Data Stories Gallery topics

The Book of a Paper Trader: 355 trades, one honest question. Is there an edge?

PatricRc — Thu, 05 Nov 2026 13:57:52 GMT

ALPACA · PAPER ACCOUNT · 27 AUG – 30 OCT 2026

Most trading dashboards show you a P&L number and stop. This report keeps asking why. Over ten weeks an automated bot won +$23,859, and still closed the book at ▼ −$4,711. The story is about where that money went, which part of the system actually works, and what is sitting on the desk right now.

Profit factor 0.84 · win rate 50.1% · expectancy ▼ −$13.27 per trade · account equity $95,819

The story in five pages

Cover · What am I looking at? Three headline numbers, one candle per trading day, and a “how to read” strip for R-multiple, profit factor and why PAPER matters.
The Book · Is the account growing? No. Stop exits alone erase 54% of all gains. September was the bleed (▼ −$6,828), October stopped it (▼ −$13). Only crypto pays (▲ +$631, PF 1.14).
The Edge · Where do wins and losses come from? Shorts carry the book: ▲ +$4,727 at 62% win vs longs ▼ −$9,438 at 44%. 40 trades slipped past −1R (▼ −$8.2K), while trailing stops made ▲ +$2,845 at 81% win.
The Desk · What is open right now? 74% of equity sits in cash ($71.3K). One name (KO) is 39% of exposure. 631 orders: 43.9% filled, 52% cancelled by design.
The Ledger · Let me explore it myself. Every strategy by asset class with trades, a net P&L heat bar, win rate, avg R, profit factor and an EDGE / WATCH / CUT verdict.

Storytelling choices

Titles state the finding, not the chart type. “From $23.9K won to $4.7K lost”, “Shorts carry the book”, “One name carries the gain”. Every title is a DAX measure, so it rewrites itself when the data changes.
One question per page, in the order a trader would ask it: am I growing → why → what is at risk now → let me dig.
Honest framing. A PAPER badge on every page, the 15 crypto pairs with no price data are called out instead of hidden, and the cover explains every metric before you see it.
Markers inside the chart instead of legends: PEAK / LOW chips on the equity curve, a win-rate chip on every strategy, “+N smaller” for treemap tiles too small to label.

How it is built

Microsoft Fabric end to end. Notebooks pull Alpaca (paper) and Binance data into a lakehouse and move it bronze → silver → gold as Delta tables.
Two semantic models. A Direct Lake model for daily work, plus an Import copy for Publish to web (Direct Lake can't be published publicly).
146 DAX measures, zero custom visuals. Every chart (equity curve with drawdown, waterfall, lollipop, R-multiple dot plot, treemap, order-flow sankey, cover candles) is drawn in DAX as SVG or HTML. It is fully data-driven and loads as plain images.
One design system. A single token set (colour, type scale, radius, 8px spacing) generates the DAX, a tokens.css file and the Power BI theme JSON, so native and custom visuals look identical. Serif display titles, sans labels, tabular figures.
Navigation and accessibility. A dark rail with an active pill, made clickable by an invisible vertical Page navigator. Gain/loss colours pass WCAG AA, ▲▼ arrows mean colour is never the only signal, and alt-text measures are written for the main visuals.

Explore

▶ Live report: embedded above (start on the cover and use the left rail)

↗ GitHub (notebooks, all DAX measures, theme and design tokens): github.com/PatricRc/trading-analytics-fabric

↗ LinkedIn: linkedin.com/in/patriciorioscanepa · Portfolio: data-pat-ai.netlify.app

Paper trading only. Simulated fills, no real money, not financial advice. Built by Patricio Rios with Microsoft Fabric + Power BI.

Workout Wednesday 2026 W45 | Big Numbers and Key Point Indicators

Robbinv8 — Mon, 02 Nov 2026 17:05:07 GMT

This weeks Workout Wednesday challenge is all Big Numbers and Key Point Indicators!

Varying stakeholder requirements mean we need to have different visuals ready to go, this week we set out a large range of options for you to work on. How many can you build?

For more info: https://www.workout-wednesday.com/pbi-2026-w45/

Notes on refresh scheduling in shared workspaces

zzqr13w2 — Wed, 21 Oct 2026 03:06:16 GMT

Some notes on scheduling refreshes when datasets are shared across workspaces, hoping this helps someone.

Workout Wednesday 2026 Week 42 | Power BI: Overlay Information With Bookmarks

vivienlee — Wed, 14 Oct 2026 09:10:31 GMT

Hi everyone, this is my submission for this week's Workout Wednesday!

