Pizza Sales Report
Five business insights from data Most Pizza Selling In 16-20 Hours. Most Selling Large Pizza Name of Thai Chicken. At 8242, Friday Had the Highest Sum Of Quantity And Was 36.57% Higher Than Sunday, Which Had The Lowest Sum Of Quantity At 6035. Quarter 2 Has Increase Pizza Quantity. Quarter 4 Has Decrease Pizza Quantity eyJrIjoiOTA1OGYwZWYtMjVhNS00NTY0LTgzNmYtNGJhZWJkNGE1ZTViIiwidCI6ImJmYjZiODdiLTViODUtNDkxMS1hYWMxLTJkODIyMThiOGQ4ZCIsImMiOjl9
Roadmap to Safety: Analyzing NYC Traffic Collision Trends & the 2020 Lockdown Anomaly
Overview This interactive Power BI dashboard transforms raw city incident logs into a comprehensive operational data story analyzing 75,000 traffic collisions across New York City. The primary objective of this report is to identify critical peak hours, high-risk geographic bottlenecks, and primary contributing factors to help urban planners and logistics managers optimize road safety initiatives. Key Business Insights Discovered: The Lockdown Anomaly: The report features a time-series analysis highlighting the dramatic drop in daily accidents precisely when the COVID-19 lockdown was enacted, providing clear historical context to the data. Peak Risk Windows: Utilizing a dual-axis matrix heatmap, the analysis isolates Friday afternoons (16:00 - 18:00) as the highest-density window for collisions across the city. Vulnerable Road Users: Dedicated analytics isolate the impact on cyclists and pedestrians, breaking down injury and fatality rates to pinpoint exactly which boroughs require immediate infrastructure intervention. Behavioral Drivers: The data clearly identifies "Driver Inattention/Distraction" and "Unsafe Speed" as the overwhelming leading causes of both total collisions and fatalities. Technical Features Built Into This Report: Advanced UX/UI Design: A seamless, custom-built left navigation panel that mirrors a premium SaaS application for intuitive user exploration. Dynamic Filtering: Integrated timeline slicers and borough cross-filtering to allow end-users to drill down from high-level KPIs to granular, street-level data. Data Density Control: Strategic use of conditional formatting matrixes and custom tooltips to display dense multi-dimensional data without cluttering the canvas. Data Source & References Primary Data Provider: Sourced via the Maven Analytics Data Playground repository. eyJrIjoiZGRkOTM2NjMtMGEwMS00OTc0LWIzNjctOTI3OWRkMzNiNDFkIiwidCI6ImY1MTg1YWY3LTQ2YmUtNDRhNS05MDkyLWM4ZWMwZmQ4ZDBhNyJ9๐ Powerful interactive calendar in Power BI ๐
Did you know how powerful the native Power BI ๐ฆ๐ฐ๐ฎ๐๐๐ฒ๐ฟ ๐๐ต๐ฎ๐ฟ๐ is? What if I told you that, ๐ฐ๐ผ๐บ๐ฏ๐ถ๐ป๐ฒ๐ฑ ๐๐ถ๐๐ต ๐ฎ ๐ ๐ฎ๐๐ฟ๐ถ๐ , it could create a ๐ณ๐๐น๐น๐ ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ฐ๐ฎ๐น๐ฒ๐ป๐ฑ๐ฎ๐ฟ? Back in 2019, I created a ๐๐๐ฒ๐ฝ-๐ฏ๐-๐๐๐ฒ๐ฝ ๐๐๐๐ผ๐ฟ๐ถ๐ฎ๐น to show how to build a fully interactive calendar in Power BI by using the Matrix visual. While it worked well, I knew I could improve it. This year, I wanted to explore a new technique by ๐ฐ๐ผ๐บ๐ฏ๐ถ๐ป๐ถ๐ป๐ด ๐๐ต๐ฒ ๐ ๐ฎ๐๐ฟ๐ถ๐ ๐๐ถ๐๐ต ๐๐ต๐ฒ ๐ฆ๐ฐ๐ฎ๐๐๐ฒ๐ฟ ๐๐ต๐ฎ๐ฟ๐, and here is the result. ๐ช๐ต๐ฎ๐โ๐ ๐ป๐ฒ๐ ๐ถ๐ป ๐๐ต๐ถ๐ ๐๐ฒ๐ฟ๐๐ถ๐ผ๐ป? โข A full-year view with statutory holidays for Sรฉnรฉgal, France, and Quรฉbec โข A refined layout leveraging Scatter and Matrix visuals (no custom visuals) โข A dynamic title built with a simple SVG โ no external images required โข Interactive filters to switch between years, regions, and ๐ฐ๐ผ๐น๐ผ๐ฟ ๐ช๐ฎ๐ป๐ ๐๐ผ ๐ฒ๐ ๐ฝ๐น๐ผ๐ฟ๐ฒ ๐ถ๐ ๐๐ผ๐๐ฟ๐๐ฒ๐น๐ณ? Iโm making the .