Using Analytics to Measure Productivity

Managing a business with yesterday’s data is like driving while looking in the rearview mirror. A few weeks ago, I shared how we’re using AI to drive better outcomes for our partners and their merchants. But generating meaningful insights takes more than just smart tools — it requires a shift in mindset. At NMI, we’re moving from 𝘳𝘦𝘢𝘳𝘷𝘪𝘦𝘸 𝘮𝘪𝘳𝘳𝘰𝘳 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 to 𝘸𝘪𝘯𝘥𝘴𝘩𝘪𝘦𝘭𝘥 𝘮𝘦𝘵𝘳𝘪𝘤𝘴: real-time signals that help us actively steer the business forward, not just analyze where we’ve been. As part of this shift, we’ve developed multi-point partner health scores that give us a holistic, dynamic view of customer health across our ecosystem. To enable this, we’ve: •Integrated analytics into our channel account dashboards (and update them monthly) •Blended signals from product usage, billing, support interactions, and customer sentiment •Invested in streaming data to spot lags in transactions and provide more consultative, timely support Real-time insights allow us to act on what we see. These insights feed into our regular partner health check-ins, and when warning signs appear, we proactively reach out to help partners course-correct. Windshield metrics not only help us manage our business more effectively, they also enable us to better support our partners. Over time, our goal is to evolve these analytics into a solution our partners can offer to their own merchants, strengthening every link in the value chain — from NMI to our partners, and from our partners to their customers. Moving towards windshield analytics is just one way we’re continuously evolving to enhance the partner experience. How does your organization approach data? Are you still operating on “rearview” insights? Or have you adopted real-time analytics? Let me know in the comments! 👇 #Fintech #Metrics #RealTimeInsights #TechLeadership #DataDrivenLeadership

It’s Monday morning. You’re staring at a swamp of dashboards, knowing you’ll have to painfully wade through them to make sense of business performance — what happened, why it happened, and what to do next. Consider a different approach: • Model your metrics and their relationships using a metric or KPI tree. • Connect these trees to your underlying data for context. • Incorporate dimensionality so you can slice and dice metrics across segments and hierarchies. • Leverage automation — products like HelloTrace can trace (no pun intended) these trees, identify key drivers, surface insights, and proactively deliver them to you. Instead of spending four hours performing a few shallow analyses, imagine having a 24/7 analytics agent working across your organization — augmenting decision-making by continuously surfacing what matters most. To get inspired, here are a few metric tree examples across different business models, showing how this approach scales across industries and teams. https://lnkd.in/gZmj6Ynt

When Industry 4.0 meets your mud engineer, expect a leap in efficiency and insight. With real-time data, machine learning, and automation, routine tasks like mud checks are transformed into continuous monitoring, allowing engineers to focus on strategic decisions rather than manual checks. Collaborative intelligence combines their expertise with AI’s power, creating a digital twin of your mud engineer’s workflow. This enables proactive management of solids removal, dilution economics, and waste—driven by real-time diagnostics and predictive analytics. Engineers gain deeper visibility into well conditions, optimizing drilling performance and reducing Non-Productive Time (NPT). Industry 4.0 empowers mud engineers with the tools needed to make informed decisions swiftly, setting a new standard in digital fluids management that enhances operational performance and well integrity.

The Contrarian Truth About Real-Time Analytics Peter Thiel’s famous question to founders, “What important truth do very few people agree with you on,” is designed to expose contrarian insights. Contrarian insights change the world. A year into working at StarTree, I’ve realized an insight that flies in the face of conventional wisdom - real-time insights are actually less expensive than stale ones. At first, that statement feels backwards. For decades, real-time analytics were dismissed as costly luxuries, reserved for only the most mission-critical use cases or requested by those who didn’t have a plan to act faster with accelerated insights. Batch processing, with its scheduled jobs and nightly updates, was seen as the pragmatic, “cheap enough” alternative. The logic was simple: real-time must mean more compute, premium storage, more complexity, and ultimately more expense. But the opposite is true if you design the system with real-time in mind from the start. Take Apache Pinot™, the open-source #OLAP datastore created at LinkedIn and now adopted by companies like Uber, Stripe, DoorDash, Together AI, Slack, and 1000s of other companies. Pinot was built for high-volume, low-latency analytics at scale. When you design for real-time from the start, the architecture looks very different. In Apache Pinot™, ingestion from streaming sources like #Kafka makes data immediately queryable—there’s no waiting for transformation or batch reloads. The design center is sub-second queries at scale: innovative indexes like the Star-Tree, avoiding shortcuts like lazy loading, and reconciling upserts continuously rather than bolting on before or after load. By treating freshness, speed, and scale as first principles, Pinot avoids the extra layers and workarounds that creep into systems retrofitted for real-time. Further, its architecture eliminates the need for the patchwork of systems many companies cobble together: a batch database for analytics, plus a key-value store for fast serving, stitched together by brittle pipelines. That approach doesn’t just add latency, it multiplies infrastructure costs. The cost savings are tangible. Uber reduced infrastructure spend by more than $2 million per year after consolidating real-time analytics onto Pinot. They also cut CPU cores by 80% and data footprint by 66%. That’s not the profile of an expensive system, it’s the profile of a smarter one. The truth is, stale data is expensive. Every additional batch pipeline, every duplicate data store, every ETL job running on a schedule is a tax you pay for not solving the problem at its root. Real-time data done right doesn’t just deliver fresher insights faster, it does so at lower cost and with far less operational overhead. So when someone tells you “real-time is too expensive,” remember: that’s the conventional wisdom. The contrarian truth is that stale data costs more. And the companies that discover this secret early are the ones that win.