Bookmarks can be a little bit tricky sometimes especially when there's lots of elements to show and hide. This Workout Wednesday uses 4 bookmarks (and even more for the bonus challenge) but it's so satisfying when it comes together. This is especially useful if there's parts of your dashboard that you'd like to explain to the user and you don't feel like a tooltip is enough.

Grão Raro: assinaturas de café especial

nayarahellen — Tue, 13 Oct 2026 23:11:12 GMT

Relatório de acompanhamento de um clube de assinatura de cafés especiais. A Grão Raro é uma empresa fictícia e todos os dados são fictícios, criados para este portfólio.

O relatório foi pensado para quem gerencia a operação de assinaturas: receita, base de assinantes, cancelamentos e entregas, um mês de cada vez, com comparação contra o mês anterior.

Visão geral: o resumo do mês. Receita recorrente, assinantes ativos, novas assinaturas e taxa de cancelamento, com a receita por mês e plano, o saldo semanal de entradas e saídas, os cafés mais pedidos, o mapa de assinantes por estado e a preferência de torra.

Manual do relatório: o que cada página mostra e como usar cada controle.

As páginas Assinantes, Cancelamentos e Pedidos e entregas estão em construção e aparecem no menu com cadeado.

Recursos de experiência de uso:

Central de ajuda: uma janela de conversa, aberta pelo menu, que simula uma assistente de IA dentro do relatório. A pessoa digita uma pergunta em linguagem natural (“quanto faturou em março?”, “qual café mais vende no Sul?”, “quantos cancelaram por região?”) ou escolhe uma sugestão por assunto, e recebe o número, a comparação, um mini gráfico de barras e, quando cabe, a foto do café. Responde para qualquer mês, por café, torra, região, plano e canal. A conversa é mantida ao trocar de página, e há botões de nova conversa, copiar e avaliar a resposta.

Filtros: reunidos em um painel próprio, em botões com estado de hover, com contador de filtros ativos e Limpar filtros.

Manual e tour guiado: a página Manual do relatório descreve cada página e cada controle, e o tour “Como usar” apresenta os recursos passo a passo.

Acessibilidade: contraste no nível AA em todos os textos, e todos os indicadores batem com os visuais de onde saem.

Workout Wednesday 2026 Week 41: Stripe Chart Using Native Column Chart

Meagan — Tue, 13 Oct 2026 16:09:55 GMT

This week's challenge is from Santhana Lakshmi Ponnurasan.

What if a column chart could tell a story through color? A stripe chart uses colored stripes to reveal patterns and changes in data at a glance.

In this exercise, we’ll transform Power BI’s native column chart into a stripe chart where each stripe represents a data point, and its color helps us spot patterns, changes, and unusual values.

Same visual, a different way to see the data. Ready to give your column chart a makeover?

For more info, see: https://www.workout-wednesday.com/pbi-2026-w41/

ANALISI GLOBALE DI FATTURATO E VOLUMI

luca_bartolomei — Sun, 11 Oct 2026 05:43:59 GMT

Il sistema visualizza immediatamente i KPI macroscopici: Fatturato Totale, Pezzi Venduti, Margine Medio e la Crescita Anno su Anno. Permette di esplorare il dettaglio delle vendite per singolo Paese e per specifico modello di prodotto, analizzando l'andamento mensile del fatturato e della marginalità per identificare istantaneamente i picchi stagionali o i mesi critici.

Strumento di simulazione predittiva (What-If Analysis) applicato al controllo di gestione. In condizioni di mercato stabili, impostando il Parametro Costi su un valore pari a 0, il sistema calcola la baseline del Margine Assoluto e del Margine Simulato per ciascuna combinazione di Paese e Prodotto.

Valutazione dell'impatto dei fattori macroeconomici sulla redditività aziendale. L’azienda può anticipare le criticità vedendo quali prodotti o interi mercati andrebbero in perdita (valori in rosso), permettendo di rimodulare i listini di vendita prima che l'erosione del margine si rifletta sui bilanci ufficiali.

Segmentazione strategica del portafoglio clienti basata sul principio di Pareto.

Correlazione avanzata tra volumi di vendita e guadagno reale per singolo cliente. Questa vista incrocia la Classe ABC del fatturato con la reale Classe ABC del Margine. Consente di smascherare i "falsi leader": clienti che generano un fatturato altissimo (Classe A) ma che erodono marginalità a causa di sconti eccessivi o costi logistici elevati, venendo così declassati nella classificazione del margine.

