Canada's Hiring Pressure Monitor: where hiring is getting harder, and where talent is overlooked
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.comLogistics Support Ticket Overview
Logistics Support Ticket Overview This dashboard was built for the customer-support team of a worldwide third-party logistics provider, using ticket data from 2021 to 2024. 1. Project purpose Logistics providers live and die by how fast—and how well—they solve customer issues. The goal of this report is to give ops leaders and CS managers a one-page pulse on the four service channels (Calls, Chats, Emails, Escalations): Work-load – ticket volumes & YoY change Speed – average resolution time and SLA compliance Quality – customer-rated satisfaction Root cause – top categories driving contacts 2. Data & modelling Item Detail Source 73 402 anonymised tickets (CSV, 2021-01-01 → 2024-12-31) Fields Dates opened/resolved, Channel, Category, Priority, Region, Shipment type, SLA days, Resolution days, CSAT Model FactTickets (one row per ticket) + DimDate + slim lookup tables (Channel, Category, Region…) → star schema Measures Explicit DAX with VAR pattern (e.g. Tickets YoY %, Avg Resolution, SLA Met). DIVIDE used for safe ratios. 3. Page design choices (see screenshot) Design element Rationale Column-per-channel layout (4 cards) Instant side-by-side comparison; uniform reading path KPI card + YoY badge At-a-glance headline plus directional cue (green ▲ / red ▼) Quarterly mini-bars Quick seasonality check without leaving the page Resolution-time histogram Reveals skew & long-tail outliers better than a single average SLA annotation Inline reminder of contractual target per channel Top-5 category bars Zero-ink alternative to tables; drives conversation on root causes Custom theme & icons Consistent brand colours (teal, amber, olive, cyan) and intuitive glyphs Year slicer (top-right) Lets users time-travel while keeping the canvas uncluttered 4. Key insights (demo data) Calls remain the busiest (5 417 in 2023) but volume slipped -2 % YoY, hinting at channel-shift to chat. Chats grew +1 % and show the fastest average resolution (3 days) thanks to simpler issues like Live Tracking. Emails hover around 5 400 tickets; resolution time skews wider (long tail up to 18 days). Escalations are <15 % of load, yet SLA compliance lags (only 79 %)—risk area for the COO. 6. How to use the report Pick a year in the slicer (defaults to current). Scan the YoY badges to spot channels needing attention. Hover over a bar chart for exact ticket count & % within SLA. Click any category bar to cross-filter the entire page (e.g., isolate Damaged Goods issues). Export to PowerPoint for exec meetings or subscribe to a Power BI alert on SLA < 85 %. Why this matters: In logistics, every delayed resolution compounds downstream costs. This dashboard distils four years of ticket data into a 30-second situational briefing—so leaders can move from gut feel to data-driven decisions. A big thank you to Injae Park for his guidance on this project. eyJrIjoiZDExZTM1N2YtNGZhYS00ZTM4LWE1ZWItZmY4ODkwZDZiNGUzIiwidCI6IjQ5MzkwMzQ4LTk2ZGMtNDZhZC05YTYyLWMxMDQzMDIwZmQ2MyJ9U.S. Airsoft Market Analysis
📊 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 #PortfolioProjectDocynx Bank | Executive Banking Analytics Dashboard – Power BI
Designed a modern executive banking analytics dashboard to provide a consolidated view of banking performance, customer activity, lending, revenue, and risk. The dashboard includes: 💰 Revenue & Deposit Performance 🏦 New Accounts & Capacity Utilization 📈 Deposits & Loan Book Trends 👥 Customer Satisfaction ⚠️ NPL & Charge-Off Rate Monitoring 💳 Deposit Mix by Account Type ⏱️ Processing & Decision Time Analysis 📅 Dynamic Branch, Date Range & Product Line filters The focus was on creating a clean executive UI with strong KPI storytelling, making it easy for management to identify performance trends, operational bottlenecks, and potential risk areas at a glance. Dashboard: Explore the live demo and full design insights on Docynx PowerBI Dashboards: Analytic Pulse Blog: Check out the full blog and explore it live on Docynx Docynx Online Productivity Tools : Boost your workflow with our suite of online tools designed for data visualization, PDF conversions, and more.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.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.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, eyJrIjoiM2VkNWQxODYtMmRjOS00ZjZmLTliZTUtMWFhZDNiMzkyMDZhIiwidCI6ImQ4ZTFiMDVlLTcwYWEtNGVmNy1iODc4LTQ2NmI2ODhmOTUyZiJ9
Olist E-Commerce Operations Dashboard: From Raw Reviews to Kano Model!
Hello everyone. I spent the last two weeks building a Power BI dashboard on a public e-commerce dataset (~100K orders). This started as a personal practice project but ended up covering more ground than I expected, so I'm sharing it here in case it's useful to others in the community. What the dashboard covers: Executive Overview 99K orders, 16M in revenue, 97% delivery rate. There's an interactive map that responds to filters so drilling into any state updates the whole dashboard. Delivery Performance A process flow breaking the order journey into stages, paired with a Delivery Delay Distribution chart. Review scores drop sharply once an order goes late and keep falling the longer the delay runs. On-time orders average 4.3 stars. Orders delayed +15 days average 1.7. Customer Satisfaction This is the page I put the most thought into. The original dataset is in Portuguese and includes customer scores but no complaint categorization. I used an LLM via API to classify +14K reviews into 9 categories in a single run, consistently and without manual labeling. It's like an Affinity Diagram, but instead of a team sitting in a room with sticky notes for hours, manual labeling, guesswork, an LLM did the classification consistently, at scale. From there, I built a Pareto analysis of Low Rating Orders (LRO) by complaint category, then combined it with ANOVA results to produce a Kano Model that maps each category by its impact on both satisfaction scores and fulfillment rate. Key findings: While most of the analysis provided by other analysts focused on Delivery Timeline, we can see from customers feedback that "Item Not Delivered" is a Must-Be requirement that is not being met consistently, followed by Missing Items. Early delivery was the only Delighter in the entire dataset. Six Sigma Quality Control Sigma Level 3.0, Cpk 0.4. The page includes a control chart, Cpk distribution, and DPMO trend over time, benchmarked against the e-commerce industry standard of 4 sigma as the next target. Tools used: Python + OpenAI API (review categorization), Microsoft Excel, Power BI. Dataset: Olist Brazilian E-Commerce, publicly available on Kaggle.Gestão 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.
