/* global React */
const POSTS = [
  {
    slug: "what-is-a-pareto-chart",
    title: "What Is a Pareto Chart — and How to Use One in Power BI",
    excerpt: "Pareto charts show which contributors drive the majority of a result. Here's what they're used for, how they work, and how to build one in Power BI.",
    date: "2026-08-05",
    category: "Concepts",
    relatedProduct: "pareto-pro",
    body: [
      { h: "What Is a Pareto Chart?" },
      { p: "A Pareto chart is a combination chart that visualizes concentration: it shows which contributors drive the majority of a result, and how quickly the cumulative total is reached as you move down the list." },
      { p: "It's named after the Italian economist Vilfredo Pareto, who observed in the late 1800s that roughly 80% of Italy's land was owned by 20% of the population. That same 80/20 pattern turned out to appear everywhere in business and nature:" },
      { ul: [
        "80% of revenue comes from 20% of customers",
        "80% of inventory movement is driven by 20% of SKUs",
        "80% of defects originate from 20% of root causes",
        "80% of support tickets come from 20% of product issues"
      ] },
      { p: "The Pareto chart makes this concentration visible at a glance." },
      { h: "How it's built" },
      { p: "A Pareto chart has three components:" },
      { ul: [
        "Sorted bars — the entities you're analyzing (customers, products, defect types...) sorted from the highest contributor on the left to the lowest on the right. Each bar shows that entity's share of the total.",
        "Cumulative line — a line overlaid on the bars that shows the running total as a percentage of the whole. It starts low on the left and climbs toward 100% as you move right.",
        "Reference line — a horizontal marker (usually at 80%) that intersects the cumulative line. The vertical crosshair shows you exactly which group of entities accounts for that 80%."
      ] },
      { p: "Everything to the left of that crosshair is your critical group — the few contributors that explain most of the result." },
      { h: "What Is a Pareto Chart Used For?" },
      { p: "Pareto charts are standard tools in quality management, operations, sales strategy, and supply chain. Some of the most common applications:" },
      { h: "Sales & revenue analysis" },
      { p: "Which customers generate the most revenue? A Pareto chart instantly surfaces whether your revenue is concentrated in a handful of accounts or spread broadly — critical information for key account strategy and resource allocation." },
      { h: "ABC inventory classification" },
      { p: "In supply chain, the ABC method groups products by their contribution to total volume or value: A items (top ~70%) get tight stock control; B items (70–90%) get standard replenishment; C items (90–100%) are reviewed for potential discontinuation. A Pareto chart is the standard visualization for this analysis." },
      { h: "Defect prioritization (Lean / Six Sigma)" },
      { p: "Which root causes should your quality team tackle first? The Pareto principle says that fixing the top two or three defect categories will eliminate the majority of your quality issues. The chart shows which ones they are." },
      { h: "Supplier & procurement analysis" },
      { p: "How concentrated is your supplier base? If 80% of your purchasing spend goes through two suppliers, that's a supply chain risk — and a Pareto chart surfaces it immediately." },
      { h: "Product portfolio review" },
      { p: "Which SKUs should stay in the catalog? Which ones occupy warehouse space for negligible volume? Pareto analysis separates the vital few products from the long tail." },
      { h: "General Features of a Pareto Chart" },
      { p: "Before getting into a specific tool, here's what any well-built Pareto chart should do:" },
      { ul: [
        "Automatic ranking — entities must always be sorted from highest to lowest contributor. The whole point of the chart is the ranked order; if the sorting is wrong, the analysis is meaningless.",
        "Dual-axis display — the left Y-axis shows each bar's individual contribution (usually as a percentage of total); the right Y-axis shows the cumulative percentage tracked by the line.",
        "Configurable reference lines — the 80% line is the classic default, but good implementations let you set the threshold to any value. ABC analysis requires two lines (at 70% and 90%); some use cases need three.",
        "Binning — when you have hundreds or thousands of entities (e.g. 5,000 SKUs), showing one bar per entity makes the chart unreadable. Binning groups entities into percentage-based buckets — the top 10%, the next 10%, and so on — turning a 5,000-bar chart into a clean 10-bar summary.",
        "Outlier handling — a single extreme entity (one mega-account worth 5× the next largest customer) distorts the entire cumulative shape. Good tools let you exclude top/bottom outliers so you can analyze the distribution of the \"normal\" part of your data.",
        "Cross-filtering — in a BI tool context, clicking a bar should filter the rest of the report. The Pareto chart is most powerful when it acts as an entry point that drives a table, a map, or other charts on the same page.",
        "Drilldown — for hierarchical data (Category → Subcategory → SKU), being able to drill into a Pareto level and have it automatically recalculate is far better than building a separate chart for each level."
