Business Intelligence Development

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.)

🚀 How AI Will Disrupt Business Intelligence (BI): From Dashboards to Dialogues For decades, BI has meant dashboards, reports, and scheduled refreshes. But the era of static insights is fading. The next generation of BI is not about pushing reports to users—it’s about pulling answers from AI, instantly, interactively, and intelligently. 💬📊 Here’s how it’s all changing—and fast. 🔄 From Push to Pull Instead of waiting for reports to arrive in inboxes, users will now ask natural language questions: 🧠 “What’s driving our drop in Q2 retention?” 📈 “Can you plot churn by segment for the last 12 months?” AI-powered interfaces will deliver real-time answers—as both textual narratives and dynamic visuals. Think ChatGPT + Tableau + Analyst—all rolled into one. 🎨 The Rise of Data Storytelling No more sifting through 20 dashboards. AI teammates will curate narratives, highlight anomalies, explain trends, and even suggest next actions. 📚 From dashboards to data stories 🎯 From static KPIs to contextual insights 🛠️ What This Means for BI Tools The BI stack is evolving fast: Exploratory data analysis (EDA) will increasingly happen in AI-native tools like Claude, ChatGPT Enterprise, or Cursor. Visualization and governance will still matter—but traditional BI tools will need to integrate with context-aware AI agents. BI tools must become "AI-first" presentation layers—not the primary workspace for analysts. 🧪 The Future of BI is Agentic AI “teammates” will become your go-to analysts: 🔍 Ask. 📊 Visualize. 🗣️ Explain. 🎯 Recommend. The result? Faster decisions, democratized insights, and fewer bottlenecks. We’re heading toward BI without borders, where data fluency meets AI fluency. 🔮 Looking Ahead In the next 12–18 months: ✅ AI will dominate exploratory analysis ✅ MCP and other protocols will standardize context delivery ✅ BI tools will either evolve or get unbundled ✅ Users will expect stories, not slides 💬 What will your BI stack look like in 2026? Let’s talk in the comments 👇 #BIRevolution #AIinAnalytics #GenAI #BusinessIntelligence #DataStorytelling #AIUX #PromptEngineering #AIxBI #AnalyticsTransformation

At most companies, thousands of users interact with dashboards every day. They make decisions, shape strategy, and drive operations based on what's in the BI layer. It’s not just a record of past performance; it’s a real-time reflection of how the business actually runs. Yet most AI initiatives ignore it. Rather than building on years of embedded knowledge, companies often start from scratch. New datasets. Fresh labeling. Models trained in isolation without leveraging the system that already captures how decisions are made: their BI tools. The challenge is that BI environments are messy. Duplicated dashboards. Conflicting metrics. Sprawling assets with no clear ownership. But within that mess is meaning. BI is the semantic core of the enterprise. It encodes business logic, behavior, and context. It's the most valuable — and most overlooked — input for building decision intelligence. The opportunity is clear: 1) Clean it. Standardize it. Extract what matters. 2) Use it to power automation. Train models with real context. 3) Turn BI from a passive archive into a foundational asset for enterprise AI. The future of analytics won’t be built from raw data alone. It will be built on the intelligence already inside your business.

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

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

📌 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

Business intelligence is more than just a buzzword; it's your competitive edge. Yet, many CTOs and CIOs find themselves swamped by data but starved for insights. You need a sharp, actionable plan. Here are six steps to turbocharge your BI efforts. Clarify your objectives. - Define what success looks like. - Align BI with business goals. - Prioritize key performance indicators. Streamline data collection. - Ensure data accuracy. - Automate data gathering. - Focus on relevant data sources. Enhance data analysis. - Implement advanced analytics. - Encourage exploratory data analysis. - Validate insights through testing. Boost data visualization. - Use intuitive dashboards. - Highlight critical metrics. - Simplify complex data. Promote a data-driven culture. - Encourage data literacy. - Reward insight-driven decisions. - Foster open data discussions. Evaluate and iterate. - Regularly review BI strategies. - Adapt to evolving needs. - Embrace feedback for improvement. The big takeaway: A strategic approach to BI reveals hidden opportunities. Trust in a structured, iterative process. What’s your biggest BI challenge today?

𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗪𝗼𝗿𝗸 – 𝗠𝗖𝗣 𝗰𝗼-𝗽𝗶𝗹𝗼𝘁 𝗳𝗼𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 💭 Imagine this: instead of switching between Power BI Desktop, Tabular Editor, and DAX Studio, you ask an assistant: “𝘊𝘳𝘦𝘢𝘵𝘦 𝘢 𝘠𝘰𝘠 𝘮𝘦𝘢𝘴𝘶𝘳𝘦, 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘦 𝘪𝘵, 𝘢𝘯𝘥 𝘴𝘩𝘰𝘸 𝘮𝘦 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘵𝘩𝘦 𝘣𝘰𝘵𝘵𝘭𝘦𝘯𝘦𝘤𝘬 𝘪𝘴 𝘪𝘯 𝘍𝘌 𝘰𝘳 𝘚𝘌.” And it just happens. No context switching. No manual tracing. The assistant writes, injects, tests, and reports back. ⸻ 𝗛𝗼𝘄 𝗕𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗟𝗼𝗼𝗸𝘀 𝗧𝗼𝗱𝗮𝘆 Power BI developers spend a surprising amount of time on overhead: • Looking up metadata in Desktop • Writing measures by hand • Jumping to DAX Studio for performance traces • Iterating back and forth until it’s “good enough” We’re craftsmen, but much of our craft is consumed by glue work between tools. ⸻ 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗧𝗵𝗮𝘁’𝘀 𝗖𝗼𝗺𝗶𝗻𝗴 With AI-native tooling, that glue work dissolves. The developer’s role shifts from manual executor to architect and reviewer. • Instead of typing out boilerplate DAX, you validate and refine generated measures. • Instead of manually running traces, you interpret FE/SE diagnostics delivered conversationally. • Instead of juggling tools, you stay in a single feedback loop with your model. ⸻ 𝗪𝗵𝘆 𝗜’𝗺 𝗘𝘅𝗰𝗶𝘁𝗲𝗱 This isn’t just theory. In MCP I've developed for Power BI Desktop, I’ve taken a step toward that co-pilot future - it can already: • Generate and inject measures into the model • Explore metadata programmatically • Run FE/SE traces automatically Something that previously required 𝘁𝗵𝗿𝗲𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗼𝗼𝗹𝘀 (Power BI Desktop, Tabular Editor, DAX Studio) can now all happen in a 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗵𝗮𝘁 𝘄𝗶𝗻𝗱𝗼𝘄. I recently heard in an interview: “English has become the hottest programming language in the Valley.” It reminded me of what Brian Julius and Sam McKay, CFA discuss about the agentic future of BI - we’re moving away from low-level execution toward intent-driven workflows. In a few years, Power BI development will move several abstraction levels higher. From writing every line of DAX → to 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗹𝗼𝗴𝗶𝗰 𝗶𝗻 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲. Just as we once moved from assembler to modern programming languages, we’re now moving from low-level BI tasks to high-level intent-driven development. ⸻ 𝗧𝗵𝗶𝘀 𝗙𝘂𝘁𝘂𝗿𝗲 𝗜𝘀𝗻’𝘁 𝗛𝗲𝗿𝗲... 𝗬𝗲𝘁 There are obstacles: • Many companies restrict LLM access, fearing information leakage. • MCP support across LLMs is still scarce - the list of models that don’t support it is much longer than those that do. • Right now, only Claude supports MCP natively in their Desktop app. But I see this as a matter of “𝘄𝗵𝗲𝗻,” 𝗻𝗼𝘁 “𝗶𝗳.” Once LLM access normalizes and MCP becomes a standard, the co-pilot future for BI developers becomes inevitable. ⸻ #PowerBI #FutureOfWork #DAX #MicrosoftFabric #ModelContextProtocol

The biggest misconception about Business Intelligence (BI) is that it’s merely a dashboard function. In reality, it serves as a decision-making function. When BI is closely aligned with Finance, Go-To-Market (GTM), and Product teams, it transforms into an engine that synchronizes how the entire business comprehends performance, going beyond just data visualization. The value of BI lies in: - Defining the metrics that genuinely drive revenue - Building data models that accurately reflect how Annual Recurring Revenue (ARR) behaves - Ensuring that Finance, Sales, Customer Success (CS), and Product teams interpret the same signals - Reducing time-to-insight, allowing for quicker decision-making - Improving forecast reliability through consistent inputs - Eliminating the downstream noise that arises from teams operating on different truths When BI is built correctly, it reduces friction, speeds up planning, and gives leadership a much clearer sense of WHY the business is performing the way it is — not just WHAT happened. That’s where the real leverage is. Tools matter. But the structure, governance, and cross-functional alignment behind the data matter more. BI done well makes the company run smarter, faster, and more confidently.