Rappresentazione visiva immediata del posizionamento del portafoglio clienti tramite un grafico a dispersione. L'asse orizzontale misura il Fatturato Totale, mentre l'asse verticale indica la percentuale di Margine. Le linee di media dividono il grafico in quattro quadranti strategici: in alto a destra si posizionano i clienti "Campioni" (alto fatturato, alto margine), in basso a destra i clienti "A rischio" (volumi elevati ma bassa redditività), mentre i punti verdi a sinistra identificano la clientela frammentata a basso fatturato.

Interattività avanzata tramite funzioni di Drill-Through e Tooltip dinamici. Navigando con il cursore sopra i punti di dispersione del grafico, il sistema fa emergere finestre informative contestuali senza costringere l'utente a cambiare schermata.

Workout Wednesday 2026 Week 40: Metric Navigator

Meagan — Thu, 08 Oct 2026 16:20:09 GMT

This week's challenge comes from Andrei Khaidarov

A profit card tells you the result. It does not show how that result is built, which components changed, or where to investigate next.

This week, build an interactive Financial Metric Map in Power BI. Start with Net profit and reveal its components one branch at a time: revenue, cost of goods sold, operating expenses, interest and tax. Continue down to order volumes, basket size, unit economics, discounts and returns.

The goal is not to put more KPI cards on a page. It is to connect each number to its calculation and comparison. A reader should be able to follow a change in profit down to a smaller, understandable component, inspect the formula, and compare the same structure across countries and years.

For more info, see https://www.workout-wednesday.com/pbi-2026-w40/

Daylight Check: Window to Floor Area Ratio on a BIM Model

powerbiblogic — Thu, 08 Oct 2026 06:47:25 GMT

This report checks the window to floor area ratio of a residential building level by level, directly on its BIM model. Most building codes require the glazed area to be at least one eighth of the floor area: here every level gets its ratio, compared against that threshold, and the levels that fall short turn red on the 3D model.

The model is an IFC file converted into tabular data with Vcad, so glazed areas, floor areas and levels come straight from the model, with no manual schedules.

How to use it:
- Click a level in the bar chart or in the table to isolate it. Cards, donut, table and 3D model filter together.
- Use the Level slicer to jump to a specific floor.
- Switch between As designed and What-if. The what-if scenario reduces the glazing on levels 2, 8 and 14 to 35% and shows which levels stop complying.
- The donut shows where the glass is, grouped by band of levels.

Colors on the model: blue for glazing, yellow for levels that pass, red for levels below 1/8.

The ratio is computed on the gross floor area of each level, which makes it a conservative early check rather than a room by room verification.

Daylight Check: Window to Floor Area Ratio on a BIM Model

powerbiblogic — Thu, 08 Oct 2026 06:42:33 GMT

The model is an IFC file converted into tabular data with Vcad, so glazed areas, floor areas and levels come straight from the model, with no manual schedules.

Colors on the model: blue for glazing, yellow for levels that pass, red for levels below 1/8.

The ratio is computed on the gross floor area of each level, which makes it a conservative early check rather than a room by room verification.

Built with Power BI and the Vcad 3D visual. How it works: https://www.vcad.it/window-to-floor-area-ratio-bim-power-bi/

30 years of Paraguay's exports

fsamaniegoc — Wed, 07 Oct 2026 15:35:06 GMT

Hi, my name is Facundo, and this is my first dashboard. I built it to showcase Paraguay's exports from 1994 to 2025, using data from the Central Bank of Paraguay (BCP). The dashboard lets you explore the total value of exports, the main products, and their destinations. You can also filter by time period (within the 1994–2025 range) and by product.

One of the most interesting findings: electricity went from more than half of Paraguay's exports in the 1990s to just 10.9% in 2025, while agriculture now accounts for 64%.

PMO Portfolio: From Strategy to Success

Ali_India — Wed, 07 Oct 2026 13:00:55 GMT

What decisions should a PMO make about its live projects?

This three-page report turns 36 months of portfolio snapshots (100 projects, 1,696 monthly records) into one clear answer: fund 4, fix 22, pause 5, stop 3, and confirm 18 projects nobody is watching.

Pulse: how is the portfolio trending?
Health is holding at 84.5, but cost overrun has climbed back to +12.9% and average delay has grown by 15 days in a year. A snapshot slicer lets you rewind any month, and the headlines rewrite themselves.
Diagnose: what drives the outcomes?
Scope changes, overdue risk mitigations and capacity gaps hurt every outcome. Projects with 2+ scope changes overrun by +22%, against +6% with one. Strategic alignment, priority and budget size show no link to delivery at all.
Decide: what should leaders do?
Every live project gets a call from five transparent rules, documented in the report's About panel. Two ZoomCharts Drill Down visuals work together:

Drill Down Combo Bar PRO compares planned vs forecast return by decision, then drills into the Recover types.
Drill Down Scatter PRO maps strategic value against delivery health and filters when you click a bar.

