Abiola A. David, MSc, MVP

🚀 Unlocking the Power of DAX in Power BI! 🚀 As a data analyst, one of the most powerful tools in my arsenal is DAX (Data Analysis Expressions). It’s a formula language that enhances data modeling and analysis in Power BI, enabling me to create complex calculations and dynamic measures that transform raw data into insightful, actionable information. 🔍 Why DAX? Dynamic Calculations: DAX allows me to build custom calculations, aggregating data across different dimensions with ease. Enhanced Data Modeling: It enhances relationships between tables, making data models more robust and efficient. Real-time Insights: With DAX, I can create real-time KPIs, trend analyses, and detailed reports that provide deeper business insights. 🌟 Key Features of DAX: CALCULATE Function: To modify context and perform calculations. FILTER Function: To filter data dynamically and refine insights. SUMX, AVERAGEX: To perform row-by-row calculations across tables, providing precise measures. 🎯 Examples in Action: Creating dynamic sales forecasts. Building performance metrics tailored to specific business needs. Developing complex calculations for profit margins and ROI analysis. DAX is not just a tool; it’s a game-changer in transforming data into a strategic asset. Whether you’re a beginner or looking to sharpen your skills, diving deep into DAX can significantly enhance your data analytics capabilities in Power BI. Let’s connect and discuss how we can leverage DAX to drive better decision-making and business growth! 🚀📊 #PowerBI #DAX #DataAnalysis #BusinessIntelligence #DataScience #Analytics #DataDriven #PowerBICommunity

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Dear Linkedln Family, As a Data Analyst, mastering Power BI is essential for several key reasons: Data Visualization: Turn complex datasets into actionable insights with clear and interactive visuals. 🌐 Efficiency: Automate data cleaning, transformation, and reporting to save time for deeper analysis. ⏳ Real-Time Insights: Make data-driven decisions with up-to-date dashboards and reports. 📈 Seamless Integration: Connect with multiple data sources, ensuring scalability and flexibility across projects. 🔗 #DataAnalytics #PowerBI #DataVisualization #DataDriven #BusinessIntelligence

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I just completed "Introduction to DAX in PowerBI". This journey into the world of DAX has been eye-opening. I’ve deepened my understanding of key concepts like DAX functions, calculated columns and measures, row vs. filter context, and the power of variables with VAR. I’ve also tackled more advanced topics, including iterator functions, using CALCULATE to filter measures, creating calculated tables, and mastering Date and Time intelligence. I am excited to put these new skills to work and continue exploring what DAX can do! #PowerBI #DAX #DataAnalytics #DataAnalyst

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Hello people 👋 I recently worked on this US Resident Sales Data sent to me using Power BI. The following are the methodology used: 1️⃣ Problem statement 2️⃣ Data ingestion into Power Query 3️⃣ Data normalization 4️⃣ Creation of Dax measures 5️⃣ Exploratory data analysis 6️⃣ Data storytelling 7️⃣ Business recommendations 8️⃣ Executive summary 🫱Kindly go through the report for insights and business recommendations. Here is the link to my GitHub repository: 🔗https://lnkd.in/dMGrcGfm 👉🏻 follow me for more updates and share to your network. ✨Ensure you follow me on GitHub to learn about exciting data science projects.✨ Happy learning! #powerbi #dataanalysis #dataingestion #datanormalization #exploratorydataanalysis #reporting #datastorytelling

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Thanks for sharing, Pelumi. Yes, they deserve it. Some of the benefits are: Date tables give accurate grouping by years, quarters, months, weeks, days and more. They create consistent relationships between multiple datasets for solid time-based analysis just like you've said. Also, you can use advanced time functions like SAMEPERIODLASTYEAR, TOTALYTD and more as they work properly only with a date table. This is just a tip of the iceberg as there are more benefits to unravel from date tables. Hence, deserve even more than the hype they get. I create date tables using DAX expression in the front-end part of Power BI.

