Business Intelligence Consulting

📌 The 4 Layers of Data & BI Problems Most businesses approach data & BI problems by fixing symptoms instead of root causes. They have a problem-solving problem. They spend all their time fixing what’s visible without ever addressing what’s critical. → Dashboards break? They find a workaround. → KPIs are wrong? They update them on the go. → Reports load slowly? They try to optimize queries. But Business Intelligence problems live across four layers. You need to understand where problems really originate and how to solve them once and for all. 1️⃣ 𝐒𝐮𝐫𝐟𝐚𝐜𝐞-𝐋𝐞𝐯𝐞𝐥 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 These are the easiest to spot but the least impactful to solve in isolation: → Dashboards loading slowly → Incorrect KPIs → Data export errors Most teams mistakenly spend most of their time firefighting here. But fixing symptoms without addressing the root cause means these problems will resurface, again and again… 2️⃣ 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐚𝐥 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 This is generally the technical debt accumulated over time. You have to dig deeper. If your reports and visualizations frequently break, the issue lies in structural layers like: → Data pipelines failing silently → Unreliable ETL processes → Poor semantic models causing frequent manual adjustments Until you invest in solid data engineering practices and build reliable pipelines, your BI layer will remain unstable. 3️⃣ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 Even with the best pipelines, your BI strategy will fail if the underlying business alignment is broken: ⤷ No standardized KPI definitions across teams (Finance defines “Revenue” differently from Sales). ⤷ Data silos block cross-department collaboration and create fragmented insights. ⤷ Critical systems aren’t integrated which leaves decision-makers blind to the full picture. This is where true data leadership comes in. Fixing this requires cross-functional alignment and establishing enterprise-wide data definitions. 4️⃣ 𝐂𝐨𝐫𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 At the deepest level, problems always boil down to: → Weak or non-existent data governance → Unclear ownership and accountability → Missing a "single source of truth" The hard truth is: You can’t fix a broken BI strategy with more dashboards. 1) Fixing only surface-level problems means symptoms will reoccur. 2) Structural and strategic layers demand clear communication and cross-team collaboration. 3) Core data problems require a robust data governance strategy Addressing the root issues will transform your BI strategy from constant firefighting to true strategic enablement. Which layer does your organization struggle with most? Let’s discuss below 👇 #BusinessIntelligence #DataStrategy #DataGovernance

The Evolution of Data Architectures: From Warehouses to Meshes As data continues to grow exponentially, our approaches to storing, managing, and extracting value from it have evolved. Let's revisit four key data architectures: 1. Data Warehouse    • Structured, schema-on-write approach    • Optimized for fast querying and analysis    • Excellent for consistent reporting    • Less flexible for unstructured data    • Can be expensive to scale    Best For: Organizations with well-defined reporting needs and structured data sources. 2. Data Lake    • Schema-on-read approach    • Stores raw data in native format    • Highly scalable and flexible    • Supports diverse data types    • Can become a "data swamp" without proper governance    Best For: Organizations dealing with diverse data types and volumes, focusing on data science and advanced analytics. 3. Data Lakehouse    • Hybrid of warehouse and lake    • Supports both SQL analytics and machine learning    • Unified platform for various data workloads    • Better performance than traditional data lakes    • Relatively new concept with evolving best practices    Best For: Organizations looking to consolidate their data platforms while supporting diverse use cases. 4. Data Mesh    • Decentralized, domain-oriented data ownership    • Treats data as a product    • Emphasizes self-serve infrastructure and federated governance    • Aligns data management with organizational structure    • Requires significant organizational changes    Best For: Large enterprises with diverse business domains and a need for agile, scalable data management. Choosing the Right Architecture: Consider factors like: - Data volume, variety, and velocity - Organizational structure and culture - Analytical and operational requirements - Existing technology stack and skills Modern data strategies often involve a combination of these approaches. The key is aligning your data architecture with your organization's goals, culture, and technical capabilities. As data professionals, understanding these architectures, their evolution, and applicability to different scenarios is crucial. What's your experience with these data architectures? Have you successfully implemented or transitioned between them? Share your insights and let's discuss the future of data management!

