Alex Goga

🚀Expanded Table is one of those DAX concepts that is often overlooked when learning how DAX works at its core. In this post (🔗in the comments) we'll uncover this important concept using both visuals and measure examples. Next, we hand it over to my friend ChatGPT only to find that AI is no substitute (yet) for learning DAX from reliable sources! 🤖 Big thanks to the #SML Crew for starting this thoughtful conversation on the value of teaching Expanded Table Concept to new Power BI students. Thanks for reading! #PowerBI #SML #DAX

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💡 Curious about your perspective One thing I’ve noticed in both data analytics and regulatory work is how often the real challenge isn’t the tools. It’s the translation. You can have the cleanest SQL query, the slickest Power BI dashboard, or the most bulletproof compliance framework… but if the audience doesn’t understand the story, the insight gets lost. That has me wondering: 👉 What’s the most effective way you’ve found to bridge the gap between technical detail and executive decision-making? Is it: 🔹 Simplifying visuals 🔹 Using analogies and storytelling 🔹 Leading with business outcomes first 🔹 Something else entirely? I’d love to hear what’s worked best for you, especially across industries like healthcare, medtech, finance, or beyond. #DataAnalytics #StorytellingWithData #RegulatoryAffairs #BusinessIntelligence #Leadership

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Have you ever notice a chart that doesn’t add up the way you expect? While building a Recruitment Priority Dashboard with Wisconsin workforce data, I saw my bar charts 📊 were showing 1 instead of the correct percentages. After exploring solutions with the assistance of ChatGPT, I realized the issue was the data type, not the visuals themselves. The solution was to convert the numbers from whole numbers to decimals in Power Query, and then the charts displayed correctly. It was a simple step that made the dashboard meaningful to the audience. If you run into similar data type issues, this short guide on changing data types in Power Query is helpful: https://lnkd.in/gm9FMdBf. This was a reminder that while using Power BI of the importance of solid problem solving and understanding the data structure.

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You can now create a #PowerBI composite semantic model using import mode and direct Lake mode. (Public preview of course) I had two somewhat polar reactions to this. First, awesome feature.There are some great use cases to bring together import mode data with something that is queries in real time. I'm looking at your Translytical task flows. We were doing this using Direct Query, and I'm excited to switch this to Direct Lake. However, on the opposite end, doesn't this just prove that Direct Lake ain't nowhere close to the speed of import mode? If it was just as fast, or didn't have any of the gotchas, you wouldn't need direct lake mode. #Fabric #Microsoft #data https://lnkd.in/ezrm5HNi

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Navigating the future of business intelligence means facing a crucial question: What's the best way to integrate AI into our data strategy? I've been following the conversations on Qlik News and Microsoft Fabric blog focused on AI in business, and the buzz around 𝙌𝙡𝙞𝙠 𝙎𝙚𝙣𝙨𝙚 𝙖𝙣𝙙 𝙈𝙞𝙘𝙧𝙤𝙨𝙤𝙛𝙩 𝙁𝙖𝙗𝙧𝙞𝙘 is electric. The debate isn't just about which platform is better; it's about two competing visions for the future of enterprise data. Microsoft Fabric is making a strong play with its unified, "all-in-one" platform, seamlessly integrating tools like Copilot and a new suite of AI functions directly into the data workflow. This promises to simplify the path from data to AI-powered insights. Meanwhile, Qlik is doubling down on a different approach. Their recent integration with Databricks AI Functions showcases a "best-of-breed" strategy, allowing businesses to leverage advanced AI models while retaining Qlik's powerful analytics capabilities. This offers flexibility and a deep associative data model that many users swear by. For data professionals and business leaders, this dynamic presents a fascinating challenge. Do you go all-in on a unified platform with out-of-the-box AI, or do you build a more modular data fabric with specialized tools? #DataAnalytics #BusinessIntelligence #AIinBusiness #MicrosoftFabric #QlikSense #DataStrategy #DigitalTransformation

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Bad data isn’t found in dashboards. It’s created at entry. If Salesforce reports can’t be trusted, start by using them as validation tools, not just visuals: • Find nulls, defaults, and impossible states • Group by Owner, Record Type, Integration User • If a “bad data” report hits row limits, that’s a process failure, not a reporting one Salesforce reports tell you where the data breaks. When that stops scaling, PowerBI’s transform aka Power Query helps explain why: • Data completeness scoring • Pattern and format validation • Duplicate detection beyond Salesforce matching rules • Identifying integration or release-related data regressions Cleaning data after it’s in the database is necessary—but it’s also expensive. The real fix is standards at entry: • Required fields that actually matter • Validation rules for format and logic • Flows that guide users instead of letting them fail • Guardrails that ensure data is trustworthy before it’s saved Rule of thumb: • Salesforce Reports → Where it’s broken • Power Query → Why it’s broken • Flows & Validations → Prevent it from happening again Bad data is rarely user error. It’s missing standards and weak automation. #Salesforce #DataQuality #PowerQuery #PowerBI #CRM #Automation #RevOps #TechDebt

