F&B Sales Performance Dashboard
F&B Sales Intelligence Dashboard An interactive dashboard developed as part of my data analytics portfolio to explore sales performance across three F&B outlets in Indonesia. It uses the Anonymous Transactional Dataset from Mendeley Data, containing historical POS transactions and product metadata for January–September 2025. The dashboard covers sales KPIs, outlet performance, weekly trends, transaction intensity by day and operating hour, and product rankings. It also incorporates RFM-based transaction segmentation using K-Means, replicating a published methodology with the full dataset. Four transaction segments are summarized by volume, average spending, and total transaction value. The analysis covers 53,820 transactions with a total transaction value of approximately IDR 1.32 billion. Key findings include SHOP001 contributing nearly 49% of total transaction value and high-value transactions contributing a substantial share despite their smaller volume. Tools: Power BI and Google Colab (Python) Skills: Data modeling, DAX, KPI development, interactive visualization, RFM analysis, K-Means clustering, and insight communication. Dataset: https://data.mendeley.com/datasets/kcgf45y24m/2 Feedback on the dashboard’s design, analysis, and usability is welcome.Crime Overview — City of Los Angeles (2020 - 2024)
🚔Los Angeles Crime Analysis Dashboard (2020–2024) This project presents an end-to-end analytical dashboard exploring crime patterns in the City of Los Angeles using official LAPD open data. The report focuses on temporal trends, geographic distribution, crime characteristics, and victim demographics to uncover meaningful insights about when, where, and how crimes occur. 📊Tool: Power BI 📅Period: 2020–2024 📍Location: Los Angeles, California 📂Data Source Los Angeles Police Department (LAPD) — Open Data Portal Crime Data from 2020 to 2024 The dataset contains detailed records of reported incidents, including date and time, location, crime type, victim information, and weapon usage. Note: In October 2024, LAPD migrated from UCR to the NIBRS reporting system. The legacy dataset used in this analysis is no longer updated and is maintained only for historical purposes. Because newer records follow a different reporting standard and remain incomplete, 2025 data were excluded. 🔎Key Analytical Areas 🧭 Crime Overview Total crime volume across years Most common crime types Distribution across city areas Long-term trends 🕒Temporal Patterns Crime distribution by hour of day Weekday vs weekend behavior Seasonal variation Identification of peak crime periods 🗺️ Geographic Analysis Crime hotspots across LAPD areas Spatial concentration of incidents Neighborhood-level differences 👥Victim & Crime Characteristics Victim age and gender distribution Ethnicity patterns Crime severity (violent vs property) Weapon involvement Location types where crimes occur Adults represent the largest victim group, and crimes most frequently occur in public outdoor environments. ⚠️Notable Insights Property crimes dominate overall incident counts. Violent crimes account for roughly one quarter of reported cases. Personal force is the most commonly used “weapon,” indicating many incidents involve direct physical contact rather than firearms. Outdoor/public spaces show the highest exposure to crime. Victim demographics broadly reflect the city’s diverse population. Identity theft and other non-contact offenses highlight the growing role of non-traditional crime types. 🛠️ Analytical Approach The dashboard was built using a structured BI workflow: Data cleaning and transformation Calendar table for time intelligence Derived metrics and DAX measures Categorization of crimes (violent vs property) Aggregation of demographic groups Interactive filtering across all visuals 🎯Purpose This project demonstrates the ability to: Transform raw public data into analytical insights Design business-ready dashboards Apply data modeling and DAX Communicate findings through clear visual storytelling eyJrIjoiZjZlMjU3MzktMjU4OC00OGQ3LWIzMGMtOTI5N2JjNjI5NDI4IiwidCI6IjY1NWVhZjVhLTBhMTctNDEzOS05NzU5LTFlMDIzMTRkMDJhYiIsImMiOjZ9Gestão de Pessoas
Um relatório de Power BI pensado para o RH e a liderança acompanharem a equipe mês a mês, do quadro ativo à folha de pagamento. O que tem em cada página Visão geral: quadro ativo, admissões, desligamentos, turnover e custo do mês, com a variação contra o mês anterior. Movimentação: admissões e desligamentos por mês, turnover por diretoria, motivos de saída e tempo de casa de quem sai. Perfil da equipe: pirâmide etária por gênero, gerações e participação de mulheres por nível do cargo. Remuneração: folha mensal, salário médio por nível e gênero, horas extras por unidade e composição do custo. Colaboradores: a lista de quem está em experiência, de férias ou afastado. Analítico: uma tabela em que o usuário escolhe as colunas e as medidas. Glossário: cada indicador com o que ele mede e a fórmula. Recursos de Power BI usados Parâmetros de campo, que deixam trocar a métrica e o eixo dos gráficos pelos botões Página de detalhamento (drillthrough), aberta a partir de qualquer gráfico Dicas de ferramenta com resumo em frase Frases de leitura embaixo dos gráficos, escritas em DAX, que mudam com os filtros Painel de filtros aberto por indicador (bookmark) Modelo em star schema, com tabela de medidas organizada em pastas Os dados são fictícios.