๐ฝ๐ฏ๐ถ๐ file available for free so you can dive in, customize it, see the logic behind it, and let me know your thoughts. eyJrIjoiOTQyY2JmMjQtZTIxOS00ZTI1LTgwOTMtYTU1Yjg5ZGZkZjI2IiwidCI6ImE5ZTRlYjg3LWY4OTAtNGE3My05YTYwLTRiMmVjZGI3ZWY4YiJ9&embedImagePlaceholder=true&pageName=75869a7237688a6574a1๐ Powerful Sales Performance Dashboard in Power BI ๐
๐๐ Hello, I'm Cheikh Abdou Seye, and I'm passionate about the dynamic world of data engineering, management, and visualization. I'm excited to share my latest personal Power BI project - a robust Sales Performance Dashboard, crafted entirely using native visuals only! This dashboard provides deep insights into sales data from a fictitious metropolitan French retail grocery company. ------------- โ ๏ธ Disclaimer Please note that the datasets used to showcase this Sales Performance Dashboard were randomly generated using ChatGPT and are intended for demo purposes only. The sales representatives' names displayed are also fictitious. Any resemblance to real persons or businesses is purely coincidental. -------------------------------------- Business rules -------------------------------------- All visuals are conditionally formatted based on these 4 rules: Leaders (green): meet or exceed both Revenue Performance Target (95%) and Gross Margin Target (45%) Revenue Performers (R-p) (yellow): meet or exceed Revenue Performance Target (95%) only Gross Margin Performers (GM-p) (orange): meet or exceed Gross Margin Target (45%) only Need Help (red): Not meet both Revenue Performance Target (95%) and Gross Margin Target (45%) -------------------------------------- Key Features -------------------------------------- ๐ KPIs: shows Actual vs Target, trends, and comparison against precious period (MoM and YoY) New card combined with SVG styling (gradient sparkline) New card combined with SVG styling (comparison) Dynamic number formatting (G, M, k, etc.) ๐บ๏ธ Maps: Shape map with custom metropolitan French regions and department maps (topoJSON) Conditional formatting based on Revenue performance & Gross margin. In-Page bookmark for a toggling map view change (Region vs Departement) ๐งพ Table: shows a ranking (top-to-bottom) of Revenue performance & Gross margin, by Product and Sales managers (Departement and Region) SVG Sparkline to show Revenue trend (last 60 days) SVG Styling with Conditional formatting by Products and Sales Manager (Departement and Region) Slicer built with Field parameter. ๐ Magic Quadran: shows Product and Sales Manager in 4 clusters (Leaders, R-p; GM-p and Need Help) Scatter chart Conditional formatting based on Revenue performance & Gross margin. ๐ Audio Wave: shows the variance between Revenue Target and Actuals Error bar Conditional formatting based on Revenue performance & Gross margin. Slicer built with Field parameter for Period selection (D, W, M, Q, Y) -------------------------- The