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𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 Data visualization unlocks insights. I’ve built interactive Power BI dashboards with complex DAX expressions and row-level security, ensuring compliance and accessibility at Zoetis and Fifth Third Bank. These dashboards empowered stakeholders with actionable insights. 💡 Pro tip: Secure, user-friendly dashboards drive self-service analytics. What’s your go-to BI tool? Let’s discuss! #PowerBI #DataVisualization #Analytics

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If you are coming in and doing discovery on PowerBI reporting, a super useful approach is to download the free DAX Studio add-in and use the export functionality to create CSVs of every "table" within PowerBI. You can then use those as DBT seeds that you can perform routine compares against in your data build process to validate transactional accuracy and no surprises slipping into your dataset. This is particularly important if you are reverse engineering trusted datasets. Form a validated foundation, then you can remodel and have solid content to tie out to.

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Just got off a call with a fantastic data leader—no names, no invoices… just a question: "How should I respond to some org changes given our heavily federated model?" This comes up a lot. Centralized vs. decentralized. Combined engineering & analytics or split? Spoiler: there’s no one-size-fits-all. And whatever structure you pick shouldn’t be static. My take: Start by centralizing. Unify the team. Build a strong data culture. Level up your talent. Then revisit decentralization from a position of strength. I've seen it pay off—intake to delivery gets faster, and team members get exposure across the data spectrum (shoutout to the data governance analyst who turned into a killer analytics engineer). Yes, there are trade-offs. Prioritization becomes a political game. Context can get diluted. But that’s where process and vision come in. If you set the right foundation, you can shift later without chaos. Honestly, one of my favorite questions is still: "Ryan, who should the data team report to?" (Ask five companies, get six answers.) As data becomes a mature profession, these decisions only get harder. But they’re the right conversations to be having. How is your team structured today—and is it working?

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Who is responsible for trusted reporting and analytics in a large organization? Not one person. Many. Trust in reporting is never accidental. It is the result of multiple actors fulfilling their roles across the entire data lifecycle. Quality is compounded, positively or negatively, at every step. 1️⃣ Data creators Those entering data must do more than follow a prescribed format. They must respect agreed standards, workflows, and the intended use of the system. Data captured without discipline creates downstream distortion. 2️⃣ Data processing mechanisms Transformation, storage, aggregation, and formatting processes shape raw data into something usable. If these processes are flawed, misaligned, or poorly governed, the output may no longer mean what users think it means. 3️⃣ System configurators The choices made around classifications, definitions, and permissible values are not neutral. They must be guided by true subject matter experts who steward the integrity of the system on behalf of the entire organization, not for local or individual convenience. 4️⃣ Data engineers and integration teams Those who connect systems, design pipelines, and move data for reporting purposes carry enormous responsibility. Integration errors or ungoverned transformations silently erode trust. 5️⃣ Dashboard and report developers Visualization is interpretation. The way metrics are calculated, filtered, and presented directly influences decision-making. Clarity, consistency, and alignment with commonly agreed definitions are essential. Across all of this, standards must be defined by business owners and technical experts, and actively monitored. Governance is not a document; it is a living discipline. If trust in reporting is weak, don’t kill the messenger. Ask instead: “What is my role in strengthening it?”

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You trained the model. You trusted the output. But can you trace the data that led to the decision? 👀 AI in analytics isn’t dangerous because it’s wrong. It’s dangerous because it’s convincing, even when it’s wrong. 📰 This article breaks down the real risks of AI-powered insights and how to design for trust with traceability, governance, and human oversight. Read it here: https://lnkd.in/eKuQnv7J

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Your Tableau license isn't your BI strategy. I've watched organizations spend six figures on visualization tools while the data feeding them was incomplete, unmapped, and ungoverned. Beautiful dashboards. Confident mistakes. Real BI leadership starts three layers deeper: → ETL pipeline integrity → Automated data quality monitoring → Governance frameworks with accountability built in → Source validation before anything hits the reporting layer I've built these systems at Wells Fargo, First Horizon, and BNY Mellon. The pattern is always the same — organizations that invest in the foundation make better decisions. The ones that skip it just make faster ones. Fix the pipeline before you polish the dashboard. Are you investing in your data foundation or just your front end? 👇

