From Movie Releases to Revenue: Finding the Perfect Release Window
🎬From Movie Releases to Revenue: Finding the Perfect Release Window 🍿📊 This dashboard explores the movie industry through an important business question: 📅Which months create the biggest opportunity for a movie to succeed? Using movie release, revenue, budget, popularity, ratings, production companies, and profitability data, I built an interactive Power BI experience to uncover: 🎥Movie release patterns by month and year 💰Revenue and profitability trends 🔥Blockbuster movies and performance drivers 🎞️ Interactive movie visuals and image-based storytelling The goal was not just to visualize movie data, but to turn it into a decision-making story — helping identify when and why a movie release can have a better chance of success. A big learning experience in DAX, data modeling, interactive storytelling, and Power BI visualization!
Lego Data Museum - World Champs BCN 26 - Round 2 - Richard Ampie
This one was fun. For the Power BI DataViz World Championship I turned the Rebrickable LEGO database into a small data museum — four halls, one guide who walks you through all of them. The Grand Hall: your ticket, the key takeaway, and the doors to everything else. The History: 77 years of LEGO on a single gold curve, with an evidence vault backing every claim. The Sanctuary: a floating gallery of the landmark builds, like the drivable million-piece Bugatti. Beyond the Bricks: parks, films, the fans' designs… and an arcade room with playable LEGO-inspired games, right inside the report. I learned a lot building it: about the data, and about how far a canvas can stretch when you stop treating it like a dashboard. eyJrIjoiYmMxY2Y0NDAtZDhkNy00Y2Y1LWIyOGItOTU1MjY3NGVjMWJlIiwidCI6IjE5Y2VmZmU2LWEzNWUtNGY2Mi1hZDU5LTZkMGQwYTFiNGE3ZCIsImMiOjZ9
Lights, Camera, Insight! — Success Has No Single Winner
What makes a movie successful? This report explores movie success through three different lenses: Revenue, Return, and Audience. Rather than forcing a single definition of success, the analysis applies different eligibility rules to each path and makes those differences visible. Revenue focuses on commercial scale, Return on financial efficiency, and Audience on viewer approval. The story shows that these lenses do not crown the same winner. Bigger budgets generally align with higher revenue, but not necessarily better returns, while audience ratings are only weakly related to box-office performance. The final comparison highlights the trade-offs: each champion performs strongly on one dimension and less strongly on another. A report-specific Balanced Score is used only to identify unusually balanced films and is clearly distinguished from any official industry metric. Key takeaway: Movie success depends on the lens. Revenue, efficiency, and audience approval reward different kinds of films, so no single metric tells the whole story. Built in Power BI Desktop using only the provided Round 3 dataset, with accessibility, transparent assumptions, and responsible data use incorporated throughout the report. Connect with me on LinkedIn: What makes a movie successful? This report explores movie success through three different lenses: Revenue, Return, and Audience. Rather than forcing a single definition of success, the analysis applies different eligibility rules to each path and makes those differences visible. Revenue focuses on commercial scale, Return on financial efficiency, and Audience on viewer approval. The story shows that these lenses do not crown the same winner. Bigger budgets generally align with higher revenue, but not necessarily better returns, while audience ratings are only weakly related to box-office performance. The final comparison highlights the trade-offs: each champion performs strongly on one dimension and less strongly on another. A report-specific Balanced Score is used only to identify unusually balanced films and is clearly distinguished from any official industry metric. Key takeaway: Movie success depends on the lens. Revenue, efficiency, and audience approval reward different kinds of films, so no single metric tells the whole story. Built in Power BI Desktop using only the provided Round 3 dataset, with accessibility, transparent assumptions, and responsible data use incorporated throughout the report. Connect with me on LinkedIn: linkedin.com/in/martín-rojas-páez
TMDB Movie Analytics Explorer
Statement Explore movie performance through budget, revenue, profit, genres, and interactive drillthrough pages. Search for any movie title, compare trends, and discover insights from the TMDB dataset with an intuitive Power BI experience. Appeal I created this report to explore movie success from multiple perspectives rather than relying on a single metric. Interactive filtering and drill-down features allow viewers to discover their own insights and compare movies, genres, and trends over time. Thank you for reviewing my submission! "For instance, I filtered the data by a released action movie franchise. It appears that multiple favorable conditions aligned perfectly to enable the continuous production of this film series." [Key Insights from the Dashboard Data] Overwhelming Financial Success When isolating the 'Mission: Impossible' franchise, the data reveals a total budget of $1.41 billion against a staggering total revenue of $4.71 billion, yielding a massive net profit of $3.0 billion. This guaranteed return on investment is clearly the primary driver behind the studio's greenlight for successive sequels. Sustained Popularity and Audience Engagement The total popularity score reached 715.57, demonstrating that the franchise has successfully maintained high audience engagement and brand equity over a long span—from the original 1996 release up to the most recent installments. Data Constraints & Nuances (Responsible Data Use) Interestingly, the average vote rating sits at a moderate 6.15. This highlights a critical nuance in the movie industry: massive commercial success and high popularity do not necessarily correlate with critical acclaim or top-tier audience ratings.Lights, Camera, Insight — What Makes a Movie Successful?
