Vivek Halai

Nando’s faced some data problems before their big platform upgrade. In our new post on 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐭𝐨 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬, we share how Nando’s overcame these problems and transformed their data platform. By using data mesh and data contracts, they found smart ways to improve their data setup, packed with advice for any data professional. Want to see how they fixed their data problems and upgraded their system? https://lnkd.in/gkRRrzVM Ben Watson's Original Post: https://lnkd.in/g9bzE3hi Andrew Jones's Post: https://lnkd.in/gbe45AFh Check out their story and see if their approach could work for your next data challenge. (Created by Erfan H. and Hasan Geren) #dataengineering #datamesh #datacontracts #nandos

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𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐭𝐨 𝐃𝐚𝐭𝐚 𝐀𝐝𝐯𝐨𝐜𝐚𝐭𝐞𝐬 Recently, I was asked by someone new to data engineering whether our role is solely to assist business users with their reporting requirements. While our primary task as data engineers is to support the reporting and ML needs of our business users, our access to vast amounts of raw data places us in a unique position. We can analyze this data and offer valuable insights on how our business partners can leverage it to uncover new dimensions and propel the business forward. It's crucial to meet our business users' requirements by building robust data pipelines and converting raw data into structured formats. However, I firmly believe that our role should also involve guiding users on what data is available, how it can be used, and what insights can be derived from it. This is possible when we build business knowledge on the datasets we use. 𝐁𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: Don’t just build data pipelines to meet business requirements, also empower users with insights that drive informed business decisions. I am curious to know if you guys agree to it or have a different perspective ? #dataengineer #data #analysis #analytics #business #datalake

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Take that time out of your busy schedules to develop a skill and you will see that really out of no time is really a time. I am happy to showcase my new skill and start into data analytic world 💃😁 #Dataanalytics #Datascience #Excel #dataexploration #data insight

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Exploring the Line Chart Power BI Visualisation I decided to use the Line Chart to visualise the results of this online store delivering and selling products to the UK and Europe in 2019 and 2020. According to the result, the category with the highest purchase in 2019 is clothing and very low in 2020. Appliances became the highest in 2020 in aspect of category and the second highest category in 2019. From that analysis, many customers order appliances more than other categories. Portugal and France are miles ahead in terms of the country they supply. Many customers are from France and Portugal in both 2019 and 2020. In 2019, France was the highest; in 2020, Portugal had the highest order. In terms of the device used to place the orders, many customers use the PC to place their orders over other devices such as smartphones or tablets. From the above analysis, if the store desires to improve sales, it must enforce the following recommendations: 1. Place great preference on advertisements targeting France and Portugal over other European countries. 2. Their online advertisement must be more PC-friendly than smartphones and other devices. 3. For categories of products, high priority should be placed on making clothing, appliances, smartphones, books and electronics always available than other categories. #datavisualisation #dataanalytics #dataanalysts #datagovernance #data #powerbi #dataquality #tech #datastory

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Some non data food of thoughts for data professionals which are equally important as hard skills to thrive in longer run. 1.Develop a Strategic Muscle Examine the problem critically before solving it. Quantify value proposition of the problem using well defined and tangible metrics which are aligned with long term goals. 2.Get the Basics Right Understand core fundamentals of the business and it’s consumers. Have thorough understanding of the tools, its functionalities and limitations before applying them. Keep problem at the center and work towards a solution,rather aligning problem to the solution. 3.Remain Abreast Be proactive and stay updated on long-term vision and key initiatives of the business. Cultivate good listening skills, stay informed about the latest trends in your field, ask pertinent questions and reflect on these insight by engaging with your peers & leaders. 4.Be a Good Storyteller Storytelling is a tough art to nail, if not done well your insights might go unnoticed.Harness the power of visualization, focus on summarizing insights emphasizing the impact. Make your storytelling interactive by encouraging audience to participate. More in details here 👇🏼

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How much does data accuracy matter? In this case - quite a lot! While data folk bang on about data quality and accuracy, not all of it matters that much. In some cases, you just need an indication that something is working or isn't. In other cases, it is vital that your data is 100% accurate. Particularly when it deals with people's lives. In the energy industry, I have seen data that would lead to debt collectors being sent to a person's house. A person who had never been supplied by that company. Now that matters. The government knowing you are alive. That also matters. Discover more of my posts by following hashtag #datatranslator #data #analytics #dataquality

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Truly, Perfection is often unrealistic, especially when the situation is complex. it's less about delivering a flawless solution and more about making smart trade-offs that push things forward. Focusing on what delivers the most value, even if it's not perfect is what really drives results