Do we need real-time data for everything?” That’s a question I’ve heard from multiple Chief Data Officers. In one of our discussions, we dug into the realities of real-time analytics—where it truly adds value and where it’s unnecessary. The consensus? Real-time data is transformative, but only in the right scenarios. Here are three situations where it delivered immediate results: 1. Capturing Sales at the Right Moment “We saw customers abandon their carts at checkout,” the CDO explained. By implementing real-time analytics, the company triggered personalized offers during hesitation, boosting conversions by 22%. “Without acting instantly, those sales would’ve been gone.” 2. Preventing Costly Disruptions The CDO highlighted how their logistics team tracked fleet performance in real time to avoid delays and downtime. “We saved thousands by rerouting deliveries immediately after detecting bottlenecks.” Real-time monitoring kept operations smooth and efficient. 3. Stopping Fraud Before It Escalates “Fraud detection was our biggest win,” they noted. Real-time analytics flagged unusual transactions as they happened, blocking fraudulent activity before funds were lost. “It wasn’t just about money—it protected our reputation.

🔹 Real-Time Data Processing with Snowflake: Struggling to get real-time insights from your data? Snowflake’s architecture supports real-time data processing, enabling you to access and analyze data as soon as it’s generated. Let’s explore how Snowflake can power your real-time analytics. 🚀 Imagine this: You’re running a retail business and need up-to-the-minute sales data to make quick decisions. Traditional data warehouses can’t keep up, but Snowflake provides a solution that ensures your data is always fresh and ready for analysis. 🌟 Here’s how Snowflake enables real-time data processing: 1. Snowpipe for Continuous Data Loading: Snowpipe automatically loads data into Snowflake as soon as it arrives in your cloud storage. This ensures that your data is always up-to-date without manual intervention. ⏱️ 2. Integration with Streaming Platforms: Snowflake integrates seamlessly with streaming platforms like Apache Kafka and Amazon Kinesis, allowing you to ingest and process streaming data in real-time. 🌐 3. Instantaneous Querying: With Snowflake, you can query your data as soon as it’s ingested, enabling real-time analytics and decision-making. Run complex queries on fresh data without delays. ⚡ 4. Data Sharing: Share real-time data securely with stakeholders within and outside your organization. Snowflake’s data sharing capabilities ensure that everyone has access to the latest data. 🤝 5. Real-Time Dashboards: Connect Snowflake with BI tools like Tableau, Power BI, and Looker to create real-time dashboards. These dashboards provide up-to-the-minute insights, helping you monitor and respond to changes quickly. 📊 6. Scalable Compute Resources: Snowflake’s architecture allows you to scale compute resources independently to handle real-time data processing workloads efficiently. Scale up during peak times to ensure seamless performance. 📈 Why does this matter? Real-time data processing enables you to make timely decisions, improve customer experiences, and stay ahead of the competition. Snowflake’s capabilities ensure that you can handle real-time data seamlessly and efficiently. 💡 Pro Tip: Use Snowpipe in combination with Snowflake’s integration capabilities to automate your real-time data pipelines, ensuring continuous and efficient data flow. How do you currently handle real-time data processing? Have you explored Snowflake’s real-time capabilities? 💬 Share your thoughts or experiences in the comments below! 🚀 Ready to unlock the power of real-time data processing with Snowflake? Follow my profile for more insights on data engineering and cloud solutions: [https://lnkd.in/gVUn5_tx) #DataEngineering #Snowflake #DataWarehouse #CloudComputing #RealTimeData #Analytics