What makes a movie successful? “Lights, Camera, Insight” explores this question through three independent analytical lenses: • Commercial Scale — How much worldwide revenue did the movie generate? • Financial Efficiency — How effectively did reported revenue convert the reported budget? • Audience Approval — How strongly did audiences rate the movie when sufficient voting evidence was available? The report was designed as an interactive cinematic experience rather than a traditional collection of charts. Users can change the success lens, year, genre, vote threshold, and ranking scope to see how the definition of a “hit” changes with context. The story is organized into five chapters: 1. The Success Question An overview of the selected analytical lens, its leading movies, historical evolution, and genre ranking. 2. The Economics of a Hit An examination of budgets, revenue, break-even performance, and the difference between aggregate results and the typical movie. 3. Can We Trust the Rating? An analysis of audience scores, vote volume, Bayesian-adjusted ratings, and the importance of sufficient evidence. 4. Many Ways to Win A comparison of commercial reach, financial efficiency, and audience approval, showing that the same movie can perform very differently depending on the chosen lens. 5. Method & Limitations A transparent audit of data coverage, eligibility rules, missing values, duplicate identifiers, future releases, and the claims the dataset can—and cannot—support. The solution combines native Power BI visuals with dynamic DAX-driven HTML cards, interactive movie posters, contextual tooltips, responsive KPI panels, and lens-aware narrative text. The central takeaway is simple: There is no universal definition of a successful movie. Success must be evaluated according to the decision being made, the evidence available, and the appropriate comparison group. Data source: Full TMDB Movies Dataset 2024. Built with Power BI, Power Query, DAX, HTML, CSS, and SVG. slindsay eyJrIjoiNzZhNDNhYTEtZGQyNS00NjZjLWEzYjEtMDY1YjE4MjAzZjk3IiwidCI6IjMxYzJjZDk3LWE3MGItNDgwYy1iZTM1LTBhMGRiMzgwZTNlNCJ9Fabric Shortcut Inventory
A Microsoft Fabric notebook that scans workspaces and builds a complete inventory of OneLake shortcuts, detecting orphaned, circular and ungoverned external shortcuts, with an interactive HTML report and optional persistence to a Delta table. Why? OneLake shortcuts are easy to create and hard to govern: over time you accumulate shortcuts pointing to deleted items, circular chains between lakehouses, and connections to external storage (S3, ADLS, GCS…) with no sensitivity label or endorsement whatsoever. Fabric offers no centralized view of any of this. This notebook builds one in a single run. What it does Discovers workspaces — the entire tenant (with admin permissions) or a specific list, by name or ID. Discovers items — lists the items in each workspace via the Fabric REST API. Extracts shortcuts — in parallel, from the item types that support them (Lakehouse, KQL Database, Mirrored Database). Parses targets — normalizes OneLake targets (workspace/item/path) and external ones (Amazon S3, ADLS Gen2, Google Cloud Storage, S3 compatible, Dataverse, Azure Blob Storage, OneDrive/SharePoint). Validates: Orphaned: the target item no longer exists within the scanned scope. Circular: shortcut chains that form a cycle (DFS detection over the dependency graph). Governance: external shortcuts on items with no sensitivity label and no endorsement (Certified/Promoted). Resolves names — turns target GUIDs into human-readable names, even for targets outside the scanned scope. Presents — an interactive HTML report with 4 views (Overview, Circular, Orphan, Governance) and per-row color coding. Persists (optional) — writes the inventory to a Delta table so you can query it with SQL or build Power BI reports on top All the code and documentation can be found in the Github Repository. https%3A%2F%2Fgithub.com%2Fkilianbs%2Ffabric-shortcut-inventory%2Fblob%2Fmain%2Fshortcut_inventory_en.ipynb