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/
Live Weather Dashboard
Live IoT Weather Dashboard This is a real-time weather dashboard powered by a custom-built DIY IoT weather station. While many weather dashboards available online use static datasets or merely change color schemes on the same template, this project is fundamentally different. It is a live system that bridges the gap between physical hardware and digital analytics. Technical Overview Data Collection: Unlike dashboards that rely on CSV uploads, this station utilizes an ESP8266 microcontroller paired with DHT11 and BMP280 sensors. Live readings for Temperature, Humidity, and Pressure are logged directly to Google Sheets in real-time. Region-Specific Calibration: Specifically calibrated for the unique tropical climate of the Trichy, Tamil Nadu region, ensuring localized accuracy that global APIs often miss. Advanced Analytics: Using only four raw data points (Timestamp, Temp, Humidity, and Pressure), I have developed a custom suite of DAX measures. These calculate complex weather trends, "feels like" temperatures (Heat Index), and atmospheric dew points. Dynamic Visuals: The interface isn't just a static skin; it features weather-dependent iconography and a custom refresh schema that ensures the data you see is as fresh as the air outside. Why this stands out Most dashboards you see are "re-skins" of existing templates. This project demonstrates a full-stack data engineering pipeline: Sensor → Microcontroller → Cloud Storage → Power BI Logic → End-User Visualization. eyJrIjoiYTVjNzg4MzMtZDJlMS00NmY5LWI1ZDItNzRhMjRmNDdkZWIwIiwidCI6IjBjM2QwNTc2LTFkOWYtNGM4Ny05OTNjLTg2YjQ0MGE1YjA3OCJ9Unit lifecycle on the BIM model
Every unit in a building has a commercial life of its own: vacant, reserved, for sale, sold, rented, back under maintenance. That history usually lives in a spreadsheet, far away from the drawing everyone actually looks at. This report puts it back on the model. It covers a 28-unit mixed-use building — residential, offices, retail and a restaurant across five levels — and tracks every unit from January 2023 onwards. Five pages: Overview — the numbers first: 12 units on the sale side, 16 on the rent side, broken down by vacancy and status. Underneath, every status change plotted over time, one line per unit, split between commercial and residential. You can see a unit go For Rent → Rented → Vacant → For Rent and read how long each leg took. History 2D — the floor plans, with each unit coloured by its status on the date you pick. Move the slider and the plan repaints. History 3D — the same timeline on the 3D model, filtered by contract type and level, with the occupancy mix next to it. Latest – Rent and Latest – Sale — the current picture on each side of the business: what is still available, what is gone, and where it sits in the building. The model comes into Power BI through Vcad, so geometry and unit properties arrive as tables you can relate to anything else — here, the status history. Selection works both ways: click a row and the unit lights up in the model, click the unit and the tables filter. The data is fictional. The workflow isn't. One question, since the report reads history in two different ways: do you get there faster through the status lines on the Overview, or through the slider repainting the plan? I keep changing my mind depending on who I'm showing it to.PowerBi and Bim model data
The report provides the user with all the necessary tools to have a detailed view of spaces, features and their use, thanks to PowerBI and Vcad viewer. Data can be obtained directly from IFC files or from Vcad . You can select building floors, details of specific areas or a set of areas highlighted by colors, filter the spaces combined with occupants of the structure. The viewer will respond in real time to the filters showing the floors of interest and the highlighted areas. Learn more here Follow us on Twitter and Linkedin eyJrIjoiMjhlNjM0ODItNTE5ZC00NTZhLWJhMTctMGUxYmFhOGMyYzM4IiwidCI6IjkyNWJkNTViLTA1MjEtNDAwMy1iN2M5LWMzZTY4MzY3ZDcyNCIsImMiOjh9Occupancy monitoring, Vcad custom visual and Autodesk Forge
Power BI report that, using markers, monitors the occupancy of workstations in an office building. Click here https://www.bimservices.it/how-it-works/ to learn more about Vcad. eyJrIjoiZGJmMTE2YWQtZDM5MC00OTE5LTllZGEtNWViNjNlYTEyNDJmIiwidCI6IjkyNWJkNTViLTA1MjEtNDAwMy1iN2M5LWMzZTY4MzY3ZDcyNCIsImMiOjh9&pageName=ReportSection92c08b2c41e182ae4910Quantities and materials dashboard from BIM file - Vcad for Power BI
Dashboard highlighting how to use quantity and material data in a report. We then see how Vcad makes it easy to leverage the information contained in BIM files, transforming it and associating it with the rendering of the model. eyJrIjoiODQxOTkyMjYtYjgwNC00ZjY4LTg3MGEtMWJkZDg5NTA1YTZhIiwidCI6IjkyNWJkNTViLTA1MjEtNDAwMy1iN2M5LWMzZTY4MzY3ZDcyNCIsImMiOjh9Project Management Dashboard
This project management dashboard is essential because it provides a centralized, visual overview of project progress, making it easier to track tasks, deadlines, resources, and budgets, as well as identify potential issues early. This centralized view improves communication, streamlines decision-making, and ultimately helps keep projects on track and within budget. Why project management dashboards are so important: Real-time visibility: Dashboards offer a quick and clear view of a project's status, allowing teams and stakeholders to instantly see progress and identify potential issues. Improved communication: By providing a single source of truth for the project, dashboards reduce the need for constant updates and follow-ups, fostering better communication between team members and stakeholders. Improved decision-making: With clear and concise data readily available, project managers and stakeholders can make informed decisions based on real-time information. Efficient Resource Allocation: Dashboards help track resource utilization, ensuring they are allocated effectively and that no team member is overburdened. Early Risk Identification: By monitoring key metrics and trends, dashboards can help identify risks and potential issues before they escalate, enabling timely intervention. Increased Accountability: With clear visibility into who is responsible for each task, dashboards can help promote accountability and ensure deadlines are met. Improved Project Execution: Ultimately, the benefits of a project management dashboard contribute to better results, including on-time delivery, on-budget completion, and improved team performance. Visit Website: https://insight-analytics.com/dashboards/ Thank you eyJrIjoiY2MyZTRlZjgtMWM1MC00NWY0LWFlNmQtZWZiNDQ4OGZjNGQ0IiwidCI6IjBlMGNiMDYwLTA5YWQtNDlmNS1hMDA1LTY4YjliNDlhYTFmNiIsImMiOjR9
