Data Analysis Skills Training

Your data platform is live. The dashboards are running. The reports are being generated. And yet the decisions feel exactly the same as before. This is more common than most organisations acknowledge. And the reason is almost never the platform. A large enterprise came with this exact problem. Snowflake was live. Microsoft Power BI was deployed. Six months in leadership was still making decisions based on gut feeling and outdated spreadsheets. The technology was working. The organisation was not using it right. Three things were broken beneath the surface. Data was being collected. But nobody had defined what decisions it was supposed to support. Every business unit had its own metrics. Same numbers. Different definitions. Zero alignment. Reports were being built for visibility. Not for action. The platform was answering questions nobody was actually asking. The fix was not technical. It was strategic. We started with the decisions first. Mapped every key business decision to a data output. Standardised metric definitions across every business unit. Rebuilt reporting around actions not observations. Connected every dashboard directly to a business outcome the leadership team owned. Within three months decision cycles dropped by half. The board had clarity. The teams had direction. The platform had not changed. The thinking behind it had. Data platforms do not improve decisions. The strategy behind them does. If your organisation is sitting on data and still not moving faster that gap is solvable. Let Discuss in the comments. #DataAnalytics #BusinessIntelligence #DigitalTransformation #DataStrategy

Business Intelligence (BI) organizations that want to raise their visibility and impact can benefit from following a value ladder strategy. While value ladders come to us from the world of marketing, they make sense to use if you think of BI orgs as being service providers. While there are many perspectives on value ladders, I particularly like this definition. "A value ladder is a marketing strategy that is designed to take customers on a journey from a low-priced entry-level offer to a high-priced premium product." Next, we need a conceptual mapping of offers to the world of BI. The following conceptual framework does this nicely. Descriptive Analytics - What happened in the business last month, last quarter, last year, etc. Diagnostic Analytics - Why things happened last month, last quarter, last year, etc. Predictive Analytics - What is likely to happen in the future. Prescriptive Analytics - How to optimize the business. Traditionally, Descriptive Analytics has been the bulk of BI service offerings. Don't get me wrong on this. When I talk to technical leaders about analytics, I clarify that you can't skip Descriptive Analytics. Putting it the context of this post, BI orgs have no value ladder without Descriptive Analytics. Unfortunately, I often see that Descriptive Analytics is where the value ladder stops. BI orgs can raise their visibility and impact by moving up the value ladder. The next rung on the value ladder is Diagnostic Analytics. BI orgs are uniquely positioned to deliver Diagnostic Analytics offerings. For example, BI orgs often build dashboards (Descriptive Analytics) that can export data to Microsoft Excel. Why is exporting to Excel a common ask? Because users want to analyze data. Here's where BI orgs can deliver a higher-value offering - teaching and mentoring the organization on Diagnostic Analytics using Excel. This isn't nearly as difficult as it sounds. For example, BI orgs can start by teaching/mentoring using Excel's powerful charting features to analyze data visually. Later, the offering can include the mighty process behavior chart. BTW - Disproportionate value from this rung of the value ladder is common. It is advisable to spend much time here before moving on. The next rung on the value ladder is Predictive Analytics. While this rung is all the rage on the LinkedIn Feed, BI orgs should take a practical approach. The approach is to teach/mentor Excel users how to analyze data using techniques like linear and logistic regression. This rung of the value ladder also has a second aspect. BI orgs can offer data analysis services with predictive models in scenarios where Excel is the wrong tool. Does your BI org take a value ladder approach? Stay healthy and happy data sleuthing! #businessintelligence #analytics #businessanalytics #dataanalytics #excel

