Fluxo e Caixa / Contas a Receber / Inadimplências
Originalmente desenvolvi esse dashboard para um cliente, integrado com SAP Hana e ambiente de Datawarehousing, troquei os dados e fiz uma versão de demonstração. Este dashboard consta com: Fluxo e Caixa: a comum tabela de fluxo e caixa com 4 opções de grão de data, além de páginas designadas apenas para recebimentos e pagamentos, além de opções de detalhamento. Contas a Receber: Análise temporal poderosa, insights prontos, formatação condicional, detalhamento em grande parte da página, comparação temporal, comparação com o que estava vencido, média de pagamento por cliente, média de atraso por cliente, análises com tooltips. Inadimplência: insights prontos, análise com foco no cliente, comparação temporal, análise de média de atraso, detalhamento em grande parte do dashboard, análises com tooltips. Além disso, a maioria dos visuais possui uma explicação do valor exibido ao parar com o mouse no visual e clicando no "?" que aparecerá. Quanto já foi pago? Quanto será pago? Quanto está vencido? Que títulos vencerão daqui 7 dias? Que títulos vencerão esse mês? Quem cobrar primeiro? Qual a média de atraso desse cliente? Qual o valor dos títulos desse cliente em média? Qual foi a última compra desse cliente? A quanto tempo está vencido esse título? Quais títulos estavam vencidos mês passado e foram pagos esse mês? Esse dashboard responde rapidamente essas perguntas e muitas outras.
African Gig Economy & Digital Wallet Risk Analysis
For the August #DataDNA challenge, I developed a Power BI report focused on understanding fraud exposure, fraud drivers, and operational risk across a digital wallet ecosystem serving the African gig economy. The objective was to move beyond reporting metrics and answer three business questions: What is the current risk exposure? What factors are driving fraud risk? Where should controls and monitoring be strengthened? To support this analysis, the report was structured into three sections: Executive Risk Overview Assess transaction performance, fraud exposure, disputes, and reversals. Fraud Driver Analysis Identify the customer, behavioural, and transaction characteristics associated with elevated fraud risk. Operational Risk & Controls Evaluate dispute patterns, reversal behaviour, transaction timing, and market risk indicators to identify opportunities for intervention. Key Observations Fraud exposure was more closely associated with customer behaviour, transaction type, and channel usage than demographics alone. USSD channels and Cash-In transactions consistently recorded elevated fraud rates. Month-end periods showed increased cash-out activity and reversal risk. Observed fraud exposure did not always align with expected market risk levels. The report also incorporates recent Power BI enhancements, including Donut Chart Center Value and Drop-down Slicer features introduced in the August Power BI update. Source: https://datadna.onyxdata.co.uk/challenges/august-2026-datadna-african-gig-economy-and-digital-wallet-analytics-challenge/Sales and Financial Performance
Sales and Finance Dashboard Why a unified Sales & Finance dashboard matters (and what it actually fixes) The Importance of This Dashboard Most teams split sales tracking from financial reporting. Sales sees revenue; Finance sees cost. They rarely meet in one place until it’s too late. This dashboard exists to close that gap. It connects execution to profitability. You can move from an executive summary down to the individual transaction without switching tools. It shifts the conversation from “How much did we sell?” to “What did we actually earn?” Profit, COGS, margin, and cost-per-unit are built in—not calculated separately. It serves both micro and macro decisions. The same structure supports a rep reviewing their profile, a manager coaching a team, or a leadership team reviewing monthly trends. It replaces scattered reports with one interactive source. Year, country, category, and product filters apply across views, so the data stays consistent. Key Benefits of Having It For Sales Managers & Team Leads Manage by profit contribution, not just revenue. Identify which countries, categories, or users are pulling the team down. Compare team/rep performance on a common baseline. For Finance / Controllers / CFO Monitor true profitability by geography and product line. See cost structure (COGS) directly against revenue without manual reconciliation. Validate margin efficiency at a glance. For Executives / Leadership Get a high-level pulse (revenue, profit, shipments, trends) instantly. Spot concentration risk and monthly shifts without waiting for static reports. Make faster strategic calls backed by consistent data. For Operations / Analysts Drill from summary to line-item detail when needed. Filter, search, and audit individual transactions by date, rep, country, or product. Reduce time spent stitching spreadsheets together. For Individual Sales Reps Understand their full impact—revenue, units, profit, and margin—not just a top-line number. Track YoY progress in one view rather than across multiple files. Bottom Line Benefit You stop making decisions on half the story. Revenue without cost is misleading; cost without sales context is blind. This brings both into one workflow, faster decisions, clearer accountability, and less reporting overhead. For more, visit: https://arbazahmad-bi.netlify.app/ Thank you.