Ebola 2026 Outbreak Landscape
The 2026 Bundibugyo Ebola virus outbreak in the Democratic Republic of Congo is tracking rapidly to become the worst Ebola outbreak in history, even with vaccines currently in trials. I developed a simple report in Power BI to compare how deadly this strain is, compared to the biggest Ebola epidemic of 2014 in West Africa, and early COVID-19 globally. This report uses column charts, line charts, and a scatterplot to explore the risk landscape of the 2026 outbreak in its first 100 days. A few extra pages deep-dive into the 2026 outbreak to observe the growth of fatality rates and whether the outbreak is slowing down when viewed through a weekly rolling average. This analysis is first and foremost a data visualisation exercise, but it shows how serious Ebola outbreaks are. I hope the light at the end of the tunnel comes sooner. Many thanks to the good folks collating and managing the EVD 2026 data on GitHub.Consumer Financial Complaints Analytics
This project moved beyond simple reporting to deliver a structured, four-pillar analytical framework to understand, diagnose, and prescribe solutions for the consumer finance ecosystem. The Dashboard's Analytical Structure: Executive Snapshot: Volume, Trend, and Compliance. Objective: Provides an immediate, high-level view of the complaint landscape and overall system health (e.g., volume change, timely response rates). Root Cause Deep Dive: Product & Issue Drivers. Objective: Isolates the specific "what" and "why" behind the complaints (e.g., mortgages vs. credit reporting, and the specific issues within those products), allowing for targeted policy and product intervention. The 'Who': Company Accountability & Benchmarking. Objective: Compares company performance using metrics like "Substantiated Complaint Rate" and "Timely Response," identifying potential bad actors that require regulatory focus. Resolution Channels & Outcomes. Objective: A critical analysis of the resolution process. It answers: How timely and effective are company responses? Do outcomes differ based on the channel (Web, Phone, Mail) used for submission? Key Finding: By correlating submission channel and response speed (Page 4) with root cause (Page 2), the data reveals systemic bottlenecks that, if addressed, could dramatically increase resolution efficiency and strengthen consumer trust. This dashboard is designed to be an actionable regulatory intelligence tool. I'd welcome any feedback on the structure and analysis! eyJrIjoiOGM2OGFlN2YtZDY2MS00MDA3LTg3NjEtM2VmMjM5NWE4YzVlIiwidCI6IjkyNTcwMDE0LWZmM2QtNDAxMC04MTNkLTQxM2YwZmY5OWQ5MiJ9Oil & Gas Operations Dashboard | DataFlip
Running oil and gas operations means balancing production, equipment, maintenance, safety, and costs. When these areas are tracked through separate reports, it becomes difficult to spot downtime, underperforming sites, and rising costs early. This dashboard brings key operational data into one clear view, helping teams act faster and improve performance. Business Impact This dashboard helps organizations: Track production, revenue, downtime, maintenance, safety, and emissions Identify underperforming sites and unused capacity Analyze equipment downtime and maintenance costs Monitor asset reliability and operational efficiency Compare performance across sites and regions Track safety incidents and CO2 emissions Support faster, data-driven operational decisions It helps energy teams reduce downtime, improve asset performance, strengthen safety, and gain better control over operations. Who Can Use This Template Oil & Gas Operators Energy Managers Asset Performance Teams Maintenance Leaders HSE Teams Operations Executives Production Analysts Discover more insight-driven templates built for real business decisions. Visit DataFlipPizza 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 eyJrIjoiOTA1OGYwZWYtMjVhNS00NTY0LTgzNmYtNGJhZWJkNGE1ZTViIiwidCI6ImJmYjZiODdiLTViODUtNDkxMS1hYWMxLTJkODIyMThiOGQ4ZCIsImMiOjl9Global Conflicts Monitor
I built a Global Conflict Monitor that combines conflict event data from ACLED with UNHCR displacement statistics into one interactive report. The goal: Exploring patterns and connecting the dots between where violence is happening and where people are forced to flee. The dashboard covers 1.3M conflict events and 560M+ displaced persons worldwide, since 2015. Some of what's inside: Azure Maps visualizing conflict intensity across all countries, Smart Narratives that show dynamic insights as you filter the data, country deep-dives with demographic pyramids and trend analysis, and a scatter plot exploring whether more conflict leads to more displacement (check the 'Displacement Analysis' to see). I'd love to get your feedback, especially if you work in humanitarian response, conflicts analysis, peace building, etc. What else would you like to see on this tool? Feel free to connect on LinkedIn. eyJrIjoiZGE3ZWJjYmQtODg4Yi00MzM2LThkYTYtZjhhZjk3MzMxZmI4IiwidCI6ImQ4YWJjM2VkLWE2ZTEtNGUzNi1iMGFhLTQ1NzA3Zjc5M2YxMiJ9
