Business Intelligence Consulting

Most EPM vendor demos look convincing. Clean dashboards. Smooth workflows. A clear implementation plan... And a promise that this time will be simpler than the last. But a good demo does not tell you whether the platform will create value once it is live, or whether the business will actually use it. That requires a different set of questions. 1. Functional fit Can the business adapt planning logic, scenarios and assumptions themselves, or will every change require IT or vendor support? 2. Technical capabilities Can the platform integrate reliable actuals from ERP, HRIS and other core systems without manual reconciliation? And does it meet data protection, security and audit requirements from the start? 3. Cost and value What is the full cost, including integration, administration, licences, training and change effort? And will the business case still hold as the organisation scales? 4. Vendor and support What do comparable companies say about the vendor after implementation, not just during selection? And is the support model strong enough for the complexity of your organisation? 5. Implementation and adoption How much real team effort is needed to reach a useful first forecast? And will budget owners plan directly in the tool, or will finance still collect inputs offline? Once the criteria are clear, the selection process matters just as much: • Define the requirements before engaging vendors Align internally on the business problems the platform must solve. • Compare a real shortlist Assess options against agreed criteria, not preference, familiarity or demo quality. • Test with your own data Validate planning logic, integrations, reporting and user experience in your actual context. • Secure leadership commitment before signing Adoption depends on decisions, ownership and behaviours, not only configuration. The best EPM platform is not the one with the strongest demo. It is the one that fits your planning ambition, data reality and ability to change. Which of these questions would your current platform struggle to answer? P.S. This lens is part of our broader FP&A software selection framework. Happy to share the full guide if useful for a selection you are running.

📌 Power BI vs Tableau vs Looker Studio (Which Data Visualization Tool Should You Use?) Let’s get one thing clear: there’s no universal best tool. The right choice depends entirely on your business needs, budget, and data maturity. In 2025, the three tools that are dominating the market are: ⤷ Power BI (Microsoft) ⤷ Tableau (Salesforce) ⤷ Looker Studio (Google) But how do they really stack up? 1️⃣ 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 If your company is already using Microsoft tools (Azure, Excel, Teams), Power BI is a natural fit. → Seamless integration with the Microsoft stack → Advanced data modeling with DAX → Strong governance & security for enterprise use However, there’s a steeper learning curve for advanced modeling, and licensing can get REALLY expensive as you scale up to Premium capacities. It’s best for mid-to-large enterprises focused on operational reporting and executive dashboards that require strict data governance and security. 2️⃣ 𝐓𝐚𝐛𝐥𝐞𝐚𝐮 If you want beautiful dashboards and powerful visual exploration, Tableau is hard to beat. → Industry-leading visualization and design flexibility → Drag-and-drop interface that’s intuitive for business users → Excellent for exploratory data analysis and presentations But be aware: the licensing costs are high, and complex data preparation often requires additional tools like Tableau Prep or upstream data cleaning during the ETL process. This is best for organizations focused on data storytelling and visual insights, especially for presentation-ready dashboards. 3️⃣ 𝐋𝐨𝐨𝐤𝐞𝐫 𝐒𝐭𝐮𝐝𝐢𝐨 Everyone loves Looker Studio. It doesn’t offer the same performance at scale as a tool like Power BI, but it’s the go-to tool for most organizations, especially for Marketing and Sales teams. → 100% free to use → Native integration with Google Analytics, Google Ads, BigQuery, and YouTube → Perfect for marketing teams and website performance tracking One of the main drawbacks I’ve seen is the lack of advanced modeling capabilities. 💡 The Bottom Line: Choose Based on Your Maturity, Not Just Features. If you’re a startup → Start simple with Looker Studio. If you’re growing and need operational reporting → Power BI is the natural choice. If you want visual impact for leadership and presentations → Go with Tableau. The tool is just the means. The real value comes from a clear data strategy. What's your experience with these tools? Which one do you prefer and why? Share your insights below! 👇 #DataAnalytics #DataVisualization #BusinessIntelligence

𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝘃𝘀. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 𝟭. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗼𝗿𝘀: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Extensive built-in connectors with strong integration into Microsoft services. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Broad range of connectors, including advanced data source integrations. 𝟮. 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Powerful data transformation via Power Query, integrated within the platform. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Intuitive data cleaning and transformation with Tableau Prep, often requiring an additional tool. 𝟯. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Rich visualizations with integration into Microsoft tools, though less customizable than Tableau. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Advanced and highly customizable visualizations with extensive graphical options. 𝟰. 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Easy sharing and collaboration through Power BI Service, with built-in features for access control. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Robust sharing and collaboration via Tableau Server and Tableau Online, often requiring more complex setup. 𝟱. 𝗣𝗿𝗶𝗰𝗲: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Cost-effective with a free desktop version and affordable subscription plans. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Generally higher cost with multiple pricing tiers for different needs. 𝟲. 𝗠𝗮𝗿𝗸𝗲𝘁 𝗦𝗵𝗮𝗿𝗲: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Rapid growth, especially among organizations using Microsoft products. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Strong market presence with a focus on advanced analytics and enterprise solutions. 𝟳. 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: User-friendly for those familiar with Microsoft interfaces, with a focus on ease of use. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Known for its drag-and-drop interface and flexibility, though it may have a steeper learning curve. 𝟴. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Generally performs well with smaller datasets and integrates smoothly with Azure. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Handles large and complex datasets efficiently, offering advanced performance optimization features. 𝗦𝘂𝗺𝗺𝗮𝗿𝘆: 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: Integration with Microsoft ecosystems. Cost-effective, ideal for budget-conscious organizations 𝗧𝗮𝗯𝗹𝗲𝗮𝘂: Advanced visualization capabilities. Robust analytics, suitable for enterprises with complex data needs. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲𝗺 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻: - Your specific requirements. - Budget considerations. - Existing data infrastructure.

Do you know what "End-to-End" Power BI Development and implementation actually looks like? End-to-end Power BI project execution doesn't means just creating the report. It consists of all the following steps : 👨💻 📍Development process : ➡️ Understanding the business needs and various component of the client's requirement and identifying the associated KPIs. ➡️ Connecting Power BI desktop to the data sources. ➡️ Cleaning and transformation the data at source level as well as BI level using the Power Query Editor. ➡️ Creating an efficient data modelling between various tables based on the associated keys. ➡️ Writing many dax formulas as per the need to achieve the business objective. ➡️ Creating the business intelligence reports and visualizing the data by considering the performance issues that may occur. ➡️ Performing User Acceptance Testing (UAT) and doing the changes in the report as per the end user's requirements and suggestions. 📍Post- Development Process : 📊 ➡️ Publishing the report in the Power BI service after the completion of UAT. ➡️ Installing and setting up the gateway connection. ➡️ Scheduling report refresh after finalizing the time, considering clients requirements as well as the running time of data pipelines. ➡️ Creating Dashboards in Power BI service (if there's any need) ➡️ Considering and implementing security components and providing access to the users. ➡️ Automating downloading of some visuals from the report (if there's any need from the end user) And that's how we provide end-to-end value adding and impactful Power BI solutions 👨💻 #dataanalytics #powerbi

𝗠𝗼𝘃𝗲 𝗕𝗲𝘆𝗼𝗻𝗱 𝗗𝗮𝘁𝗲𝗱 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗗𝗲𝘀𝗶𝗴𝗻𝘀 If your Power BI reports feel a bit outdated - same old layouts, clunky visuals, and little focus on modern UI/UX - it might be time to shake things up. 𝗪𝗶𝗱𝗴𝗲𝘁𝘀 𝗮𝗻𝗱 𝗖𝘂𝘀𝘁𝗼𝗺 𝗖𝗮𝗿𝗱 𝗩𝗶𝘀𝘂𝗮𝗹𝘀 are a great way to give your reports a clean, modern feel without overcomplicating things. These aren’t just for show. They help you create reports that are intuitive, focused, and better aligned with today’s design standards. Whether you’re building embedded solutions or improving your everyday dashboards, this approach can make a big difference. 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀 𝗮𝗻𝗱 𝗖𝘂𝘀𝘁𝗼𝗺 𝗖𝗮𝗿𝗱𝘀? • 𝗕𝗿𝗲𝗮𝗸 𝗔𝘄𝗮𝘆 𝗳𝗿𝗼𝗺 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻: Move past standard, formulaic layouts. • 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Highlight key metrics without overwhelming the user. • 𝗠𝗼𝗱𝗲𝗿𝗻𝗶𝘇𝗲 𝘁𝗵𝗲 𝗟𝗼𝗼𝗸: Clean, polished designs feel more professional and easier to navigate. • 𝗔𝗹𝗹 𝗕𝘂𝗶𝗹𝘁 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜: No extra tools needed - everything’s native. 𝗛𝗼𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗧𝗵𝗲𝗺: It's actually easier than it looks. • 𝗦𝗵𝗮𝗽𝗲𝘀 𝗳𝗼𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Use rectangles and rounded corners to create card-like visuals. Add transparency or shadows for a modern touch. • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗔𝗫 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀: Use measures to populate visuals with KPIs, trends, or user-specific insights that respond to filters. • 𝗔𝗱𝗱 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗙𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴: Use colors, icons, or data bars to emphasize trends and changes. • 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝘄𝗶𝘁𝗵 𝗚𝗿𝗶𝗱𝘀: Align visuals neatly for a clean, app-like feel. • 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗦𝗶𝗺𝗽𝗹𝗲: Avoid clutter. Minimalist designs with clear labels and ample whitespace work best. Try it in your next report and see how much more engaging your Power BI dashboards can feel. #PowerBI #DashboardDesign #UIUX

