Talha Khan

Learning and understanding the concept and functions of Power BI it won't take much time.May be less than a month you can understand. But If you want to get rich experience or get pro on that and get real time experience it will take a few years. minimum 3 to 5 years. here is the small example... Watching a movie or a series will take less than a week.But if you want to take or produce a movie or series you need a year of experience and knowledge. think and act wisely 😉

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3 Data Engineering Myths to Leave Behind in 2025: 1- More Data Means Better Insights It’s easy to think that the more data you have, the better your insights will be. But quality always beats quantity. Too much irrelevant data can actually slow down processing and clutter up valuable insights. Focusing on targeted, high-quality data is where the real value lies. 2- Manual Monitoring is Sufficient for System Reliability Relying solely on manual checks or basic monitoring tools is risky, especially as systems get more complex. Manual processes leave room for error and slow down response times. Today’s best systems use AI-driven monitoring to catch issues in real time and keep operations stable. 3- One Data Pipeline Fits All Data engineering needs are as unique as the companies behind them. A one-size-fits-all pipeline rarely meets the specific demands of large-scale, dynamic environments. Customizing pipelines for specific workflows, data types, and performance needs can make all the difference in speed and reliability. Here’s what I’d do instead: 1- Focus on Data Quality over Volume. Make sure data is relevant, clean, and ready to use. High-quality data gets you to insights faster without the excess noise. 2- Adopt Real-Time, AI-Powered Monitoring. Systems that monitor and adjust on their own save time and reduce risk, especially as complexity grows. 3- Customize Pipelines Based on Specific Needs. Tailored pipelines mean faster, more reliable data processing that’s aligned with the company’s unique requirements. As data engineering continues to evolve, leaving these myths behind can help teams stay efficient, scalable, and ready for growth. #DataEngineering #TechMyths #RealTimeMonitoring #DataQuality #ScalableSolutions

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In your Analytics career you can choose between 1. working within a single industry or domain, or 2. jumping around between different industries There are advantages to both! Sticking to one industry: - You probably get more replies when applying - You hit the ground running faster in a new job - Your domain knowledge compounds over time Jumping around: - Wider pool of job openings available to you - Get exposed to a wider variety of tools & methods - On average, it helps you grow more as a generalist Based on your experience, what else would you add? And what do you prefer? #data #analytics #career

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AI is reshaping analytics: Discover how the latest Gartner Report predicts the future of BI 🤖📈 As a data professional who's been in the trenches of analytics, insights, and predictive modeling, I'm thrilled to share the latest industry benchmark. Here's what you need to know: 1. Microsoft Power BI continues its reign 👑   - Leader for the 17th consecutive year   - Positioned furthest right for Completeness of Vision   - Highest in Ability to Execute for 6th year running 2. Google makes a strong showing 💪   - Named a Leader, showcasing their innovative approach   - Integrating AI to revolutionize data interactions 3. Oracle joins the Leaders quadrant 🏆   - Recognized for its robust analytics capabilities What does this mean for us data professionals? It's time to upskill and adapt! The landscape is evolving rapidly, with AI integration becoming a game-changer. Whether you're in data engineering, visualization, or predictive modeling, staying ahead of these trends is crucial. What's your take on this year's Magic Quadrant? Which platform are you betting on for the future of analytics? Share your thoughts below! 👇 #GartnerMagicQuadrant #BusinessIntelligence #DataAnalytics #AnalyticsTrends #MarketResearch #DataInsights #BIReporting #TechInnovation #DigitalTransformation #DataDrivenDecisions