📉 Why Do Linear Models Fail to Fit a Linear Trend with a Change Point? When fitting a linear regression to a time series with a linear trend, the results are often surprisingly good. But what happens when the underlying trend is still linear—yet includes a change point? In practice, a single linear regression will usually introduce bias. If you use that model for forecasting, the error becomes systematic and typically grows with the forecast horizon (assuming the most recent trend continues). 🤔 Why does this happen? The mathematical intuition - A linear trend with one or more change points effectively turns the series into a shape that closely resembles a convex function. From basic properties of convex functions, a straight line can intersect a convex curve at most twice. After the second intersection, the distance between the line and the curve grows as time moves forward—exactly what we observe as increasing forecast error. 🧠 A more intuitive explanation (via linear regression properties) Simple linear regression has a few key constraints: ➡️ It is a weighted average of all observations ➡️ It must pass through the point defined by the mean of x and the mean of y When a change point is present, these constraints force the model to “compromise” between different regimes. As a result, the fitted line struggles to represent either trend accurately—especially near the boundaries—leading to biased fits and poor extrapolation. The plot below illustrates this effect clearly 👇 🛠️ A practical fix A simple and effective solution is piecewise regression with knot features, which allows the model to estimate separate linear trends before and after the change point—without abandoning interpretability. Want to learn more about time series analysis and forecasting? 📬 Subscribe to 𝐓𝐡𝐞 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐞𝐫 newsletter: https://lnkd.in/gwsWSD_d Follow Rami Krispin for more content on AI, MLOps, and forecasting. #datascience #forecasting #stats

Want to make your models useful across your organization? Think about who is making decisions with them, and what level of detail they need to see for the data. Creating layers is one way to make BI models useful for many different end-user groups! CEOs look at results at the highest level across an organization. Even though they're technically looking at the most data points in their KPIs, they will look at the total aggregated numbers to get a quick picture of the totals and trends. Below the KPIs though, there can be another layer to support the more detailed trends and analysis behind the aggregated totals. This can include visuals like line charts (to display time series trends), bar charts (to break down totals by categories), and scatter plots (to show relationships between two numeric variables). Below that, we can build another layer for the product manager or a particular department to display much more granular details through visuals like tables or matrices. Thinking about the end-users enables us to optimize the value of our models by adjusting our communication strategy to the target audience. But that's not to say that CEOs aren't interested in the details or that one department isn't interested in the high-level picture within an organization. We want to present the level of detail to the appropriate audience of our models but still give them the option to get more details in the level below or get a higher-level picture in the level above. #BusinessIntelligence #Design #DataAnalytics

20 years ago, business intelligence (BI) was about static reports. Spreadsheets, PDFs, and dashboards provided snapshots of what had happened. Today? BI is dynamic, predictive, and everywhere. Here’s how it evolved: 1️⃣ Then: BI tools were expensive and accessible only to Fortune 500s. Now: Platforms like Power BI and Tableau democratize analytics for businesses of every size. 2️⃣ Then: Decisions were based on historical data. Now: Real-time data integration and AI-driven insights empower leaders to act in the moment. 3️⃣ Then: It was about centralization (a few analysts crunching numbers). Now: Self-service BI tools empower every employee to access and act on data. 🔮 The future of BI? Generative AI is already redefining how we interpret data. Imagine querying your BI system with “What are the top trends driving sales in the past quarter?” and receiving an instant, conversational insight. If you’re not investing in BI now, you’re leaving opportunity on the table. 💡 Where do you see BI evolving next? P.S. If you’re exploring BI, start small but think big. (Your business will thank you.)

Linear Regression in Excel with Python and Copilot 🔗 https://lnkd.in/gAcCmx8h Regression has been around forever, but it’s still one of the most useful tools in modern analytics. And now, with Copilot and Python in Excel, you can build, interpret, and visualize sophisticated regression models without leaving your spreadsheet. This post walks you through building a linear regression from scratch in Excel, step by step, using a fuel economy dataset. Here’s what you’ll learn 👇 📈 How to run simple and multiple linear regressions in Python directly in Excel. 🧮 How to interpret coefficients, evaluate model fit with R-squared and RMSE, and visualize predicted vs. actual values. 🔍 How to check model assumptions using residual plots and identify potential issues like nonlinearity or heteroskedasticity. 💡 How to make real-world predictions and understand why regression still matters for business decision-making today. If you’ve ever wanted to go beyond Excel’s built-in tools and use regression to make smarter, data-driven predictions, this post shows how Copilot makes it intuitive and powerful.