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! eyJrIjoiOGM2OGFlN2YtZDY2MS00MDA3LTg3NjEtM2VmMjM5NWE4YzVlIiwidCI6IjkyNTcwMDE0LWZmM2QtNDAxMC04MTNkLTQxM2YwZmY5OWQ5MiJ9OneLake security data access role replication
This Python notebook provides a basic example for OneLake security data access roles replication between different environments. At the moment: OneLake security roles are only deployed through Fabric deployment pipelines, which only covers the roles defined, not the actual Entra ID user/group member assignments (just permission group assignments). On its latest version, fabric-cicd does not deploy OneLake security roles. How it works: Through the OneLake security REST APIs, we can take the existing role definition and assignments from a specific environment and replicate them to another entirely. The example relies on two specific methods: List Data Access Roles Create Or Update Data Access Roles This way, we can: Persist/modify the already assigned Entra ID users and/or groups to the existing roles from one lakehouse to another. Point the existing permission group assignments to the target (workspace + lakehouse). Credentials for the whole process are provided through NotebookUtils. ⚠️IMPORTANT The process, as it is, overwrites the existing roles and assignments at the target with the source's configuration. Any roles and/or assignments non declared at the source will be deleted. The identity running the process must have at least the Member role in the referenced workspaces. When running under a service principal, token management could require some adjustments, specially if the code does not run inside Fabric. You can find the notebook here, feel free to adapt the code as needed. For example, once tweaked, it could be used as an step inside an ADO/GitHub pipeline and deploy the existing OneLake roles defined over each lakehouse while replicating the roles established and updating the user/group assignments depending on the deployment environment, complementing fabric-cicd deployments. Another possible implementation can be done with Terraform. If interested in managing Fabric with Terraform, you can take a look at my GitHub repo. Hope you find it useful!Toy Store Revenue Dashboard | DataFlip
Running a toy store means tracking sales, products, and customer trends across multiple channels. Without clear insights, it's difficult to know which products generate the most revenue or which marketing efforts drive the best results. This dashboard brings all your key retail metrics into one place, helping you make smarter business decisions. Business Impact This dashboard helps businesses: Track sales, revenue, profit, orders, and conversion performance Identify top-selling and most profitable products Compare performance across sales channels, campaigns, and devices Understand customer buying behavior and traffic sources Monitor refunds and returns to protect profit margins Improve merchandising and marketing with data-driven insights It helps retailers increase sales, improve profitability, and reduce manual reporting. Who Can Use This Template Toy Retailers Retail Executives E-commerce Managers Category Managers Merchandising Teams Discover more insight-driven templates built for real business decisions. Visit DataFlip eyJrIjoiYWJkNjQ2NzEtYzNlYi00ZWFjLTkwNzAtNmMzOTY2OTA1NDdiIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9Line Segment and Legend Color Based on Measures
