Golden Wok Delivery Network Intelligence
From network pressure to service reliability and corridor-level action Golden Wok’s delivery network presents a connected operational challenge: understanding where delivery pressure originates, when service reliability begins to deteriorate, and which corridors require attention. The report follows a three-stage analytical journey: Network Performance → Service Reliability → Network Optimization 01 | Network Performance The first page explores where delivery pressure originates across the network. Delivery radius, traffic friction, weather conditions and zone tiers provide the context for understanding where operational pressure is concentrated and how those conditions affect delivery performance. Outcome: Identify where network conditions create the greatest delivery pressure. 02 | Service Reliability The second page focuses on what happens as delivery performance deteriorates. One of the clearest findings was the relationship between delivery duration and SLA risk. Breach rates increased sharply across longer delivery-time bands, while customer ratings remained comparatively stable. This made service reliability a stronger operational signal than the customer-rating response in the supplied data. Outcome: Understand when delivery performance begins to translate into significant SLA risk. 03 | Network Optimization The final page moves from diagnosis to action. I combined Profit Efficiency and SLA Risk to classify delivery corridors into Priority Intervention, Service Intervention, Economic Optimization, and Protect & Maintain. ABC classification then adds economic importance, helping distinguish valuable corridors that should be protected or repaired from weaker, higher-risk corridors that may warrant deeper review. Interactive tooltips provide additional corridor and intervention context without overcrowding the main report. Outcome: Prioritize corridor-level action based on economic value and service reliability. Validation Before Visualization The brief served as a checklist for testing key assumptions against the supplied data. Where discrepancies appeared, values were adjusted only when defensible or excluded when they could not be reliably interpreted. This kept the analysis grounded in the brief while allowing validated evidence to determine the final story. Final Takeaway Golden Wok became a simple decision journey: Diagnose → Understand → Prioritize The project reinforced the importance of validating assumptions before visualization and keeping each report page focused on a clear business question. A rich operational dataset does not need a crowded report. Every visual should help move the analysis closer to a decision. Data Source: September 2026 DataDNA – Golden Wok Food Delivery Analytics ChallengeF&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.Kanban Board
A sales pipeline where moving a card changes more than its column. Drag individual deals or a group of cards between stages and watch the weighted forecast update. Capacity limits, keyboard moves, and undo make the board as practical as it is tactile. The board is live connected to a database so moving cards between columns updates and saves automatically.Makeover Monday week 36 - Americans favourite season
At the start of the week I saw that at dm-p had developed and released a chart xkcd custom visual for Power BI so I thought I'd use it for this weeks Makeover Monday data on Americans favourite season. It's a very small dataset and not all of it ideal for the visuals but was a good opportunity to try it out, I even bought myself to create some pie and Doughnut charts. eyJrIjoiZjc4MTY4YjItMzNmMi00YzBiLTgyMGYtNTM3MDgxMTBmMGY1IiwidCI6IjgzNzBjZjE0LTE2ZjMtNGMxNi1iODNjLTcyNDA3MTY1NDM1NiIsImMiOjh9E-commerce Profitability Audit: Protect, Monitor or Act
Built with the dataset from the ZoomCharts 4U Report Challenge (European e-commerce, 2024–2025). The question: Sales grew 69% and contribution margin more than doubled, but which categories actually stayed profitable? Page 1 – Audit: each category gets a verdict (Protect, Monitor, Act) based on three rules: CM %, product cost % of sales, and other operating cost %. The rules are shown on the page, so the verdict is transparent. Hover over any category to see a tooltip comparing its cost lines with the healthy categories. Page 2 – Detail: a P&L variance table (current vs prior year) and a margin bridge from prior-year CM to current-year CM. Every subtotal reconciles. Key findings: Beauty, Fashion and Sports & Outdoors deliver about 79% of margin. Electronics runs at 3.4% CM, the only category in Act. Product cost rose about $97K, but most costs grew slower than sales, so CM % improved by 3.3 pp. Feedback welcome.E-commerce Profitability Audit: Protect, Monitor or Act