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🚄 Exciting Insights from UK Train Data! 🚄 🔍 Delving into the fascinating world of data analytics, I recently had the opportunity to explore insights from @Maven Analytics #mavenrailchallenge (https://lnkd.in/gHtp7mMc)  on UK train routes. The findings are truly eye-opening and shed light on passenger behaviours, revenue patterns, and transit experiences. 🌟 One standout revelation is the popularity of routes like Manchester Piccadilly to Liverpool Lime Street, suggesting a bustling connection between these cities. Meanwhile, the correlation between early morning and early evening travel preferences hints at the importance of these times for daily routines, possibly linked to work shifts. 🕒 Notably, there's a significant surge in morning travel, likely driven by work commutes and school schedules. This underscores the pivotal role of data in understanding consumer behaviour and optimizing service offerings. 💼 As someone passionate about data analytics, I'm thrilled to announce that I'm open to new opportunities—whether full-time, part-time, or freelance. If you're seeking a data analyst with a knack for uncovering actionable insights, let's connect! 💡 Additionally, the data highlights strategic opportunities for revenue optimization, particularly regarding the pricing power of first-class tickets and the impact of delays on refunds. 🔗 If you're interested in leveraging data analytics to drive informed decision-making in your organization, let's collaborate! I'm available on platforms like Upwork (https://lnkd.in/g7vCxrgJ) and Fiverr (https://lnkd.in/gUm7MQdh) to provide tailored data analytics solutions to meet your needs. ✨ Let's harness the power of data to unlock growth and innovation together. Reach out, and let's embark on this exciting journey! 🚀 #DataAnalytics #UKTrains #Insights #Opportunity #DataDrivenDecisionMaking #Freelance #Upwork #Fiverr #ConnectToday

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DAX in Power BI: The Superpower Behind Every Data Analyst and Developer When working with Power BI, DAX (Data Analysis Expressions) is the secret sauce that transforms raw data into meaningful insights, whether you're a Data Analyst or a Power BI Developer. 🧠 As a Data Analyst, DAX allows you to: Create meaningful metrics like revenue growth, customer retention, and profitability. Perform advanced data analysis, from time intelligence calculations (like YoY growth) to complex filtering. Build impactful reports that tell a compelling data story for decision-making. For a Power BI Developer, DAX helps you: Optimize model performance by crafting efficient measures and calculated columns. Design dynamic, interactive dashboards with real-time KPIs that update automatically. Handle complex relationships and conditions across multiple datasets, ensuring accuracy in all visualizations. Whether you're analyzing data for business insights or building advanced dashboards, DAX is your essential toolkit. Mastering it unlocks the true power of data in Power BI! 💡 What DAX functions have you found most useful in your projects? Share below! 👇 #PowerBI #DAX #DataAnalysis #DataAnalyst #PowerBIDeveloper #DataAnalytics #BusinessIntelligence

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Learning DAX today was a bit challenging, but in the end, I overcame. I had challenges understanding 𝗔𝗟𝗟𝗘𝗫𝗖𝗘𝗣𝗧 and 𝗔𝗟𝗟𝗦𝗘𝗟𝗘𝗖𝗧𝗘𝗗 functions however, I understood after watching YouTube videos. 𝗗𝗔𝗬 𝟴: 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗳𝗶𝗹𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝟭. 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗧𝗮𝗯𝗹𝗲𝘀: These are virtual tables created using functions like FILTER Profit (quantity over 3) = CALCULATE( [Profit], FILTER( 'sales by store', 'sales by store'[Quantity] >3) The expression above evaluates the filter before evaluating the filtered virtual table against the profit measure. 𝟮. 𝗔𝗟𝗟(): Ignores all filters, and returns all rows in column/table. However, It doesn't accept virtual tables. 𝟯. 𝗗𝗜𝗦𝗧𝗜𝗡𝗖𝗧(): Returns a column of unique values when a column name is given. It returns all combinations of values, if a table name is given. 𝟰. 𝗩𝗔𝗟𝗨𝗘𝗦(): Similar to 𝗗𝗜𝗦𝗧𝗜𝗡𝗖𝗧(), except that it also returns blanks. 𝟱. 𝗦𝗘𝗟𝗘𝗖𝗧𝗘𝗗𝗩𝗔𝗟𝗨𝗘(): Returns a value when there's only a value in a specified column, Otherwise returns an alternate result. This means if you do not specify alternate result and there's more than one vale in specified column, it returns nothing. 𝟲. 𝗔𝗟𝗟𝗘𝗫𝗖𝗘𝗣𝗧(): Removes all filters in a table except filters applied to specified columns in the query. Also, 𝗔𝗟𝗟𝗘𝗫𝗖𝗘𝗣𝗧 can access other columns within the expanded version of base table. 𝟳. 𝗔𝗟𝗟𝗦𝗘𝗟𝗘𝗖𝗧𝗘𝗗(): It keeps external filters but removes all filters specified in the query. It respects other filters except row and column filters in table visual. Follow me to learn more! #DAX #DataAnalysis