The key insight: Recover projects were approved at 2.42× return but are now forecasting 2.05×, so the most valuable work is leaking value while it waits to be fixed. Six sized actions show what to do first, starting with moving 42,076 hours a month from paused and stopped work into recoveries.

Built with
Power BI · DAX (dynamic snapshot logic, measure-driven decision rules, SVG visuals for panels and dumbbells) · ZoomCharts Drill Down PRO visuals · bookmarks, field parameters and synced slicers

🔗 Interactive report: https://app.powerbi.com/view?r=eyJrIjoiNjU3YTk3NDYtNWRmMC00MzdkLWI1MjEtYWFjYmFlYThmYjBhIiwidCI6IjQ2NTRiNmYxLTBlNDctNDU3OS1hOGExLTAyZmU5ZDk0M2M3YiIsImMiOjl9

Dataset: FP20 Analytics Challenge 41, with ZoomCharts

Feedback is very welcome.

Emergency Department Operations: patient flow, triage and a shift staffing simulator

nayarahellen — Tue, 06 Oct 2026 23:05:26 GMT

Description

This report follows a fictitious network of six emergency departments through one month of care: August 2026, 47,357 visits. It was designed for the medical coordination team, and the question behind every page is where and when the team needs reinforcement. All data is made up, with no real patients or hospitals involved. The report is in Brazilian Portuguese.

The eight pages follow the patient's path: Overview, Patient flow, Triage, Journey to the doctor, Left without being seen, Outcome, Returns within 72 hours, and a Detail page for drill-through.

What to look for:

Overview: the five KPI cards are also selectors. Clicking one switches the charts on the page to that metric. The Manchester triage colors stay off until you turn on the Colors toggle, so the page doesn't open as a rainbow.
Patient flow: average arrivals by hour and weekday, plus a staffing simulator. Sliders set the doctors on duty in each shift (overnight, morning, afternoon and evening) and how many patients a doctor sees per hour. The matrix shows the occupancy of the busiest unit in every hour of a typical week in three bands: idle team below 60%, adequate from 60% to 85%, overloaded above 85%. It runs on what-if parameters, a matrix colored by measure and bookmarks.
Triage: nurses needed against nurses scheduled, hour by hour, and how many hours each unit runs short.
Journey: a timeline of the stages from arrival to the doctor, with door-to-doctor time by unit and by arrival hour.
Left without being seen: the stage where patients give up, and a quadrant chart of that rate against door-to-doctor time, with the alert zone shaded.
Returns within 72 hours: a Pareto chart of diagnosis groups.
Detail: right-click a unit, specialty or risk bar anywhere to drill through. The title names the slice you came from, and the table uses data bars and native status icons against the 85% target, with the rule in the tooltip.

A guided tour opens from the "Como usar" button and walks through each page with bookmarks. The report was first designed as an HTML mockup to settle layout and hierarchy, then built in Power BI on a star schema. Text contrast meets WCAG AA, and color never carries meaning on its own: every color comes with a label or an icon.

Brazilian E-Commerce

fsck — Mon, 05 Oct 2026 18:30:34 GMT

An interactive dashboard developed as part of my data analytics portfolio to explore sales performance across the Brazilian E-Commerce Public Dataset. About 100k orders placed between Sep 2016 and Aug 2018.

The data was modeled in a SQL Server data warehouse and exposed to Power BI as a star schema with 4 fact tables and 6 dimensions. Every visual has a dynamic subtitle that states its takeaway for the selected filters.

Key findings: net revenue reached R$ 13.49M from 98,199 orders, with 65.4% coming from the Southeast and 80% from only 18% of sellers. Orders delivered late score 2.3 stars on average, against 4.3 when on time, and 171 top sellers ship late more often than the 9.3% average, putting 24.7% of revenue at risk.

The report has three pages:

Sales Overview
Sellers & Logistics
Delivery & Reviews

You can navigate using the icons on the left panel.

Skills: Data modeling, DAX, KPI development, interactive visualization.

Feedback on the design, analysis and storytelling is welcome!

Golden Wok Delivery Network Intelligence

ugon — Sat, 03 Oct 2026 18:53:30 GMT

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 Challenge

F&B Sales Performance Dashboard

talentaps — Sat, 03 Oct 2026 13:20:00 GMT

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.

Telecom Customer Churn Dashboard | DataFlip

dataflip — Thu, 01 Oct 2026 12:23:50 GMT

Customer churn can quickly turn into lost revenue when teams cannot see which customers are leaving or why. This Telecom Customer Churn Dashboard brings customer, contract, service, and revenue data together to identify churn drivers, high-risk segments, and retention opportunities.

