -country specific regulations (Like GDPR, HIPAA, SOX, RBI etc) 3.Accountability : Helps Establish clear responsibilities and data actions (create, update, delete , share) 4.Operational & Business Continuity: Enabling rollback /recovery incase of error /fraud/corruption What impact does Data audit create? 1. Forms Strong backbone of Data lineage & Metadata Management 2. Simplifies process for organization to understand how data flows through systems & how it is transformed over time 3. Detects unauthorized check ins strengthening organizations data security posture In short : Data audit is like CCTV camera for your data , it records every change, when something goes wrong , you can replay and find truth. #DataIntegrity #DataDrivenLeadership #EnterpriseTransformation #DigitalTransformation #TruthwitTuesdays

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