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Please stop calling every pipeline a “data product.” I see it a lot, teams proudly calling their dashboards or ETL pipelines “data products.” But a real data product is way more than just moving or storing data. It’s something people can trust, use, and actually make decisions with. It has clear ownership, context, and guarantees for the businesses relying on it. From my experience running enterprise analytics projects, the difference between a pipeline and a true data product is night and day. Here’s what sets it apart: - Purpose drives design: A true data product solves a specific business problem, not just generates numbers. - Context is key: Metadata, lineage, and business meaning make data understandable and actionable. - Accessibility is non-negotiable: It should be discoverable, self-service, and seamlessly integrated into workflows. - Operational guarantees: Reliability, freshness, and consistent behavior are what separate a product from a pipeline. Pipelines are the engine, but the data product is the experience for the user. Without the human-led strategy, governance, and adoption focus, AI or automation alone cannot turn raw data into decision-making power. When was the last time your team built a “data product” that people actually relied on, and not just admired on a slide?

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From EHR to Insights: How Clinical Data Powers Healthcare Analytics 🏥📊 Electronic Health Records (EHR) contain a massive amount of clinical data. When analyzed properly, this data can transform healthcare decisions and improve patient outcomes. Here’s how clinical data from EHR drives real impact: 👩‍⚕️ Patient Outcome Analysis Clinical data such as diagnoses, medications, and lab results help analysts identify patterns that improve treatment outcomes and patient care. 🌍 Population Health Management Healthcare organizations analyze EHR data to monitor chronic diseases, track preventive care, and identify high-risk patient populations. 🔁 Reducing Hospital Readmissions By analyzing historical patient records, analysts can uncover patterns that contribute to readmissions and support proactive care strategies. ⚙️ Operational Efficiency EHR data helps hospitals optimize workflows, improve resource allocation, and enhance overall operational performance. 🔮 Predictive Analytics Using advanced analytics and machine learning, healthcare systems can predict potential complications, disease progression, and emergency visits. 📋 Quality & Compliance Reporting EHR data supports regulatory reporting and helps healthcare organizations track quality measures and performance metrics. 💡 Key Takeaway Clinical data in EHR systems is more than documentation—it’s a powerful asset that enables data-driven healthcare decisions and improves patient care. #HealthcareAnalytics #DataAnalytics #EHR #ClinicalData #HealthIT #PowerBI #SQL #DataDrivenHealthcare #HealthTech #HealthcareInnovation

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In 2026 we would experience a profound transformation💡in how Agentic systems would be designed: From: “How do I craft the perfect prompt for Agent?” ➡️ To: “How do I design the entire information ecosystem for Agent?” From: “Just add more examples to the prompt” ➡️ To: “Build strategic retrieval mechanisms, caching layers, and memory hierarchies” From: “Let’s hope the model figures it out (Magic 😃)” ➡️ To: “Continuously measure, instrument, and optimise context at every level”

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WellStack's platform, powered by Snowflake, is transforming how healthcare organizations leverage their data: ▪️ 70% faster data processing ▪️ 90% reduction in metrics execution time ▪️ 40% lower total cost of ownership  ▪️ 650+ billion records processed Real impact: Wisconsin Health Collaborative now processes 200+ billion patient records from 35+ sources, delivering quality measures that directly improve patient outcomes. "WellStack's data platform with Snowflake is 25x faster than the prior solution." - Gabrielle Rude, PhD, WCHQ CEO #HealthcareData #DataAnalytics #HealthTech #ValueBasedCare Learn more: wellstack.ai

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Data cleaning isn’t just a technical step—it’s the foundation of reliable insights. At Horizons Statistical Consulting, we’ve seen how poor-quality data leads to poor-quality decisions. That’s why we emphasize the importance of cleaning and validating data before any analysis. * Reliable data builds trust * Clean insights = stronger, evidence-based strategies * Saves time, reduces rework, and cuts costs Simply put: clean data = smart decisions. Since 2014, we’ve been helping organizations in Oman ensure their research and analysis are based on accurate, trustworthy data. Ready to transform your raw data into powerful insights? Let’s talk. #dataanalysis #datascience #database #data #marketdata #dataprotection #growth #growthmindset #growthegame #growthesport #growthjourney #growthhacking #growthstrategy #growthmindset🌱 #growth #BusinessGrowth #businessgrowth #businessgrowthtips #BusinessGrowth2025 #BusinessGrowthTools #businessgrowthmentor #businessgrowthsystems #businessgrowthstrategy #businessgrowthstrategies #DataCleaning #DataQuality #DataDriven #Research #OmanBusiness #Horizons

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