Choosing the right data tool for your projects isn’t about trends — it’s about the problem you’re solving. This visual breaks down how to choose the right data analytics tool in 2026 — based on your goal, data size, skill level, collaboration needs, and industry. (See the image for the full decision framework) Real-world project scenarios 👇 📊 Small business sales analysis → Excel or Google Sheets for quick cleaning, summaries, and insights 📈 Executive dashboards & KPI tracking → Power BI or Tableau for interactive, shareable business intelligence dashboards 🗄 Large transactional or customer data (millions of rows) → SQL for querying + Python for deeper analysis and automation 🤖 Forecasting, churn prediction, or ML projects → Python or R for predictive & prescriptive analytics ⚙ Automated reporting pipelines → SQL + Python + BI tools for scheduled refreshes Practice with real-world datasets If you want hands-on experience choosing the right tool, start with real data: 🔹 Kaggle – business, finance, marketing, healthcare datasets 🔹 Maven Analytics Playground – realistic analyst projects 🔹 Google BigQuery Public Datasets – large-scale production data 🔹 Data.gov – raw government datasets 🔹 World Bank / UN Open Data – messy global datasets 💡 Pro tip: Master the decision logic, not just the tool. Great analysts don’t ask “What tool should I learn?” They ask “What problem am I solving?” If you’re building projects, save this. If you find it insightful, repost it for others ❓Question: Which tool do you reach for first — Excel, SQL, Python, or a BI tool — and why? Image by Jayen T. #DataAnalytics #DataAnalyst #Excel #SQL #Python #PowerBI #Tableau #BusinessIntelligence #DataProjects #BuildingInPublic #DataCommunity

😎 New data tools are hitting the market at an astonishing rate! 😱 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐩𝐢𝐜𝐤 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐨𝐧𝐞? Taking cue from Enterprise Architecture (EA), I suggest defining a set of criteria for your upcoming data tool. This method facilitates the initiation of your search for a new tool or the evaluation of the latest data innovations. Although the spectrum of EA criteria for data tools is broad, below are some questions I often consider when assisting clients in the tool selection process: 👷♂️ 👷♀️ 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐬 - Who are the intended users of this new tool? Is it designed for engineers, or should it be accessible to business analysts? 🗺 𝐃𝐚𝐭𝐚 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 - How does it integrate with your existing data platform architecture? 🛠 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐯𝐬 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 - Do you prioritize having complete control over the tool, or would a service (like SaaS) be more suitable? ⛓ 𝐕𝐞𝐧𝐝𝐨𝐫 𝐋𝐨𝐜𝐤-𝐢𝐧 - Can you easily transition away from this tool if necessary? Does it adhere to open standards? 🧙♀️𝐒𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 - Does this tool complicate your data flows and architecture, or does it simplify existing processes? ☁ 𝐈𝐓 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 - Is it aligned with your IT strategy, especially if you lack a cloud strategy, is acquiring a cloud-based tool advisable? 🎁 𝐁𝐮𝐲 𝐯𝐬 𝐁𝐮𝐢𝐥𝐝 - Do you prefer to develop your own tool (using for example a framework), or would you rather not invest in software development and opt for purchasing a tool? It's crucial not to underestimate the effort of building your own tools! ⏲ 📈 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 - How does this tool align with your orchestration and monitoring tools? Can it integrate well, allowing you to monitor metrics and interface with your orchestrator? 🤙 𝐓𝐨𝐨𝐥 𝐄𝐜𝐨-𝐬𝐲𝐬𝐭𝐞𝐦 - Is there support available (consultants, partners) for this tool? Are these resources readily accessible or distant? These inquiries are applicable whether selecting tools for specific parts of a data platform (such as data ingestion, orchestration, catalogs, transformation, reporting, etc.) or for entire data platform solutions. Certainly missing in this list of criteria: Privacy and Security 🔒 What criteria do you consider important? #enterprisearchitecture #datastrategy #itstrategy