      ] },
      { h: "Pareto Chart Pro for Power BI" },
      { p: "Power BI doesn't include a native Pareto chart. You can approximate one by combining a sorted column chart with custom DAX measures for rank and cumulative percentage — but it's a multi-step setup that takes 15–30 minutes, breaks when the data model changes, and doesn't support binning without additional calculated columns." },
      { p: "Pareto Chart Pro is a Microsoft-certified custom visual available free on AppSource that handles all of this automatically." },
      { h: "How it works" },
      { p: "You add two fields and the chart renders:" },
      { ul: [
        "Entity — the category you're ranking (Customer, Product, SKU, Supplier, Defect Category — anything)",
        "Value — the numeric measure (Revenue, Units, Defect Count, Purchase Amount)"
      ] },
      { p: "The visual sorts automatically, groups into bins, draws the cumulative line, and places a reference line at 80% by default. No DAX, no calculated columns, no combo chart configuration." },
      { h: "Features" },
      { h: "Automatic binning" },
      { p: "By default each bar represents 10% of your entities — so 10 bars total regardless of how many entities you have. In the Pro tier you can set bin size from 1% to 20%, from a granular 100-bar view down to a 5-bar executive summary." },
      { h: "Up to 3 reference lines" },
      { p: "Line 1 is on at 80% by default. Change it to any threshold, add a second line (both tiers), and a third (Pro) for ABC-style three-zone analysis. Each line has its own color, position, and label." },
      { h: "Drilldown on hierarchies" },
      { p: "Add a hierarchy to the Entity field — Category → Subcategory → SKU — and the standard Power BI drill buttons appear. Drill down and the Pareto recalculates automatically at each level. No separate charts needed." },
      { h: "Outlier exclusion (Pro)" },
      { p: "Exclude the top or bottom N% of entities before binning. Useful when a single dominant account distorts the shape of the rest of the distribution." },
      { h: "Value labels (Pro)" },
      { p: "Show the percentage contribution above each bar. Essential for executive dashboards and exported screenshots where the audience won't hover over the chart." },
      { h: "Cross-filtering" },
      { p: "Click a bin to filter all other visuals on the page. Multi-select with Ctrl to compare groups. Pareto Chart Pro participates fully in Power BI's standard cross-filter and cross-highlight model." },
      { h: "High contrast & accessibility" },
      { p: "Automatically switches to system high-contrast colors when Windows or Power BI high contrast mode is active." },
      { h: "Conditional formatting on bars" },
      { p: "In the Format Pane → Bar color, click the fx button to apply standard Power BI conditional formatting rules — color by value range, percentage tier, or gradient scale." },
      { h: "Step-by-Step: Your First Pareto Chart in Power BI" },
      { ol: [
        "Install from AppSource In Power BI Desktop → Visualizations pane → three dots → Get more visuals → search Pareto Chart Pro → Add.",
        "Add the visual to your canvas Click or drag the new icon from the Visualizations pane.",
        "Bind your data Drag a category field into Entity and a numeric measure into Value.",
        "Read the result Left Y-axis: each bin's % of total. Right Y-axis: cumulative %. Orange line: cumulative curve. Red dashed line: 80% threshold. The bin where the cumulative line crosses 80% is your breakpoint. Everything to the left is your critical group.",
        "Add a second reference line for ABC analysis (optional) Format Pane → Reference Lines → Show line 2 → set to 90%. You now have three zones: A (0–80%), B (80–90%), C (90–100%).",
        "Enable drilldown (optional) Replace the single category field in Entity with a hierarchy. Use the drill arrows in the visual header to navigate levels."