Improving Power BI Performance with Dataset Partitioning While working on a Power BI project, I explored how partitioning a dataset can significantly improve performance and refresh efficiency—especially when dealing with large fact tables or historical data. What is Partitioning in Power BI? Partitioning allows you to split a large table (like a sales fact table) into logical segments, such as by year, month, or region. This is managed using SSMS by connecting to the dataset through the XMLA endpoint in a Power BI Premium workspace. Why use Partitioning? Instead of refreshing the entire table during every dataset refresh, Power BI can refresh only the relevant partitions. This approach reduces resource usage, improves refresh speed, and ensures better performance during query execution. Example: Suppose you have a Sales Fact table with data from 2018 to 2025. Instead of loading all years each time, you can create partitions like: • Sales_2018, Sales_2019, …, Sales_2025 Now, when refreshing the dataset, only the current year (Sales_2025) may need to be refreshed, while older years remain unchanged. Key Benefits: • Faster refresh times • Optimized memory usage • Better control over refresh logic • Improved performance in large datasets I’ve currently implemented partitioning manually via SSMS, and my next step is to explore how these partitions can be refreshed dynamically or incrementally using automation techniques. Would love to hear from others who’ve implemented partitioning—how are you managing refresh strategies in your Power BI solutions? #PowerBI #DatasetPartitioning #DataModeling #BI #PowerBIPremium #SSMS #PerformanceOptimization #BusinessIntelligence

Everyone talks about "growing in your Power BI career" - but what does that actually look like in how you answer questions? Let’s take a simple question and see how your answers evolve from junior to architect. "How do you optimize a slow Power BI report?" -- 👶 Junior (0–2 years) “I’ll check visuals – too many of them slow down the report. I also remove unnecessary columns in Power Query and reduce the number of slicers.” Focus: Visual clutter, basic Power Query cleanup Mindset: Fix what’s visible. -- 👨💻 Mid-Level (2–5 years) “I’ll use Performance Analyzer, review DAX measures for heavy functions like CALCULATE and FILTER, and reduce cardinality in columns. I also limit the number of visuals per page and use aggregations when needed.” Focus: Efficient DAX, cardinality, report design Mindset: Balance between visuals and data model. -- 🧠 Senior (5–10 years) “I audit the data model - removing bi-directional relationships, checking for unnecessary calculated columns, normalizing data. I also review refresh performance and apply incremental refresh for large datasets.” Focus: Model design, relationships, refresh performance Mindset: Systemic thinking, model-driven optimization. -- 🧱 Architect / Principal (10+ years) “I assess the entire architecture: is Power BI the right tool, or should a semantic layer be used (e.g., Azure Analysis Services)? I define dataflows for reusable ETL, apply row-level security without sacrificing performance, and align report structure with enterprise scalability standards.” Focus: Architecture, scalability, governance Mindset: Enterprise-first, long-term maintainability -- As you grow, your answers shift from “How do I fix this?” to “How do I design this so it never breaks?”

When inheriting a Power BI report from someone else, there are several areas you can examine to identify potential improvements: ⭐ Data Model Complexity: Simplify the data model, remove unused tables, and eliminate redundant columns. ⭐ Data Source Queries: Optimize queries, reduce data transfer, and enable query folding. ⭐ Data Refresh Frequency: Avoid frequent and unnecessary data refreshes. ⭐ DAX Formulas: Simplify and optimize DAX expressions in measures and calculated columns. ⭐ Data Visualizations: Reduce unnecessary visuals and use filters to control data density. ⭐ Page Load Time: Optimize visuals, filters, and data model to reduce load time. ⭐ Query Performance Analyzer: Identify slow queries and optimize them. Data Cardinality and Relationships: Check cardinality for efficient query processing. ⭐ Aggregations and Summarization: Use aggregations to pre-calculate data. ⭐ Data Storage Mode: Choose the appropriate storage mode (Import, DirectQuery, Dual). ⭐ Report Layout and Design: Avoid complex visuals and overlapping elements. ⭐ Parameterization and Dynamic Filtering: Use parameters for dynamic filtering. ⭐ Data Distribution and Partitioning: Consider partitioning large datasets. Resource Utilization: Allocate sufficient resources for optimal performance. ⭐ Testing Scenarios: Evaluate performance under different scenarios. Usage Metrics: Monitor usage to identify bottlenecks during peak times. ⭐ Data Compression: Optimize data compression settings. What else would you add to the list? #PowerBIMadeEasy