user-guided tour is entirely crafted with native Power BI elements (button, rectangle, speech bubble, and line) and bookmarks -------------------------- I hope this dashboard is useful and inspiring! If you like the dashboard features, please leave a comment and/or give a kudo! ๐ eyJrIjoiZjMxZjU2NWQtZGFlYy00MTA4LTg3Y2MtNzE5MDFhOWVhYmFjIiwidCI6ImE5ZTRlYjg3LWY4OTAtNGE3My05YTYwLTRiMmVjZGI3ZWY4YiJ9&embedImagePlaceholder=true&pageName=ReportSection110d3e656840690c8d4eCOVID-19 NSW Transport Impact
Information This report combines OPAL tap-on/tap-off data from NSW Trains, Ferries, Buses, Light Rail & Metro. We use these data to compare public transport utilisation year over year (2020 vs 2019). The aim is to understand the impact of COVID-19 on passenger levels across all modes of public transport, using 2019 as our baseline. The common dimensions available in the report include Service, Fare Type and Transport Mode. A definition of these can be found below: Service: Refers to the service where the tap-on/tap-off occurred e.g. Manly Ferry Fare Type: Refers to the type of fare for which the tap-on/tap-off occurred e.g. Adult Transport Mode: Refers to the mode of transport where the tap-on/tap-off occured e.g. Train Data Sources All data is sourced from the NSW Transport Open Data website - links are below: Ferry: https://opendata.transport.nsw.gov.au/dataset/opal-trips-ferry Bus: https://opendata.transport.nsw.gov.au/dataset/opal-trips-bus Train (Includes Metro): https://opendata.transport.nsw.gov.au/dataset/opal-trips-train Light Rail: https://opendata.transport.nsw.gov.au/dataset/opal-trips-light-rail Key Metrics Passengers 2019: Sum of Passengers for 2019 (Excluding months that are yet to occur in 2020 e.g. September). Passengers 2020 Sum of Passengers for 2020 Absolute Change Year over Year: Passengers 2020 - Passengers 2019 Percentage Change Year over Year: (Passengers 2020 - Passengers 2019) / Passengers 2019 Passengers Current Reporting Period: Sum of Passengers for the latest reporting month Passengers Current Reporting Period Previous Year: Sum of Passengers for the eyJrIjoiNzcxNTdlY2ItYjFhYS00YjkyLThkMmEtMTQwYjVlYzExNjA0IiwidCI6ImQyYWY4YjViLTlhN2UtNGM4NS1hM2ZkLWI2OWE2Njk4YjdkNiJ9COVID-19 Information Dashboard
Covid-19 Information Dashboard Brand new report which provides information about the pandemic across the world. There is a detailed analysis on a country level and also shows the trend across a specified date period. Content of the Covid-19 Information Dashboard Whole report has 7 pages Each one of the pages has buttons on the top where you can navigate to other dashboards 1st Page: Gives an overview of the Covid-19 evolution of the selected country as well as the trend from 01/03/2020 onwards. There are also 4 buttons, in the middle of the page, by clicking on them, you can see the trend of the country, and the comparison against the trend of the continent this country belongs to. 2nd Page: There is a series of metrics displayed on the table and also the trend of 2 key metrics (Confirmed Cases ฮบฮฑฮน Deaths) for the first 10 countries, is shown on 2 line graphs at the top 3rd Page: In this page the information is displayed