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"Feel free to recommend" Findings from my #Tableau Project. Recommendations for Reducing Customer Churn Focus on Saudi Arabia as a Key Growth Region: - Saudi Arabia has demonstrated a strong customer base with a lower churn rate compared to other regions. This makes it an ideal market for expanding engagement initiatives and deepening customer relationships. The bank should consider investing in targeted campaigns to reinforce loyalty, such as exclusive financial products, region-specific promotions, and localized customer support services. Introduce Enhanced Offers for Blue Card Members: - Blue card members represent a significant portion of the customer base but may require additional incentives to increase retention and spending. Implementing tailored rewards programs, such as cashback offers, points multipliers, or tier-based loyalty benefits, can encourage greater engagement and spending while reducing the likelihood of attrition. Improve Retention for Short-Tenure Customers: - Newer customers with shorter tenures are more prone to churn. The bank should create onboarding strategies, such as personalized welcome packages, introductory rewards, or financial health workshops, to build strong relationships early and increase long-term loyalty. Address High-Utilization Customers with Financial Support: - Customers with high credit utilization and lower limits are at risk of attrition due to financial strain. Offering proactive solutions, such as credit line increases, financial counseling, or payment assistance plans, can reduce financial stress and build trust with these customers." Expand Engagement with Low-Income Segments: -Customers in lower-income categories often churn due to limited product fit or engagement. Developing affordable, tailored financial products—such as low-fee accounts or micro-loans—can cater to their needs while increasing customer satisfaction. Leverage Regional Insights to Target High-Churn Areas: Regions with higher churn rates should be prioritized for retention campaigns. For example, introducing location-specific customer appreciation events, or creating partnerships with local businesses, can strengthen community ties and reduce attrition.

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Those are the common mistakes I’ve seen in dashboard design ⬇️ 1️⃣Not Considering the Audience and Solving a Problem Understanding your audience and the problems they are trying to solve is crucial. Tailor your dashboard to meet their specific needs and ensure it addresses their pain points effectively. 2️⃣Overloading with Information Focus on the most important metrics and avoid cramming everything onto one screen. For example, instead of displaying every possible metric, highlight KPIs that align with your goals. 3️⃣Poor Choice of Visuals Ensure that your visuals match the data you’re presenting. For instance, don't use a pie chart to show trends over time; a line chart is more appropriate for that purpose. 4️⃣Lack of Context Data without context can be misleading. Always provide comparisons, benchmarks, or historical data to give users a complete picture. 5️⃣Inconsistent Design Inconsistent use of colors, fonts, and styles can make your dashboard look unprofessional and hard to read. Stick to a consistent design scheme that aligns with your brand. 6️⃣Ignoring User Experience (UX) A dashboard should be intuitive and easy to navigate. For example, adding clear labels, tooltips, and interactive filters can enhance usability. 💡Avoiding these mistakes can make a significant difference in the effectiveness of your dashboards. A well-designed dashboard is not just about aesthetics but about making data accessible and actionable! #DashboardDesign #DataVisualization #BusinessIntelligence #DataDriven

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Someone asked me to Explain #Star_Schema in data Modelling to a layman's understanding and I said " the Fact table contains the Noun and Verb while Dimension tables contain the adjectives" pls add Yours #data #datanalysis #powerbi #Datavisualization

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The Data Analyst's Guide to Reproducibility (Part 1): https://wix.to/3cWX9tq #newblogpost #dataanalysis #reproducibility #newdataanalysts #careergrowth #datadocumentation #dataanalysistips

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Last week, we learned how to creating report parameters. 😎 This week, we're tackling a common yet critical issue: sorting problems. 😅 Power BI Tips Ep.13: การสร้างรีพอร์ตพารามิเตอร์ https://lnkd.in/g3sKdkcR Power BI Tips Ep.14: แก้ข้อมูลเรียงผิด https://lnkd.in/gf2y3qEn

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A new tool won't solve your data problems. The things that will help solve your data problems are: 1) Buy in from senior management Want your solutions to be adopted? Get buy in. Want to stop doing small requests to solve bigger problems? Get buy in. Want to improve data quality? Get buy in. 2) A focus on process Got a process that takes far too long? A new tool won't fix that. A better process will. Seeing people send round loads of Excel files? A new tool won't fix that. A better process will. Seeing a high error rate in a particular area? A new tool won't fix that. A better process will. 3) Education and skills across the organisation Want people to get more out of data? A new tool won't fix that. Training and support will. Want to increase time for analysis in your data team? A new tool won't fix that. Training and support will. Want to use data to change your business? A new tool won't fix that. Training and support will. Ever been drawn in by the slick sales process? Seen some fancy charts? And then watched as your business failed to adopt the new tool? It's an expensive approach. There are better ways. Discover more of my posts by following hashtag #datatranslator #data #analytics #shiny