Workout Wednesday 2026 Week 25 - Sales Difference
Here is my submission for the Data Days QuickViz - Workout Wednesday Remix (2026 Week 25). Challenge: Diverging Bar Chart showing sales differences compared to the selected sub-category. Features Built: Custom DAX measure for sales variance, dynamic labels with (+) and ( - )formatting, and custom color rules. QuickViz Challenges | Fabric Data Days Workout Wednesday 2021 | Week 6 - Long Labels 2026 Week 25 | Power BI: Find the difference from a selected value / Power BI, Workout Wednesday / By Meagan Longoria Introduction, We have a new contributor to WoW for Power BI, Robbin Vernooij.Sales vs Forecast | Dashboard
This dashboard was developed as part of an interview assessment to compare actual sales against forecast across Americas, EMEA and Asia. The solution used SQL Server for data ingestion, staging, cleaning, standardization and reporting views, followed by Power BI and DAX measures for KPI calculation and interactive analysis. A key insight was that actual sales exceeded forecast across the analyzed years, while approximately 7% of sales records had no matching forecast and require further business review. eyJrIjoiYmUzMTNkNTQtMmQzMC00M2U0LWE2MmItMzgzMDBiOTIzYWZlIiwidCI6ImI0MzBjNjVhLTlmY2ItNGQ4Yi1hNDU4LTBhNzJhYTI0YzQzMyJ9
PitchSide Pro — What's Really Driving Revenue
PitchSide Pro — what's really driving revenue, and where to focus next. I started with a hypothesis: that growth was "rented" from football tournaments. The data overturned it. Tournaments drive only ~5% of revenue. The real engine is everyday trading plus a commercial calendar the business controls kit launches, holiday gifting. Margins are healthy and uniform (52.8%), and customers are already loyal. The actual constraint? Conversion has been flat at 3.24% for five years. So the recommendation isn't "chase churn we don't have" it's fix the funnel: A +0.5pt conversion lift is worth +$1.73M from traffic we already pay for. Built for an executive audience answer first, evidence one layer down, every page ends in a decision. Full write-up : github.com/bedooralmareni/pitchside-pro-report #WorldChampsBCN #PowerBI #DataViz eyJrIjoiYjBmNTdlYjEtMTUzYi00NTU5LTk2YTYtYjc1OTE1NDVmZjdkIiwidCI6Ijk3ZGE5ZDRmLWRlM2EtNDMxMC04MWM5LTcwZDU4ZjM3YWJkNSIsImMiOjl9The World Cup 🏆 - Just for fun
As part of my preparation for the Power BI World Championships, I built a report using FIFA World Cup data, thinking we might be given a similar challenge during the competition. That turned out not to be the case - at least not yet. But now that I’ve qualified for the finals, I thought I’d share the report rather than let it gather dust in one of my folders. A quick disclaimer: this is still a beta version, so there may be a few bugs. I also haven’t had time to add alt text or set up the tab order properly yet. With the FIFA World Cup currently underway, it seemed like a fitting time to share it. Good luck to everyone still competing in the Power BI World Championships. I’m excited to see who will join me on stage in Barcelona! Data from: https://www.kaggle.com/datasets/piterfm/fifa-football-world-cup/data I’ve also recently joined LinkedIn, so feel free to connect with me here! www.linkedin.com/in/mariusjhelle eyJrIjoiODUwYmQwZDctNjQ1YS00M2I1LWFlOTktNGNkMTNiM2FiZTc0IiwidCI6ImI2ZGEwOTc1LTEwY2EtNDE0ZS04YTZjLTE1ZjgzMzgxZGUzMiJ9