Launchmetrics implemented customer-facing real-time analytics with Databricks and Estuary in days (link below). Here are some key takeaways for any real-time analytics project. For those who don’t know Launchmetrics, they help over 1,700 Fashion, Lifecycle, and Beauty businesses improve brand performance with analytics built on Databricks and Estuary. 1. Have data warehouses on your short list for real-time analytics Yes. Databricks SQL is a data warehouse on a data lake. And yes, you can implement real-time analytics on a data warehouse. Over the last decade improved query optimizers, indexing, caching, and other tricks have helped get queries down to low seconds at scale. There is still a place for high-performance analytics databases. But you should evaluate data warehouses for customer-facing or operational analytics projects. 2. Define your real-time analytics SLA Everyone’s definition of real-time analytics is different. The best approach I’ve seen is to define it based on an SLA. The most common definition I’ve seen are query performance times of 1 second or less, the "1 second SLA”. Make sure you define latency as well. The data may not need to be up to date. 3. Choose your CDC wisely Launchmetrics was replacing an existing streaming ETL vendor in part because of CDC reliability issues. It’s pretty common. Read up on CDC (links below) and evaluate carefully. For example, CDC is meant to be real-time. If you implement CDC where you extract in batch intervals, which is what most ELT technologies do, you stress out a source database. It does cause failures. SO PLEASE, evaluate CDC carefully. Identify current and future sources and destinations. Test them out as part of the evaluation. And make sure you stress test to try and break CDC. 4. Support real-time and batch You need real-time CDC and many other real-time sources. But there are plenty of batch systems, and batch loading a data warehouse can save money. Launchmetrics didn’t need real-time data yet, though they knew they would. So for now they stream from sources and batch-load Databtricks. Why? It saves them 40% on compute costs. They can go real-time with the flip of a few switches. 5. Measure productivity Yes. Launchmetrics saved money. But productivity and time to production was much more important. Launchmetrics implemented Estuary in days. They now add new features in hours. Pick use cases for your POC that measure both. 6. Evaluate support and flexibility Why do companies choose startups? It’s not just for better tech, productivity, or time to production. Some startups are more flexible, deliver new features faster, or have better support. Every Estuary customer I’ve talked to has listed great support as one of the reasons for choosing Estuary. Many also mentioned poor reliability and support were reasons they replaced their previous ELT/ETL vendor. #realtimeanalytics #dataengineering #streamingETL

📊 𝗦𝘁𝗶𝗹𝗹 𝗺𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗳𝗮𝗰𝘁𝗼𝗿𝘆 𝘄𝗶𝘁𝗵 𝘆𝗲𝘀𝘁𝗲𝗿𝗱𝗮𝘆’𝘀 𝗱𝗮𝘁𝗮? Many manufacturers rely on daily or weekly production reports to understand performance. These reports made sense when operations moved slower. Today, machines generate high-frequency data every second, yet decisions are often delayed by hours or even days. That delay has a real cost. 📉 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘀𝗵𝗼𝘄𝘀   • Studies from McKinsey and other industry analyses confirm that real-time monitoring and predictive analytics significantly reduce unplanned downtime and maintenance costs.   • Static reports compress complex machine behavior into a few lagging KPIs, masking early warning signals that could prevent quality losses or line stoppages. 🤖 𝗘𝗻𝘁𝗲𝗿 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜  AI agents do not just report performance. They continuously monitor production signals, perform root-cause analysis automatically, and recommend or trigger corrective actions while production is still running. Documented manufacturing deployments show incident resolution times dropping from minutes to seconds, with measurable improvements in overall equipment effectiveness. 📄 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿  Static reports explain what went wrong yesterday.  Agentic AI helps you fix problems before they become losses. If you are rethinking production intelligence for modern factories, the article breaks down:  ✔ Why batch reports are failing  ✔ How continuous monitoring changes operations  ✔ How AI agents turn data into action Read the full article to see how leading factories are moving beyond static reporting. #SmartManufacturing #IndustrialAI #FactoryAutomation #Industry40 #ManufacturingLeadership ------------------------     ✅ Follow me on LinkedIn at https://lnkd.in/gU6M_RtF to stay connected with my latest posts.   ✅ Subscribe to my newsletter “𝑫𝒆𝒎𝒚𝒔𝒕𝒊𝒇𝒚 𝑫𝒂𝒕𝒂 𝒂𝒏𝒅 𝑨𝑰” https://lnkd.in/gF4aaZpG to stay connected with my latest articles.   ✅ Please 𝐋𝐢𝐤𝐞, Repost, 𝐅𝐨𝐥𝐥𝐨𝐰, 𝐂𝐨𝐦𝐦𝐞𝐧𝐭, 𝐒𝐚𝐯𝐞 if you find this post insightful.   ✅ Please click the 🔔icon under my profile for notifications!