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When working as a #DataAnalyst, don’t let overconfidence lead you to dive into the data without clear objectives. Always start by asking the right questions and understanding what the analysis needs to achieve. 📊 Clear goals are essential for meaningful insights—without them, you risk getting lost in irrelevant details. 💡 #DataScience #Analytics #Learning #Data #BusinessIntelligence #Growth

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🚀 Unlocking the Power of Data: Transforming Numbers into Impactful Stories! In today’s world, data isn’t just a buzzword—it’s the driving force behind every major decision, innovation, and transformation. As a Data Scientist, I’m on a mission to turn raw numbers into actionable insights that fuel progress. 🔍 Whether it’s predicting trends, optimizing processes, or uncovering hidden patterns, data is the key that opens doors to new possibilities. I’m passionate about not just crunching numbers but also making data accessible and meaningful to everyone, from business owners to smart investors. 💡 If you’re curious about how data can revolutionize your business or improve your decision-making, let’s connect! Together, we can explore the untapped potential within your data. #DataScience #BigData #Innovation #DataDriven #MachineLearning #AI #SmartDecisions

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Switch between days, weeks, and months in reports⭐💡 You can now view your line chart data by day, week, or month, depending on the level of detail you need. More on : https://lnkd.in/ggDbQiPT #analytics #dataAnalysis #GA4 #whatsnew #googleanalytics

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People, if your data pipelines are as precarious as this then spend some time tackling those, they will come back and haunt you. #dataengineer #datawarehouse #datapipeline #dataleaders

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Well Well Well !!! Look who is taking the stage on our next DataPub event. One and only Mahmoud Yassin from Booking.com will be talking about power of internal data marketplace in dealing with Data Mess, a topic which is very close to my heart. Please do register & RSVP for the event at the link below. Do reach out to me and Daniëlle Bourgondiën for any questions or clarifications. https://lnkd.in/eq-fPuM4 #datapub #datamess #datamesh #cloud #snowflake #microsoft #dataproducts #lakehouse #mesh #data #dataanalytics

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Conducting another session on PowerQuery in Dubai. What is PowerQuery is Excel ? It is use to clean your messy data without writing formulas, you can combine sheet and files on few clicks.

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One of the big things Analytics hiring managers look for is a candidate's capacity to receive feedback and use it to grow. Nobody is perfect, including you (or me!) So as a Data Analyst, regardless of your level - you have to be able to hear critical feedback about your work and respond positively. - you can't shut down - you can't argue the point right away - you have to ask questions to understand - you have to be willing to admit you could do better During the interview process: if I'm on the fence about a candidate but they ask for feedback during the interview, it almost always pushes me towards moving them forward. I already know they're seeking to improve. I won't have to worry about that because I know they're on top of it. So if you're interviewing, think about asking a question like - What's 1 thing I could do to improve my analysis? - Would you have approached that problem differently? - Do you have any hesitations about my ability to succeed? #data #analytics #interviews

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In the fast-paced and dynamic field of 𝗱𝗮𝘁𝗮 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, keeping up with the latest knowledge is crucial 🚀 One invaluable resource for data professionals looking to enhance their expertise is the 𝗗𝗔𝗠𝗔-𝗗𝗠𝗕𝗢𝗞 (Data Management Body of Knowledge) from DAMA International. This book is a must-read for every data professional. 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘄𝗵𝘆 𝗶𝗻 𝗺𝘆 𝗹𝗮𝘁𝗲𝘀𝘁 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗼𝗻 𝗠𝗲𝗱𝗶𝘂𝗺. https://lnkd.in/daZkwq_q #data #datamanagement #dama #dmbok #datagovernance #dataquality

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Sometimes the biggest wins with Data are powered in the most unexciting ways: - reconciling two data sources - a lot of CASE WHEN in a SQL statement - a few bar charts & line charts on a new dashboard - asking "why?" several times until you reach the bottom So remember, you don't have to be using ML or AI or the latest trendy tool to make an impact. A good spreadsheet, a simple query and some common-sense visualizations will often get you where you need to go. #data #analytics #simplicity

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