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I would like to thank you all for the incredible support and love you've shown for the past 30-day SQL knowledge-sharing session. Your engagement and positive feedback have been truly inspiring. 🙌 Given the enthusiasm and appreciation you've expressed, I'm excited to announce that I will be launching a 30-day SQL series on solving Real-World Business Problems in eCommerce, FMCG, and Retail domain starting tomorrow. I hope you continue to support and enjoy these upcoming posts as much as you have the previous ones. You will gain a holistic understanding of the challenges Data Analysts face in these domains and the solutions they develop. This series will also help enhance your critical thinking and problem-solving abilities. Here are the day-wise topics for the upcoming 30-day series: Day 1: Identifying Best-Selling Categories Day 2: Analyzing Seasonal Sales Trends Day 3: Customer Retention Analysis Day 4: Product Return Rates Analysis Day 5: Sales Performance by Region Day 6: Identifying High-Value Customers Day 7: Analyzing Discount Effectiveness Day 8: Inventory Turnover Analysis Day 9: Daily Sales Forecasting Day 10: Supplier Performance Analysis Day 11: Customer Purchase Frequency Day 12: Revenue Impact of Promotions Day 13: Customer Lifetime Value Calculation Day 14: Sales Conversion Rate Analysis Day 15: Product Cross-Selling Opportunities Day 16: Analyzing Customer Feedback Scores Day 17: Stockout Event Analysis Day 18: Average Order Value Trends Day 19: Customer Segmentation Analysis Day 20: Price Elasticity of Demand Day 21: Sales Channel Performance Day 22: Identifying At-Risk Customers Day 23: Analyzing Cart Abandonment Rates Day 24: Supplier Lead Time Analysis Day 25: Analyzing Repeat Purchase Rates Day 26: Marketing Campaign ROI Day 27: Product Profitability Analysis Day 28: Analyzing Shipping Times Day 29: Predicting Stock Replenishment Needs Day 30: Customer Demographic Analysis Thank you once again for your support. Let's continue this journey together and dive deeper into the intricate world of business analytics in eCommerce, FMCG, and Retail. Stay tuned and keep sharing your feedback! 🤗 #DataAnalytics #SQL #eCommerce #FMCG #Retail #BusinessProblems #DataSolutions #CriticalThinking #ProblemSolving #DataScience #TechCommunity #LearningJourney #ContinuousImprovement #DataDriven #BusinessIntelligence Shashank Singh 🇮🇳 Rishabh Mishra Munna Das Chandeep Chhabra Ankit Bansal Shail Sahu Shakra Shamim Naveen Kumar Adapala Ayan Khan Zain Ul Hassan

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I have successfully completed the Databricks Lakehouse Fundamentals certification! The Databricks Lakehouse platform combines the best of data warehouses and data lakes, allowing seamless management of both structured and unstructured data. This course has deepened my understanding of unified data architectures, Delta Lake, and how to drive faster data insights and AI adoption. Looking forward to applying these concepts in future projects, pushing the boundaries of data engineering and analytics! 💡 #databricks #lakehouse #dataengineering #bigdata #cloudcomputing #deltaLake #AI #ML

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Often in the world of analytics, there is a model to run or a process to follow. Learning the process can be quick But understanding the process takes much longer Some key areas can help build that understanding: Speaking to senior people in the organisation to understand how the process helps the business achieves its goals Delving into the detail, can you follow a line of data through the process. Can you understand the inputs and the outputs? Where do they come from? Where do they go? Some parts of the process will click and others will take much longer to grasp. Understanding the bigger picture helps ensure you don't get bogged down in analysis with limited business impact. What do you think you could do to increase your knowledge of a process? Discover more of my posts by following hashtag #datatranslator #data #analytics #process

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How do you feel when you achieve a new target? It really feels great. Personally, it's feels like I have won a lottery. My 3 months data analytics training and mentorship with Zion Tech Hub is gradually winding up. We started from Excel ➡ Power Bi ➡ SQL Zion Tech Hub actually saved the best for the last which is Structured Query Language (SQL). Practice creates permeance, I have started writing queries like a pro. Below is the entity relationship diagram (ERD) of the database I created. I truly admit that I have become equipped and more and would want to explore SQL using real-life datasets. What more can I explore using SQL. If you are an expert in SQL, I would love you to share your insights on SQL challenges and solutions. More so, what more can I do to become a pro. #SQL #continiouslearining

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🎧 Data Career Lessons & AI Insights with Megan Bowers 🚀💡 I just finished listening to an insightful podcast with Megan Bowers and Maven Analytics In this episode, Megan shares valuable lessons from her journey in data and some exciting insights into the future of AI. If you're looking to grow your career in data, this is one you don’t want to miss! 📖 5 Simple Takeaways: Stay Curious Data is always evolving. Keep learning to stay ahead of the game. AI is the Future Embrace AI— it’s not just a buzzword. Understand how it can amplify your work. Build a Network Connect with like-minded professionals. Learning from others can accelerate your growth. Find Your Niche Data is broad, but finding your niche makes you an expert. Focus on a field you’re passionate about. Don’t Be Afraid to Fail Failure is a stepping stone to success. Every mistake teaches a valuable lesson. 💡 My Reflection: As someone who’s growing in the data world, Megan’s advice hit home. Her journey is inspiring, and I’m excited to apply these lessons to my own path. 🎯 📌 Listen to the full podcast here :https://lnkd.in/gCxPWHAD and get ready to level up your data career! 🎙️💻 💬 What’s your biggest lesson in your data journey? Share in the comments below! 👇 #DataCareer #AI #MavenAnalytics #MeganBowers #DataScience #CareerGrowth #DataInsights #TechLeadership #ArtificialIntelligence #LearningJourney #NetworkingInTech