Conditional formatting in Power BI just got a solid upgrade. In the July 2026 update, conditional formatting now works on Line Charts and visuals with Legends. This was one of the most requested features. It lets you apply data-driven colors across multiple visual types and keep colors consistent throughout your reports. Up until recently, when I used to interview Power BI professionals with a lot of experience in visualization, I had a good way to test them. I'd ask one of these questions: Which visuals don't support conditional formatting? I made some changes to my visual and now the conditional formatting option isn't showing. Why? How do you apply conditional formatting on Line and Pie visuals? It was an easy way to catch gaps. Conditional formatting wasn't supported on legends. For Pie visuals, the workaround was to apply it on a Bar chart first and then switch to Pie. For Line visuals, we used to get extra color markers the same way. Power BI has been improving its visualization experience steadily over the last two years, with big updates coming regularly. This long-awaited feature finally made it into the July 2026 update. Conditional formatting is now supported on Line visuals and Legends. Why it was needed on Line visuals Just like any other visual, you sometimes need line segments to change color based on a condition. This helps explain things like margins going down or current year vs previous year comparisons. Why it was needed on Legends Say your company always uses one color and your competitor uses another. On a report showing market share, you'd always want your company represented by the same color. That was possible for axis values but not for legends. This update fixes that. You can now apply conditional formatting on Pie, Stacked, and any other visual that uses a legend. Please find the file where I have used different measures to do conditional formatting on Line and Legends Measure used Brand Color = SWITCH ( TRUE (), Max('Item'[Brand])= "Brand 1", "Yellow", Max('Item'[Brand])= "Brand 2", "Green", Max('Item'[Brand])= "Brand 3", "Blue", Max('Item'[Brand])= "Brand 4", "Red", Max('Item'[Brand])= "Brand 5", "Orange", Max('Item'[Brand])= "Brand 6", "Purple", Max('Item'[Brand])= "Brand 7", "Pink", Max('Item'[Brand])= "Brand 8", "Cyan", Max('Item'[Brand])= "Brand 9", "Lime", Max('Item'[Brand])= "Brand 10", "Brown", Max('Item'[Brand])= "Brand 11", "Gray", Max('Item'[Brand])= "Brand 12", "Teal", Max('Item'[Brand])= "Brand 13", "Magenta", "Other" ) Category Measure Category Color = SWITCH ( TRUE (), Max('Item'[Category] )= "Category 1", "Red", Max('Item'[Category] )= "Category 2", "Green", Max('Item'[Category] )= "Category 3", "Blue", Max('Item'[Category] )= "Category 4", "Yellow", Max('Item'[Category] )= "Category 5", "Orange", "Black" ) Color Year Max Year = max('Date'[Year]) You can also check the video on the same - https://www.youtube.com/watch?v=SDX1gUpcJaw&list=PLPaNVDMhUXGYo50Ajmr4SgSV9HIQLxc8L&index=1Fabric Data Agent Analyzer
Fabric Data Agents are only as effective as the data foundation behind them. Following best practices is critical to achieving accurate, reliable, and consistent responses. This notebook automates the review process, helping you quickly identify optimization opportunities and improve AI readiness.
Paragliding Insights: two Bavarian flying sites as 3D twins
Oberstdorf and the Nebelhorn in the Allgäu Alps, and the Tegelberg above Schwangau in the Ammergau Alps, both at true scale from official 1 m terrain, with real flight tracks and live glider traffic drawn over them. What it does Photoreal 3D terrain built from official 1 m elevation data Two sites, switchable from a dropdown: Oberstdorf / Nebelhorn, and Tegelberg / Schwangau with Neuschwanstein below it. The area of interest is configuration, not a set of constants, and a second site is what makes that claim checkable Real IGC flight tracks, anonymised at import, with a height trace you can scrub Live traffic from the Open Glider Network Notebooks and pipelines behind it, the Fabric App in front What it deploys into your workspace Entra sign-in (Fabric identity) Static web app Data Official Bavarian elevation data and the Open Glider Network. Demonstration and training only, not flight preparation. Credits Terrain and buildings: Bayerische Vermessungsverwaltung (LDBV), CC BY 4.0. Required attribution, verbatim: Datenquelle: Bayerische Vermessungsverwaltung – www.geodaten.bayern.de [Daten bearbeitet] Coarse terrain shell: © DLR e.V. 2010–2014 and © Airbus Defence and Space GmbH 2014, 2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. © OpenStreetMap contributors (ODbL) Live glider traffic: Open Glider Network (ODbL). Data older than 24 hours is not redistributed, and OGN privacy choices are honoured. Full detail: NOTICE.md. Try it git clone https://github.com/KornAlexander/Fabric-Apps.git cd Fabric-Apps/games-and-learn/paragliding-insights npm install npx rayfin up --workspace-id <your-workspace-guid> --tenant <your-tenant-guid> Source, and always the latest version: https://github.com/KornAlexander/Fabric-Apps/tree/main/games-and-learn/paragliding-insights How it is built: https://kornalexander.github.io/Fabric-Apps/apps/paragliding-insights/ It is part of an open-source gallery of Fabric Apps: 3D twins, live maps, admin tools and a few games. https://kornalexander.github.io/Fabric-Apps/