Built with the dataset from the ZoomCharts 4U Report Challenge (European e-commerce, 2024–2025). The question: Sales grew 69% and contribution margin more than doubled, but which categories actually stayed profitable? Page 1 – Audit: each category gets a verdict (Protect, Monitor, Act) based on three rules: CM %, product cost % of sales, and other operating cost %. The rules are shown on the page, so the verdict is transparent. Hover over any category to see a tooltip comparing its cost lines with the healthy categories. Page 2 – Detail: a P&L variance table (current vs prior year) and a margin bridge from prior-year CM to current-year CM. Every subtotal reconciles. Key findings: Beauty, Fashion and Sports & Outdoors deliver about 79% of margin. Electronics runs at 3.4% CM, the only category in Act. Product cost rose about $97K, but most costs grew slower than sales, so CM % improved by 3.3 pp. Feedback welcome.Factory control center: image triage to annotate near incidents
This demonstration application shows how Fabric apps can be used in conjunction with Azure IoT Operations. On the edge of a local network, of a factory, building, or vessel, Azure IoT Operations can work with local devices. It can send data to Microsoft Fabric Real-Time Intelligence conditionally, using a secure connection via Azure Arc. What if we use a vibe-coded Fabric App for triage on images, taken at the factory floor while a vibration sensor detects abnormal behavior? On the Edge, an IP camera video stream and a LoRaWAN vibration sensor telemetry stream are combined using custom logic running in a Kubernetes pod. That data is then sent to an Eventstream custom endpoint and forwarded to an Eventhouse KQL Database table. Using table update policies, the table rows are cleaned and exposed in a Fabric Semantic model. Using vibe-coding in Visual Studio Code together with GitHub Copilot, the Fabric app is created. It shows how the latest images with the abnormal vibration deviation are presented. The user can then do triage and annotate the reason. This can then be shared with colleagues, customer or used for training agents. The application is built in ~1 day and demonstrates various features. Check this blog post. You can read about the full story on how the demonstration solution is built. Source of the custom code is available on GitHub.Executive Performance Dashboard
Executive Performance Dashboard About This Dashboard Executive Performance Dashboard - Built for Data-Driven Decision Making The dashboard covers: Overview – Key performance indicators and metrics Analytics – Deep insights and trend analysis Reporting – Automated and customizable reports Monitoring – Real-time data tracking and alerts What sets this apart? It's built on the principle that effective analytics should be both powerful and accessible. I focused on creating a user-centric design by: Highlighting the most actionable insights with clear visualizations Using intuitive navigation and clean interface design Providing interactive features for deeper exploration Ensuring fast performance and reliable data updates Making complex data accessible to all stakeholders Thank you, eyJrIjoiM2VkNWQxODYtMmRjOS00ZjZmLTliZTUtMWFhZDNiMzkyMDZhIiwidCI6ImQ4ZTFiMDVlLTcwYWEtNGVmNy1iODc4LTQ2NmI2ODhmOTUyZiJ9African 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/
Mercado Español de Turismos: Evolución y Electrificación
🇬🇧 Spanish Passenger Car Market: Evolution & Electrification This Power BI project analyzes the evolution of the Spanish passenger car market and the expansion of vehicle electrification using more than 6 million official vehicle registration records from Spain's Directorate-General for Traffic (DGT). The projet goes beyond tracking registration volumes. Its purpose is to explore the structure and evolution of the market through several analytical perspectives: How is the passenger car market evolving? Which brands lead the market? Which are gaining or losing ground? How quickly is electrification expanding across Spain? Which technologies are driving this growth? And which brands and models are contributing most to the transition? The report analyzes the period from 2024 to August 2026, while 2023 data is also incorporated into the data model to provide the historical context required for year-over-year comparisons. 