on maps. There is also a donut chart where the user can drill down on dimensions 4th Page: The data are shown on decomposition trees where someone can see which countries have the highest number of cases and the time period these cases are observed 5th Page: The trends of the 2 key metrics (Confirmed Cases and Deaths) for the top 20 countries, are shown on multiple small boxes 6th Page: Click the play button and you will see the evolution of Covid-19 from 01/02/2020 till yesterday's date. The graps and the maps will be updated automatically 7th Page: Latest COVID News Source of Information The source of the data is Johns Hopkins University eyJrIjoiMmY0YTk2NDctNTcxMy00ZTNjLThlMWItYjQ0ODY3ZmMyOTU2IiwidCI6IjIyYTc3MDZlLTA4ZTItNDc5Mi05YjA4LWJhNmE5MGRlNzkzMSIsImMiOjl9How Covid-19 strikes the bridal industry
Summary As COVID-19 pandemic forcing people to abstain from Social life and plunging businesses to its edge. The bridal industry is facing its own set of challenges, both short term, and long term. As the global economy plummets down, there will be a high impact on people's spending. Among all the expenses, people will tend to cut back the wedding expense the most, which means more and more people will go for simple and in-expensive weddings. This Case study gives a synopsis of one wedding franchise who is dealing with this sudden surge and how they are mitigating these challenges. Challenges Difficulties in understanding the most affected area in bridal fashion Absence of analyzing in-store and virtual sales Decentralized system for analyzing multiple sectors Lack of high level KPIs for quick decision making Solution Our solution enabled the following capabilities to address customer challenges: Ability to quickly compare between in-store and virtual sales performance Identifying sectors that are most affected by Covid-19 Analyzing appointment wise sales for various sectors KPIs / Stats (4) +45% +56% Increase in virtual sales Increase in total registrations +28.4% +24% Increase in category wise performance Product sales growth www.infocepts.com eyJrIjoiNTA2Y2E2MDEtN2FlMS00ZDk5LTg3NjctZTE4M2RiNjc0YzM5IiwidCI6IjA0ZWM2MTA5LTRjNzktNGM3My1hZTcxLWE0NzRjMDlhMWY1YSJ9๐ Weather tracker in Power BI ๐
This Weather tracker showcases Power BIโs native powerโthere are no custom visuals, just smart design, and native functionalities! โจ Whatโs cool about this report? ๐ The data comes from the VisualCrossing Weather Data API (free version). ๐ It updates 3 times daily and shows current weather based on the userโs local time zone. ๐ก Visual elements include: Animated Cards to display todayโs weather. A Line Chart for hourly trends. A Title Slicer for a glance at the daily forecast. A city selector to compare weather across locations. ๐ง Biggest challenge? Setting up a parameter to handle different local time zones. But with some problem-solving, itโs now running like a charm! ๐๐ If youโre inspired or have questions, give me Kudos and/or leave a comment Hope this tracker is useful and inspiring eyJrIjoiMzhiMDI1YTgtOThmMi00YWM5LWFmZWUtOGU1MTU0Zjg5ZmMyIiwidCI6ImE5ZTRlYjg3LWY4OTAtNGE3My05YTYwLTRiMmVjZGI3ZWY4YiJ9&embedImagePlaceholder=true๐ The Interactive Magic Quadrant of Best Power BI YouTube Instructors ๐