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This is the best platform for both beginners and non-beginners. Adjusting the settings of Power BI to enable proper outcomes. Understanding the interface and workflow of Power BI. Importing data from different sources, web, CSV, excel, databases etc. Front-end and back-end of Power BI (Power Query Editor). Adequate training on the power query editor ranging from: Text tools numerical tools date and time tools rolling calendar index and conditional columns calculated column practice grouping and aggregating pivoting and unpivoting merging and appending queries only to mention a few. DATA MODEL: I had the primary and deep understanding of the following: Data modeling database normalization fact and dimension table primary and foreign keys creating table relationship managing and editing relationships star and snowflakes schema etc. DATA ANALYSIS EXPRESSIONS (DAX): Dax vs M language Creating columns and measures using Dax, so many formulas etc VISUALIZING DATA WITH REPORT: The key things to know before presenting your report, starting from knowing your audience, what information you are presenting, and the right visuals to use. On this, I will be uploading more reports. ARTIFICIAL INTELLIGENCE: Anomaly Detection Smart Narratives Q&A visuals Decomposition Tres Key Influencers The Optimization tools and new Power BI features. Most importantly the bonus dataset is given for learners to practice. Maven Analytics Thank you a million times. #25hourswellspent #PowerBI

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How Regression Analysis Helped Me Simplify Complex Data Relationships Ever felt overwhelmed by data? With so many variables at play, it’s challenging to pinpoint what truly drives your results. That’s where regression analysis comes in. It transforms complexity into clarity. I remember the first time I tackled customer churn data. (Customer churn data is information collected about customers who have stopped doing business with a company or service. This data typically includes details such as: When the customer stopped using the product or service, Why they stopped, Customer characteristics, Interactions with the company). The numbers were daunting, and every variable felt significant. The real challenge was figuring out which ones mattered most. That’s when regression analysis became my secret weapon, simplifying the chaos and revealing the key factors impacting churn. No more guesswork, just actionable insights that drove our strategy. Want to unlock the power of your data? Regression analysis enables you to predict future outcomes and identify what truly matters. This is how I got started: 🔸 Understand relationships between variables. 🔸 Use it for predicting customer churn or optimizing marketing strategies. 🔸 Begin with linear regression to grasp the basics, then explore advanced models like logistic regression. By applying regression analysis, I was able to identify critical predictors of churn, allowing the company to implement data-driven strategies that reduced customer loss. Master regression, and you’ll find that the complexities of your data become manageable leading to more informed, strategic decisions. PS. Do you use regression analysis in your work? What’s been your biggest 'aha!' moment? ♻️ Repost this if regression has helped you, too—let’s spread the knowledge! #dataanalysis #regressionanalysis #datascience #data #analytics #statistics #machinelearning #customerchurn #predictiveanalytics #datadriven #businessintelligence #datastorytelling #datasciencetips #datainsights #askmeanything #shareyourexperience #followme #like #comment #repost

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Day 79: Exploring ARRAY Data Types Today's focus was on ARRAY data types in PostgreSQL, which allow storing multiple values in a single column. This can simplify data organization and retrieval for certain use cases. Example query: SELECT my_array_column[1] AS first_value FROM my_table; Excited to keep progressing 💃 #DataAnalysis #SQL #LearningJourney #90dayschallenge

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New Dashboard ✨ Hospital ER Dashboard Excited to share another #RWFD dashboard I've been working on, this time visualising Hospital ER mock data 💙 🌐 Explore this dashboard on Tableau Public: https://lnkd.in/ekNipqAS Key Features – Flexible Aggregation: View data by week, month, or year. – Interactive Filtering: Filter the dashboard by interacting with charts. – Monthly Date Selector: Set and adjust monthly dates. – PDF Export: Download the dashboard as a PDF. – Detailed Table View: Examine data in a comprehensive table. Inspired by Numa Begum and Cintra Jaggernauth 🌸 Thanks Mark Bradbourne, CBIP and Jacqui Moore for another great dataset! #datafam #dataviz #dashboard #healthcare #emergancy #tableau #design #dataanalytics

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Excited to Complete the Data Engineering Workshop by Snowflake! A great learning experience that enhanced my skills. Looking forward to applying these insights to drive impactful data solutions! #DataEngineering #Snowflake #LearningJourney

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