📊 Analysis Market Overview Provides a high-level view of passenger car registrations through KPIs, annual and monthly trends, geographical distribution and evolution by propulsion type. Market Analyzes the competitive landscape through brand rankings, market share, year-over-year variation and monthly trends. A scatter plot combines market share and YoY growth, making it possible to contextualize the position and evolution of the main brands. Electrification Examines the evolution of electrified passenger cars, distinguishing between PHEV, REEV, BEV and FCEV technologies. The analysis explores registration volumes, technology mix, monthly evolution and the geographical penetration of electrification across Spanish provinces. Electrified Brands & Models Identifies the brands and models contributing most to electrified registrations. The analysis combines registration volume, share and year-over-year growth with the technology mix of leading brands and a ranking of the most registered electrified models. Conclusions Brings together the main findings from the report, highlighting the growth of electrification, changes in the technology mix, leading brands and models, and territorial differences in electrification penetration. ⚙️ Data Preparation & Modelling One of the main challenges of the project was transforming the original DGT registration files into a consistent analytical dataset suitable for Power BI. Power Query was used to build the data preparation process, including: Integration and transformation of several years of registration data. • Classification of passenger cars and propulsion technologies. • Standardization of brand names. • Normalization of vehicle model names to reduce inconsistencies in the original registration data. • Creation of analytical categories for electrified vehicles. • Preparation of geographical dimensions for province-level analysis. • Creation of the temporal structure required for year-over-year and YTD analysis. The semantic model incorporates DAX measures for registration volumes, YTD calculations, previous-year values, year-over-year variation, market share and electrification share. Special attention was given to equivalent-period comparisons. Since the latest available 2026 data covers January through August, 2026 YTD indicators are compared against January-August 2025, preventing partial-year results from being compared with a complete previous year. This approach allows the same report to support both full-year historical analysis and comparable YTD analysis for the current year. 🎨 Report Design & User Experience The project also places strong emphasis on data storytelling, visual consistency and usability. The interface was designed to create a cohesive analytical experience across the different sections of the report, using a consistent visual hierarchy, navigation system and color language. The report includes custom navigation, interactive year selection and contextual help overlays that explain how to use filters and interpret key visualizations while keeping the underlying report visible. The final Conclusions page transforms the main analytical findings into a concise storytelling layer, connecting market growth, electrification, technology adoption, brand performance and geographical differences. The objective was to build a report that could be explored and understood independently, even by users who were not involved in its development. Tools: Power BI · Power Query · DAX · Figma Data source: Dirección General de Tráfico (DGT) Period analyzed: 2024 - August 2026 Dataset: 6M+ vehicle registration records 🔗 MORE ABOUT THE PROJECT 🚗 Full project & case study: https://cristina-mg.github.io/projects/turismos-electricos/ 📊 Data Analytics Portfolio: https://cristina-mg.github.io/ 🇪🇸 Mercado Español de Turismos: Evolución y Electrificación Este proyecto de Power BI analiza la evolución del mercado español de turismos y el avance de la electrificación a partir de más de 6 millones de registros oficiales de matriculaciones de la Dirección General de Tráfico (DGT). El proyecto va más allá del análisis del volumen de matriculaciones. Su objetivo es estudiar la estructura y evolución del mercado desde distintas perspectivas: ¿Cómo evoluciona el mercado de turismos? ¿Qué marcas lo lideran? ¿Cuáles están ganando o perdiendo terreno? ¿A qué ritmo avanza la electrificación en España? ¿Qué tecnologías están impulsando este crecimiento? ¿Y qué marcas y modelos están contribuyendo en mayor medida a esta transición? El informe analiza el periodo comprendido entre 2024 y agosto de 2026, incorporando también los datos de 2023 al modelo para disponer del histórico necesario para realizar comparaciones interanuales. 