It all started with a simple question: โ๐๐ฉ๐ฆ๐ณ๐ฆ ๐ค๐ข๐ฏ ๐ ๐ง๐ช๐ฏ๐ฅ ๐ต๐ฉ๐ฆ ๐ฃ๐ฆ๐ด๐ต ๐๐ฐ๐ธ๐ฆ๐ณ ๐๐ ๐ต๐ถ๐ต๐ฐ๐ณ๐ช๐ข๐ญ๐ด?โ ๐ค At first, I shared recommendations (books, YouTube channels, blog posts) with people who reached out on LinkedIn. But as these requests grew, I thoughtโฆ Why not use data to find the answer? Inspired by Gartnerโs famous Magic Quadrant (check it out here: https://www.gartner.com/doc/reprints?id=1-2HVUGEM6&ct=240620&st=sb), I took a data-driven approach! Using the free YouTube Data API in a Python script (Thanks to ChatGPT), I analyzed Power BI instructors from all over the world, regardless of their country or language. The result? This interactive Magic Quadrant in Power BI, designed to help learners easily find the instructor that best suits their needs - no matter where theyโre from or what language they speak. Who said Globalization? ๐ ๐ ๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ถ๐ ๐๐ผ๐ฟ๐ธ? To be included in the quadrant, a YouTube channel must meet these criteria: โ ๐ญ๐ฌ,๐ฌ๐ฌ๐ฌ+ subscribers โ The keyword "๐ฃ๐ผ๐๐ฒ๐ฟ ๐I" must appear in the channelโs title or description โ ๐ฃ๐ผ๐ฝ๐๐น๐ฎ๐ฟ๐ถ๐๐ ๐ฆ๐ฐ๐ผ๐ฟ๐ฒ (๐-๐ข๐น๐ช๐ด) โ Based on the daily average number of subscribers, weighted by the percentage of Power BI-related videos on the channel โ ๐๐ฝ๐ฝ๐ฟ๐ฒ๐ฐ๐ถ๐ฎ๐๐ถ๐ผ๐ป ๐ฆ๐ฐ๐ผ๐ฟ๐ฒ (Y-๐ข๐น๐ช๐ด) โ Based on the ratio of likes to views, weighted by how consistently the creator publishes Power BI content (๐๐น: 4 ๐ท๐ช๐ฅ๐ฆ๐ฐ๐ด ๐ฑ๐ฆ๐ณ ๐ฎ๐ฐ๐ฏ๐ต๐ฉ) Using these criteria, I categorized Power BI YouTube instructors into four distinct groups: ๐ด ๐๐ฒ๐ฎ๐ฑ๐ฒ๐ฟ๐ โ Highly popular and widely respected. Their content is trusted by thousands, making them the go-to experts. (Ex: Guy in a Cube, How to Power BI, Hashtag Treinamentos) ๐ฏ ๐ก๐ถ๐ฐ๐ต๐ฒ ๐๐ ๐ฝ๐ฒ๐ฟ๐๐ โ Deeply knowledgeable, though not as mainstream. If you're looking for advanced techniques and specialized insights, this is your goldmine. (Ex: Havens Consulting, Enterprise DNA, Power BI Na Prรกtica) ๐ ๐๐ฑ๐๐ฐ๐ฎ๐๐ผ๐ฟ๐ โ Large audiences, structured learning paths. Their content is great for beginners looking for a guided approach. (Ex: Microsoft Power BI, Leila Gharani, Chandoo) ๐ ๐๐ถ๐ฑ๐ฑ๐ฒ๐ป ๐๐ฒ๐บ๐ โ Lesser-known but with potential. These creators are on the rise and might be the next big names in Power BI education! (Ex: Pedro Paulo | Power Platform, IA & BI, Kenji Explains) ๐ก I built the entire quadrant using only Power BI's native (core) visuals (no custom visuals), just the power of out-of-the-box Power BI capabilities! ๐ ๐ค๐๐ฎ๐ฟ๐๐ฒ๐ฟ๐น๐ ๐จ๐ฝ๐ฑ๐ฎ๐๐ฒ๐! I know the Power BI community is dynamic, and new instructors emerge all the time. Thatโs why this quadrant will be updated every quarter to reflect the latest trends and rising stars in the space. ๐ Did the results surprise you? Looking at the quadrant, were there any shocking insights or unexpected names in the groups? Or do you think someone deserves a spot on the list? Let me know in the comments! ๐ ๐๐ Please give me kudos if you find this quadrant useful eyJrIjoiMjM2MmVjMzAtZWFmOS00MjA1LTkxZGQtMWU4N2QxMzBiZDIxIiwidCI6ImE5ZTRlYjg3LWY4OTAtNGE3My05YTYwLTRiMmVjZGI3ZWY4YiJ9&embedImagePlaceholder=true