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You know what I have noticed in the courses I have done in Power BI, I have not noticed a section about storytelling or best visual practices. The amount of reports I have seen on LinkedIn looks like that aspect has been glossed over. (Too many different colours, dev labels and titles, to name a few) It is vital to tell a story with your visuals in data, otherwise you have lost them and it can be hard to get your audience back #PowerBI #Bestpractices #datastorytelling

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The Top 18 Most Common Issues when Troubleshooting the Performance of a Data Model in Power BI ➡️ Non Optimised Data Loads ➡️ Slow Report Interactions ➡️ Complex Calculations Affecting Performance ➡️ High Memory Usage ➡️ Inaccurate Data Display ➡️ Inconsistent Row-Level Security ➡️ Excessive Visual Load Times ➡️ High Cardinality Slowing Model ➡️ Frequent Full Refreshes Overloading System ➡️ Excessive Measures ➡️ Managing Data Security Across Roles ➡️ Inefficient Use of Hierarchies ➡️ Calculations Not Matching Business Logic ➡️ Difficulty Understanding DAX Formulas ➡️ Need for Aggregated Data for High-Level Views ➡️ Trouble Identifying Table Purpose or Structure ➡️ Unexpected Relationships or Data Linkage Errors #PowerBI

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Harnessing the Power of Data Visualization in Data Analysis 📊 As a data analyst, my role is not just about crunching numbers but about telling a compelling story through data. Visualization is the bridge that connects complex data sets to actionable insights, making it an indispensable tool in our arsenal. 🔍 Why Data Visualization Matters: 1. Clarity: Transforms raw data into clear, understandable visuals. 2. Insight: Highlights trends, patterns, and anomalies that might be missed in tabular data. 3. Engagement: Captivates the audience, making data-driven insights more accessible and impactful. 🛠️ Tools of the Trade: - Power BI: Dynamic dashboards and interactive reports. - Tableau: Sophisticated visual analytics. - Excel: Quick and effective data representation. 💡 Recent Project Highlight: I recently worked on a sales order analysis project where I used Power BI to visualize sales performance across different regions and product categories. The insights gained helped drive strategic decisions, optimizing sales strategies and improving overall performance. In the world of data analysis, a picture truly is worth a thousand words. Let’s continue to turn data into visually compelling stories that drive informed decision-making! #DataAnalysis #DataVisualization #PowerBI #Tableau #DataStorytelling #Analytics

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Using GIFs as Background Images in Power BI 🎨✨ I recently found that We can use GIFs as report background to make it more interactive. We can do this simply using PowerPoint>Stock Video>Save as GIF or creating GIF Using Canva and add them as report background.   For detailed step by step tutorial you can checkout this video: https://lnkd.in/dDkBDS_7 #powerBI #report #dashboard #theme #microsoftfabric

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Just starting with data modelling? Start with the fundamentals, 1. Learn the basics of normalisation (1NF, 2NF, 3NF—it’s simpler than it sounds).  2. Understand the difference between OLAP and OLTP. 3. Know your facts and dimensions—they’re the foundation of good design.  4. Look into the One Big Table approach (when and why to use it).  5. Get familiar with surrogate keys and why they simplify things.  6. Figure out how to handle Slowly Changing Dimensions (SCD) for evolving data. Keep it simple, take it step by step, and you’ll get there! 👍  #DataEngineering #DataModeling

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𝐈𝐭'𝐬 𝐭𝐨𝐝𝐚𝐲! ❗❗ Launch or improve your data analysis career by joining our free data analysis masterclass this Saturday. We'll provide clarity into: ✅ How to start your data Journey ✅ How to navigate the Job market ✅ How to analyze data ✅ How to Visualize and create Interactive dashboard with Power BI ✅ and more Date: Saturday 28th September 2024 Time: 8 pm - 9 pm Link to register: https://lnkd.in/d5_XFQ55 Thanks to Easy Technologies Academy for this great opportunity 𝐃𝐨𝐧'𝐭 𝐌𝐢𝐬𝐬 𝐨𝐮𝐭! #learn #data #dataanalyis #tech #teach #powerBI #train