Business Impact

Identify high-churn customer segments and key churn drivers
Track revenue lost from customer churn and customer value
Compare churn across contract types, tenure, offers, and services
Identify high-risk customers using key customer dimensions
Understand how service adoption relates to customer churn
Find retention and revenue recovery opportunities
Monitor customer, churn, and financial KPIs in one executive-ready view
Support more focused customer retention strategies

Who Can Use This Template

Telecom & Communications Companies
Customer Retention Teams
Customer Success Teams
CRM & Loyalty Teams
Marketing Teams
Sales & Account Management
Revenue & Commercial Teams

Discover more insight-driven templates built for real business decisions. Visit DataFlip

E-COMMERCE SALES REPORT

yash426 — Fri, 02 Oct 2026 16:02:16 GMT

Hii i am Yash Garg. This is my first Power BI report published to the community. This report is related to an e-commerce sales report

Canada's Hiring Pressure Monitor: where hiring is getting harder, and where talent is overlooked

polsmirnov — Thu, 01 Oct 2026 00:10:37 GMT

Is hiring getting harder in Canada, and for whom? This report answers that in five steps, using public Statistics Canada data for July 2026.

Market direction: unemployed people per job vacancy fell from 3.1 to 2.9 over the year.
Provincial pressure: Alberta and Manitoba had the highest job vacancy rate, at 3.3%.
Industry and pay: health care and social assistance added the most payroll jobs (+82,455).
Occupation demand: which jobs employers are trying to fill (quarterly).
Untapped talent: which groups have lower employment or participation (annual).

Every page starts with a question and a finding that updates with your filters. Hovering over any measure explains it, and each page has a colour key. Methodology and sources are on the last page.

The data is prepared and checked in Python against the original Statistics Canada files before it reaches Power BI.

Built by Pol Smirnov, talent intelligence analyst, Toronto. The live site also has an AI guide: https://polsmirnov.com

U.S. Airsoft Market Analysis

CyberKalachik — Sat, 24 Oct 2026 02:36:27 GMT

📊 From almost no structured data to a complete Power BI market analysis

I was approached by people who are considering launching an airsoft business and asked to analyze the U.S. airsoft market, with a particular focus on Washington State.

And the biggest challenge became obvious almost immediately:

🔎 There was very little structured market data available.

For the first time, I had to use Python not only for analysis, but also for data collection. I gathered information from Google, Facebook, company websites, and other online sources, using APIs where available.

🧩 Collecting the data was only the beginning.

This turned out to be one of the most challenging parts of the project. I am not a web-scraping specialist, and the raw data was far from analysis-ready. Business names were inconsistent, categories overlapped, information was missing or outdated, and the same companies could appear differently across multiple sources.

Honestly, this part exhausted me. 😅

I started building the Power BI report several times, only to discover another issue in the underlying data and go back to cleaning and validating it again.

Eventually, I was able to build a dataset that was consistent enough for analysis.

⚙️ The overall pipeline looked like this:

Data Collection → Data Cleaning → Business Verification → Data Enrichment → Pricing & Services Analysis → Customer Review Analysis → Power BI Data Model → Interactive Dashboard → Business Scenario

🇺🇸 U.S. Market

The final dataset included 851 U.S. businesses, from which I identified 194 confirmed airsoft operators.

I then focused on Washington State, where I verified 10 active game operators and analyzed their locations, pricing, services, ratings, and customer feedback.

📍 Washington Market

The analysis showed:

10 verified game operators
• $22.50 median published open-play price
• 4.8 average Google rating
• 1,096 Google reviews across the verified operators

💬 Customer Voice

I also analyzed customer review text to understand not just how businesses were rated, but what customers were actually talking about.

The analysis highlighted recurring issues around operations & communication, price & value, rental equipment, and consistency in safety & rule enforcement.

📈 From analysis to a business scenario

Finally, I brought everything together in Power BI and built an interactive business scenario where assumptions such as daily attendance, operating days, admission price, rental usage, and private bookings can be changed to explore their impact on estimated monthly revenue.

💡 What this project became

For me, this project became much more than a Power BI dashboard.

It was an end-to-end data project:

finding the data → building the dataset → validating it → analyzing it → turning it into something that could support a real business discussion.

And, perhaps most importantly, it taught me how much work can happen before the first chart ever appears on a dashboard. 😅

#PowerBI #DataAnalytics #Python #DataVisualization #BusinessIntelligence #MarketResearch #DataAnalysis #PortfolioProject