      ] },
      { h: "Example Configurations" },
      { p: "Classic 80/20 customer analysis: Entity: Customer Name · Value: Revenue · Line 1 at 80% → Shows which customers to treat as key accounts" },
      { p: "ABC inventory: Entity: SKU Code · Value: Sales Volume · Line 1 at 70% · Line 2 at 90% · Line 3 at 100% (Pro) → Segments the catalog into A, B, and C items automatically" },
      { p: "Defect prioritization: Entity: Defect Category · Value: Defect Count · Bins at 5% (Pro) · Outlier filter: top 2% → Surfaces the two or three root causes responsible for most defects" },
      { p: "Supplier concentration: Entity: Supplier Name · Value: Purchase Amount · Line 1 at 80% · Value labels on (Pro) → Quantifies supply chain risk at a glance" }
    ]
  },
  {
    slug: "what-is-a-matrix-heatmap",
    title: "What Is a Matrix Heatmap (and When Should You Use One in Power BI)?",
    excerpt: "A matrix heatmap turns a two-dimensional table into something your eyes can actually scan. Here's when to use one, and what it takes to get the numbers right.",
    date: "2026-08-04",
    category: "Concepts",
    relatedProduct: "dynamic-matrix-heatmap",
    body: [
      { h: "What Is a Matrix Heatmap?" },
      { p: "A matrix heatmap is a grid where two categories — one on each axis — intersect in cells, and each cell is colored based on its value. Instead of reading numbers row by row, you see the pattern instantly: darker or brighter cells jump out, showing you where the concentration, the outliers, or the gaps are." },
      { p: "It's the same idea as a spreadsheet's conditional formatting, but built for exploration rather than static reporting — you can usually click, filter, and drill into any cell." },
      { h: "When to Use a Matrix Heatmap" },
      { p: "A heatmap earns its place when you're comparing two categorical dimensions at once and want to spot patterns, not just totals. Common cases:" },
      { ul: [
        "Coverage analysis — which combinations of two categories (e.g. service level × region) have data, and which are empty",
        "Concentration analysis — where the bulk of a metric sits (e.g. which channel and quarter drive most revenue)",
        "Quality or performance scoring — average scores across two dimensions, to spot weak intersections at a glance",
        "Anomaly spotting — a single unusually high or low cell that would be easy to miss in a table"
      ] },
      { p: "If you only have one category to analyze, a bar chart is usually clearer. Heatmaps shine specifically when there are two dimensions interacting." },
      { h: "A Simple Example" },
      { p: "Imagine you're looking at customer orders and want to understand delivery performance. You have two categories — picking type (how an order was picked) and delivery distance (how far it traveled) — and you want to know how many distinct customers fall into each combination." },
      { p: "Laid out as a plain table, this might be dozens of rows: one per picking type, repeated for every distance bracket. Scanning it for patterns means mentally cross-referencing numbers across the whole table." },
      { p: "Turn the same data into a heatmap — picking type on one axis, delivery distance on the other, customer count coloring each cell — and the pattern appears instantly. Maybe most customers cluster in \"standard picking, short distance,\" while \"express picking, long distance\" is nearly empty. That's a five-second read instead of a five-minute one." },
      { h: "Heatmap vs. Other Chart Types" },
      { p: "It helps to know when a heatmap is the right tool versus the alternatives:" },
      { ul: [
        "Table with conditional formatting — works for small datasets, but doesn't scale past a handful of rows/columns before it becomes visual noise.",
        "Bar or column chart — better when you're comparing one dimension, not two. Stacking a second dimension into a bar chart quickly gets cluttered.",
        "Scatter plot — better for two continuous numeric variables (e.g. price vs. quantity), not two categorical ones.",
        "Matrix heatmap — the right choice specifically when both axes are categories and you want to compare a metric across every combination of the two at once."
      ] },
      { h: "What It Takes to Build One" },
      { p: "A basic heatmap needs three things: two categorical fields (one per axis) and a numeric value to color the cells by. The tricky part in most BI tools isn't the chart itself — it's getting the underlying numbers right." },
      { p: "Two situations trip people up constantly:" },
      { p: "Counting unique things across two dimensions. If you want to know how many distinct customers fall into each row/column combination, a normal sum or count will overcount — a customer who appears under two categories gets counted twice when you try to recombine measures into a grid. This needs to be computed directly from the raw ID column, not a pre-aggregated measure." },
      { p: "Totals that don't add up the way you'd expect. When a heatmap shows distinct counts, the row and column totals are genuine distinct counts across the whole row — not the sum of the individual cells. That's mathematically correct, but it looks \"wrong\" at first glance if you're expecting simple addition." },
      { h: "How Dynamic Matrix Heatmap Handles This" },
      { p: "Dynamic Matrix Heatmap is a Power BI custom visual built around exactly this kind of analysis — with the in-canvas flexibility to change what you're looking at without leaving the report." },
      { ul: [
        "Pick your axes on the fly. Drag fields into Dimensions and Measures, then choose X, Y, and Value directly inside the visual — no need to rebuild the chart to explore a different angle.",
        "Exact distinct counts, done right. Drop a raw ID column into Distinct Count Of and the visual computes true unique counts per cell — solving the double-counting problem above without any DAX gymnastics.",
        "Show value as % of row, column, or grand total — useful for answering \"where does this data actually concentrate?\" instead of just \"what's the number.\"",
        "Totals that are honest. Row and column totals reflect real distinct counts, with a clear explanation of why they may be smaller than the sum of visible cells.",
        "Scales from simple to complex. Free covers up to 3 dimensions and 2 measures — enough for most reports. Pro extends that to 10 dimensions, 5 measures, and unlocks distinct counts, percentage views, and totals."
      ] },
      { p: "Nothing is watermarked or time-limited — Free is a genuinely usable starting point, not a trial." },
      { h: "Try It" },
      { p: "If you're working with two-dimensional categorical data and need more than a plain table can show you, Dynamic Matrix Heatmap is built for exactly that." }
    ]
  }
];
window.POSTS = POSTS;