Want to run better data projects? Learn from the pros: project managers. Here’s what you can learn from them: 1. 𝗖𝗹𝗲𝗮𝗿 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀 𝗮𝗻𝗱 𝗦𝗰𝗼𝗽𝗲: Defining clear goals and boundaries prevents scope creep and keeps your project focused. Begin your projects by outlining objectives, deliverables, timelines, and resource allocation. Regularly revisit these to stay on track. 2. 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Seamless communication ensures all stakeholders are aligned and informed. Schedule regular check-ins, use project management tools like Asana or Jira, and maintain transparent channels for updates and feedback. 3. 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Identifying and mitigating risks early can save the project from potential pitfalls. Conduct a risk assessment at the project's start, develop contingency plans, and monitor risks continuously.     4. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Make the most of what you’ve got. Efficiently allocating your tools, technologies, and time can be the difference between a good data project and a great one.     5. 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Efficient time management ensures deadlines are met without compromising quality. Break down your tasks into manageable chunks, prioritize based on importance and urgency, and use time-tracking tools to monitor progress.     6. 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Engaged stakeholders are more likely to support your project. Involve stakeholders early, seek their input regularly, and update them on progress and setbacks.     7. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁: Learning from past projects leads to better outcomes in future endeavors. After project completion, conduct a retrospective to identify what went well and areas for improvement. Apply these lessons to your next project. By incorporating these project management principles, you can transform your approach to data projects, ensuring that they are completed on time and within scope while delivering meaningful and actionable insights. What project management practices have you integrated into your data projects? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #projects #projectmanagement #careergrowth

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

Organizations often underestimate the cultural shift required to truly harness data—without aligning mindset, skills, and structure, even the most advanced technologies struggle to translate information into measurable business gains. To generate tangible value from data, companies need more than just tools—they need a coordinated strategy across three dimensions: business goals, technical capability, and operational readiness. This approach includes aligning use cases with drivers like cost reduction or improved customer outcomes, building robust foundations through governance, analytics, and architecture, and fostering a data-centric culture supported by organizational processes, training, and clear KPIs. Without this integration, efforts remain fragmented, limiting the impact of data initiatives. #DataStrategy #Analytics #DigitalTransformation #DataGovernance #BusinessIntelligence

🚀How to choose the right KPIs for your tech scale-up I've noticed a consistent challenge Many businesses collect extensive data but struggle to identify which metrics actually matter. Here's my top tips on choosing KPIs that will genuinely drive your business forward. 1️⃣ Start with strategy, not metrics: Your KPIs should reflect your strategy through numbers. Before opening any spreadsheets, ask yourself: 🎯where are you aiming to get to? 🥅what specific goals have you set for your team? 🥸how do you differentiate from competitors? Your answers should guide your choice of metrics, not the other way around. 2️⃣Balance leading and lagging indicators: Here's a practical example. If your goal is to increase premium tier adoption from 15% to 25%, that percentage is your lagging indicator. But you need leading indicators to drive progress. For your sales team, this might mean tracking: ✅number of upgrade conversations with existing customers ✅weekly demos of premium features ✅customer feature usage patterns These leading indicators help predict whether you'll hit your target and allow for adjustments while there's still time to impact the outcome. 3️⃣The essential metrics: Some metrics need consistent monitoring regardless of your strategy. In my experience, these include: ☑️MRR ☑️EBITDA ☑️Cash runway ☑️Customer LTV ☑️Customer churn Consider these your fundamental business health indicators. 4️⃣Make data collection seamless: Even the best-designed KPI framework fails if data collection is manual and inconsistent. Two key principles: 🖥️automate wherever possible 🐣capture data at its earliest possible point For example, don't wait for finance to categorize sales by department at month-end. Build it into your invoicing process. 5️⃣Consider the human element: Numbers need context to drive action. For KPIs to create change: 🗣️share them with the people who can impact them 🤔explain the reasoning behind each metric 🔎make them visible and accessible 🫧create clear accountability I've consistently seen that teams who understand why they're tracking certain metrics perform better than those who are simply told what to track. What separates effective KPI frameworks from ineffective ones? Keep your regular reporting focused on metrics that are: 🔗directly linked to strategy 😕simple to understand ✔️actionable by your team ❤️🩹critical to business health But maintain other data points in your systems. They become valuable when investigating problems or identifying opportunities. If you're working on refining your KPI framework, what's the one metric that's transformed how you view your business performance? Want to dive deeper into building effective reporting structures for your scale-up? DM me for a copy of our KPI framework template. #techscaleup #startupmetrics #businessgrowth #datadrivendecisions