📊 Análisis Visión General Ofrece una perspectiva global de las matriculaciones de turismos mediante KPIs, evolución anual y mensual, distribución geográfica y comportamiento según el tipo de propulsión. Mercado Analiza la estructura competitiva mediante rankings de marcas, cuota de mercado, variación interanual y evolución mensual. Un gráfico de dispersión combina cuota de mercado y crecimiento interanual, permitiendo contextualizar la posición y evolución de las principales marcas. Electrificación Profundiza en la evolución de los turismos electrificados diferenciando las tecnologías PHEV, REEV, BEV y FCEV. El análisis estudia el volumen de matriculaciones, el reparto entre tecnologías, su evolución mensual y la penetración territorial de la electrificación por provincia. Marcas y Modelos Electrificados Identifica las marcas y modelos que más están contribuyendo a las matriculaciones electrificadas. El análisis combina volumen, cuota y crecimiento interanual con la distribución tecnológica de las principales marcas y un ranking de los modelos electrificados con mayor número de matriculaciones. Conclusiones Integra los principales hallazgos del informe, conectando la evolución de la electrificación, los cambios en el reparto tecnológico, el comportamiento de las principales marcas y modelos y las diferencias territoriales en la penetración de los turismos electrificados. ⚙️ Preparación y Modelado de los Datos Uno de los principales retos del proyecto fue transformar los ficheros originales de matriculaciones de la DGT en un conjunto de datos consistente y preparado para el análisis en Power BI. Mediante Power Query se desarrolló el proceso de preparación de los datos, incluyendo: Integración y transformación de varios años de matriculaciones. • Clasificación de turismos y tecnologías de propulsión. • Estandarización de las marcas. • Normalización de los nombres de los modelos para reducir las inconsistencias presentes en los datos originales. • Creación de categorías analíticas específicas para los vehículos electrificados. • Preparación de las dimensiones geográficas necesarias para el análisis provincial. • Creación de la estructura temporal necesaria para los análisis interanuales y YTD. El modelo semántico incorpora medidas DAX para calcular volumen de matriculaciones, YTD, valores del año anterior, variación interanual, cuota de mercado y cuota de electrificación. Se prestó especial atención a las comparaciones entre periodos equivalentes. Al disponer de información de 2026 entre enero y agosto, los indicadores YTD de 2026 se comparan con enero-agosto de 2025, evitando comparar los resultados de un año parcial con los de un año completo. Este planteamiento permite utilizar el mismo informe tanto para analizar años históricos completos como para realizar un seguimiento YTD comparable del año en curso. 🎨 Diseño y Experiencia de Usuario El proyecto también presta especial atención al data storytelling, la coherencia visual y la usabilidad. La interfaz se diseñó buscando una experiencia analítica coherente entre las distintas páginas del informe, manteniendo una jerarquía visual, navegación y lenguaje de color consistentes. El informe incorpora navegación personalizada, selección interactiva del año y un sistema de ayuda contextual que explica el funcionamiento de los filtros y la interpretación de las principales visualizaciones manteniendo visible la página sobre la que se está trabajando. La página final de Conclusiones transforma los principales resultados del análisis en una capa de storytelling que conecta la evolución del mercado, el avance de la electrificación, las tecnologías, el comportamiento de las marcas y las diferencias territoriales. El objetivo final fue construir un informe que pudiera ser explorado y comprendido de forma autónoma, incluso por usuarios que no hubieran participado previamente en su desarrollo. Herramientas: Power BI · Power Query · DAX · Figma Fuente de datos: Dirección General de Tráfico (DGT) Periodo analizado: 2024 - agosto de 2026 Dataset: +6 millones de registros de matriculaciones 🔗 MÁS INFORMACIÓN 🚗 Proyecto completo y caso de estudio: https://cristina-mg.github.io/projects/turismos-electricos/ 📊 Portfolio de Data Analytics: https://cristina-mg.github.io/ 💼 LinkedIn: https://www.linkedin.com/in/cristina-mart%C3%ADnez-garc%C3%ADa-data