How we think Power BI development works: → Connect to data → Build some charts → Publish ✅ Done. But that’s not BI development. That’s just the tip of the iceberg. How it actually works: - Understand user needs - Define dashboard requirements - Connect to data - Transform and clean data - Build data model - Write DAX measures - Set dashboard layout - Add visuals and branding - Publish first version - Collect user feedback - Improve based on feedback - Iterate Power BI development isn’t linear. → It’s not clean. → It’s not a "quick ad hoc" request on a Friday. → Real development is a feedback loop. Full of complex conversations, user feedback and relentless iteration. But that’s where the value lives: - In solving real business problems. - In making complex data feel effortless. - In driving behaviors that move the business. ♻️ Repost if you know the best solutions are never built on the first try.

Choosing the right model shouldn’t be a guessing game. 📊 Not all regression models "see" data the same way. While a Linear Regression seeks a straight path, an XGBoost or Decision Tree navigates the noise through segments. In this visual guide, we’ve broken down 9 essential regression techniques to help you visualize: ✅ Bias vs. Variance: See which models overfit vs. which remain smooth. ✅ Linear vs. Non-Linear: Compare the rigid lines of Linear Regression to the flexible curves of Polynomial and Neural Networks. ✅ Model Architecture: Notice how Tree-based models (Random Forest, XGBoost) create "steps" while distance-based models (k-NN) react to local density. Whether you're a student or a seasoned ML Engineer, this chart serves as a quick mental map for your next project. Created by Antara and Aditya at Neuroxsentinel. #DataScience #MachineLearning #ArtificialIntelligence #Regression #Neuroxsentinel #Coding #DeepLearning #Statistics

📌 The 3 Layers of Business Intelligence (This is where you should start) Business Intelligence is not just about creating dashboards and reports. It’s a strategic process that involves 3 essential layers. Yet, most businesses make a common mistake: they skip the foundation and then struggle to get value from their data. 👉 Here’s a breakdown of the 3 layers of BI and why you should always start with the basics: 1️⃣ 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 (𝐓𝐡𝐞 𝐂𝐨𝐫𝐞) Most businesses fail to define their data strategy. They jump straight to dashboards, which leads to incomplete, inaccurate, or irrelevant insights. This is what you should focus on first: ⤷ Target Audience: Who needs the insights and how will they use them? ⤷ Data Sources: What data do you have and where is it located? ⤷ ETL Pipeline: How will the data be cleaned, transformed, and loaded? ⤷ Data Quality & Governance: Data must be accurate, secure, and compliant. 2️⃣ 𝐁𝐈 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 (𝐓𝐡𝐞 𝐁𝐫𝐢𝐝𝐠𝐞) Once the foundation is set, you can start planning how to turn your data into actionable insights. This will save you time, money, and headaches down the line. At this stage, you should focus primarily on: ⤷ Setting Objectives: What business problems are you solving? ⤷ Creating a KPI Framework: Which metrics truly matter? ⤷ Aligning with Stakeholders: Ensure everyone is ⤷ Defining the Budget & Timeline: What resources and deadlines do you have? 3️⃣ 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 (𝐓𝐡𝐞 𝐆𝐨𝐚𝐥) This is where you turn your data into actionable insights through: ⤷ Interactive Dashboards: Real-time insights for decision-making. ⤷ Custom Metrics: Tailored to your unique business needs. ⤷ Feedback Loops: Continuously improving based on user input. So, where should you start? Take a step back and focus on Data Strategy first. It’s the foundation that makes everything else actually work. Trust me, getting this right will save you a ton of headaches down the road. 👉 What’s your take? Are you building on a solid foundation or are you jumping straight to building dashboards? #DataAnalytics #DataStrategy #BusinessIntelligence