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"Can I get the stats for our reach, impressions and engagements on LinkedIn over the last 12 months, please?" You must have gotten such that request or something similar, in the course of your work. As a Data analyst, I do get something similar very often. The data analyst and statistics are inseparable. This is because statistics is the collection, manipulation, analysis and presentation of data. That is basically the job of an analyst. There are different types of statistics that focus on different things to serve different purposes from the same data set. Most data analysts tend to focus on one aspect of statistics called Descriptive statistics. Descriptive statistics focuses on what is in hand. It states facts and paints a clear picture of what already happened. It responds with number of impressions and engagement, tells if an increase or decrease was observed, and that's it. This level of statistics is very important and serves as the base line for informed decision making. But, there is more... A different type of statistics known as inferential statistics focuses on finding relationships between the different statistical figures generated from the descriptive stats, and serves the purpose of determining how significant that increase or decrease is. It takes a sample of that data, tests the variables to find out how they relate with each other and inferes that the increase or decrease was either due to a new intervention or by random acts of fate. This level of statistics brings clarity to why what happened could have happened, eliminating every assumption. There is yet another called Predictive statistics but I won't be talking about that in this post. However, from the name, we can already deduce what it does. One thing that must be dealt with, no matter the type of statistics being carried out, is outliers. Outliers are data points that differ significantly from other data points in a data set. Ignore outliers, and whatever statistics produced, will be unreliable. Just one outlier in a dataset can significantly alter the outcome of your statistics. I'll add that no type of statistics is superior to the other. They are focused on different things in the data, and serve different purposes, which bottom line is to move the business forward. #Leadership #growth #datanalytics #statistics

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The Data Analysis Process: Insights from a Data Analyst As a data analyst, transforming raw data into actionable insights is key. Here's a streamlined look at my process: 1. Defining the Problem Collaborate with stakeholders to clarify objectives and outcomes. 2. Data Collection Gather data from various sources ensuring relevance and accuracy. 3. Data Cleaning Handle missing values, remove duplicates, and correct errors to ensure reliability. 4. Data Exploration Understand data characteristics through summary statistics and visualizations. 5. Data Analysis Apply statistical methods and machine learning to uncover insights. 6. Data Visualization Create clear and concise charts and dashboards using tools like Power BI and Tableau. 7. Interpretation and Reporting Prepare reports and presentations to share findings and actionable recommendations. Data analysis is iterative, and continuous improvement is essential. By following this process, I help turn data into meaningful insights for informed decision-making #DataAnalysisProcess #DataAnalysis #DataAnalytics #DataAnalyst #Data #Visualsation #Charts #Analysis

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I've been asked to develop a book titled "Data Analysis with Microsoft Excel and Power BI" for a Global Technical Learning Content Provider and book publisher. I unfortunately don't have the time. If you have the skills and experience and what to leave a mark, then leave a comment and I'll put you in touch with the right person. I don't have all the details - I'll leave it up to you to find out. Who will take up the challenge? #PowerBI

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Are you looking for a way to efficiently perform complex calculations on your data in Power BI? Look no further than Measures! Unlike Calculated Columns, Measures do not take up space in your dataset and do not refresh with the data, making them a great option for performing aggregated calculations. Measures are particularly useful for performing complex aggregation calculations like Averages, Mean, Median, Count, Count distinct, and other arithmetic operations. They work on filter context and are great for making Matrix Visuals that show aggregated data based on different filters. For example, you can create a measure that calculates the average unit price of a product based on location. To create measures, all you need to do is use Dax, just like with calculated columns. So next time you're working in Power BI, consider using measures to efficiently perform complex calculations on your data without taking up valuable space in your dataset. #PowerBI #DataAnalysis #DataVisualization #BusinessIntelligence #DAX #Measures #Analytics #DataModeling #DataDriven #BI #PowerBIPro #MicrosoftPowerBI #DataAnalytics #DataInsights #DataScience #BusinessAnalytics #DataManagement #DataVisualization #DataMining #DataModel #DecisionMaking

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