What's the most expensive data tool your company bought that no one uses anymore? After decades as a data leader, I've seen it all - the game-changers and the expensive shelf-ware we don't talk about anymore. But here's the thing... Every single tool that became irreplaceable checked the same 4 boxes. Here they are: 1. The 80/20 Rule My rule of thumb: If a tool offers at least 80% of what you need out of the box, consider buying. If not, you're better off building. Why 80%? When you customize more than 20% of a tool's functionality, the maintenance and upgrade costs spike your TCO (Total cost of ownership) and eat into the ROI. 2. Look beyond the usual options "Nobody ever got fired for hiring IBM" Sure, going with established players feels safe. But are you missing innovative solutions that could give you a competitive edge? Instead of defaulting to the big names: - Connect with peers at smaller, focused conferences. - Look beyond Gartner quadrants & major analyst reports. - Tap into specialized discovery platforms for emerging tech. Your goal isn't finding the most established vendor - it's finding the right fit for YOUR needs. 3. The Proof of Concept (POC) Strategy Never (and I mean never) do your proof of concept in the vendor's environment. Yes, they'll offer their pristine cloud setup. Yes, it's tempting. Yes, it's "free." But it's misleading. You need to see how it performs in your environment, with your security controls, your connectivity, your everything. 4. The Business User Test If your tool needs business participation (like data catalogs or MDM), put it in front of actual users before buying. I've seen million-dollar implementations fail because this step was skipped. Selecting the right tool isn't about features and pricing. It's about understanding how it fits into your ecosystem and culture.

🧠 𝗚𝗶𝘃𝗲 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗥𝗲𝗮𝗹𝗹𝘆 𝗡𝗲𝗲𝗱 – 𝗦𝗺𝗮𝗿𝘁 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜! What I often see in dashboarding is that people deliver charts and expect consumers to somehow figure things out on their own, spending time extracting insights. That’s not how I envision the perfect dashboard. 𝗔 𝗴𝗿𝗲𝗮𝘁 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘀𝗵𝗼𝘂𝗹𝗱 𝘀𝗽𝗲𝗮𝗸 𝗳𝗼𝗿 𝗶𝘁𝘀𝗲𝗹𝗳 𝗮𝗻𝗱 𝗴𝗶𝘃𝗲 𝗰𝗹𝗲𝗮𝗿 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 - 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 𝗳𝗼𝗿 𝘁𝗵𝗲𝗺. 💡 One technique I often use to ensure the client gets the answers they're seeking is something I like to call 𝘚𝘮𝘢𝘳𝘵 𝘐𝘯𝘴𝘪𝘨𝘩𝘵𝘴. It’s essentially a text summary of what’s in a specific section of the report. It highlights the most important information for the end user and explains the cause behind certain results. So instead of the user going back and forth, comparing results, or exporting charts and tables to Excel for their own analysis, they get a quick, clear summary of what’s happening. ⚡ This text can be 𝗳𝘂𝗹𝗹𝘆 𝗱𝘆𝗻𝗮𝗺𝗶𝗰, adjusting based on selected measures or other slicers - essentially anything that shifts the perspective of what's happening. If you use an HTML custom visual, it can also incorporate plenty of conditional formatting to meet user needs. 𝗜 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲𝘀𝗲 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗮𝗿𝗲𝗻’𝘁 𝘄𝗶𝗱𝗲𝗹𝘆 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲: 1️⃣ It’s time-consuming, and unfortunately, developers often cut corners and settle for whats only "good enough". 2️⃣ Developers often don't know what consumers need and may hesitate to ask or lack the skills to find out. 𝗦𝗵𝗶𝗳𝘁 𝘆𝗼𝘂𝗿 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘁𝗼 𝗽𝘂𝘁 𝘁𝗵𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗳𝗶𝗿𝘀𝘁. 𝗟𝗼𝗼𝗸 𝗮𝘁 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝘃𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲𝗶𝗿 𝗲𝘆𝗲𝘀 𝗮𝗻𝗱 𝗮𝘀𝗸 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗶𝗳 𝗶𝘁 𝘁𝗿𝘂𝗹𝘆 𝗮𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝘀 𝘁𝗵𝗲𝗶𝗿 𝗻𝗲𝗲𝗱𝘀! #analytics #data #powerbi #datavisualization #report #dashboard #reporting #visualization #microsoftpowerbi #pbicorevisuals #svg #html #customvisuals #UXDesign