Global AI Adoption & Workforce Impact
Artificial Intelligence is transforming organizations faster than workforce adaptation can keep pace. This analysis examines how AI adoption influenced productivity, employment outcomes, and reskilling efforts across 30 countries, 25 industries, and 8 skill categories between 2021 and 2024. The findings show that while AI adoption accelerated and supported productivity gains, workforce outcomes remained negative overall, resulting in a net loss of approximately 290K jobs across the study period. At the same time, significant differences emerged across industries, regions, and skill categories, highlighting areas where disruption is most concentrated. This report explores three key questions: How has AI adoption reshaped workforce outcomes? Where is AI delivering measurable value? Where should governments and employers prioritize reskilling investment? 🔗 Dataset : July 2026 DataDNA – Global AI Adoption Workforce Displacement Index Analytics Challenge eyJrIjoiYmEwOTgxYzctMGNkMC00NzMwLTlkMjctMDgwMmUwM2I5NDMwIiwidCI6IjUwOWU3OGY0LTQ0MTctNDkyNi05MzFjLTQ3YzVjNWQwMThlZCIsImMiOjF9Power BI Template for Inventory Analysis
Inventory problems can quietly eat away at your profits. Stockouts, delayed orders, and poor availability don’t just cause frustration, they cost your business real money and unhappy customers. Most teams only notice these problems after it’s too late. That’s where the Out-of-Stock Report Dashboard (Power BI Template) comes in. This ready-to-use tool gives you clear, real-time visibility into your inventory so you can spot issues early, take action fast, and keep your shelves stocked. Why This Dashboard Matters With simple visuals and smart insights, this template makes it easy for supply chain teams, warehouse managers, and operations heads to: Track stockouts in real time and know exactly which products are affected. Measure revenue loss caused by poor stock availability. See service levels and fill rates to understand how well you’re meeting demand. Monitor backorders and identify delayed fulfillment risks. Review inventory turnover to avoid constant understocking. Check stockout frequency and duration across items, categories, and time. Who Should Use It? This template is perfect for anyone managing stock and supply chains: Inventory Managers Supply Chain Analysts Warehouse Teams Operations Leaders eyJrIjoiZTcxMTBhYTktNTAwYy00OGU3LWI0Y2MtMDExZTJkZDBiMzQzIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9Roadmap 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. eyJrIjoiZGRkOTM2NjMtMGEwMS00OTc0LWIzNjctOTI3OWRkMzNiNDFkIiwidCI6ImY1MTg1YWY3LTQ2YmUtNDRhNS05MDkyLWM4ZWMwZmQ4ZDBhNyJ9Manufacturing KPI & Equipment Performance Template | DataFlip
Manufacturing teams often struggle with downtime, production losses, and unclear machine performance data. When operational issues are hidden in spreadsheets, it becomes difficult to improve efficiency and reduce waste. This dashboard brings production, equipment, and quality insights together in one view to help teams improve manufacturing performance. Business Impact This dashboard helps organizations: Track OEE performance across Availability, Performance, and Quality Identify downtime causes and machine performance issues Monitor runtime, downtime, utilization, and production output Analyze MTBF and equipment reliability trends Detect waste and quality losses by product and process Improve production efficiency with data-driven decisions Support preventive maintenance and continuous improvement It helps manufacturing teams reduce losses, improve equipment effectiveness, and increase production visibility. Who Can Use This Dashboard Manufacturing and Factory Operations Teams Plant Managers Production Supervisors Maintenance Teams Continuous Improvement Teams Manufacturing Analysts Discover more insight-driven templates built for real business decisions. Visit DataFlip eyJrIjoiOTg1YWU2ZGItZDdiYy00YjM2LWJiNDQtNWM2YWQ3NDVhYTExIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9
