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Data Literacy

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Raw Data Is Not Insight

💡 Hover over any tip or practice to see examples of how raw data becomes insight you can act on
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Raw Data Is Just Records

  • Rows, logs, events, and exports are raw data, not insight
  • Raw data tells you what happened, but not what matters
  • Without structure, most users cannot interpret raw records reliably
  • Raw data is useful for audit and exploration, but not decision making by itself
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Insight Requires a Question

  • Insight starts with a question you want to answer
  • The question defines which fields, filters, and comparisons matter
  • Without a question, dashboards become data dumps
  • Good questions turn data work into clear decisions and actions
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Context Turns Data Into Meaning

  • Timeframe, units, and definitions change how numbers should be read
  • Benchmarks and baselines help users judge if results are good or bad
  • Segmenting by region, product, or customer reveals what totals hide
  • Context prevents overreacting to one off spikes or dips
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Aggregation Creates Signals

  • Summaries like totals, averages, and rates help people scan patterns fast
  • Trends and comparisons show direction, not just values
  • Choose the right grain so the metric matches the decision level
  • Overly detailed tables hide signals inside noise
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Interpretation Makes It Actionable

  • Insight includes an explanation of why something changed
  • Interpretation connects metrics to drivers and business reality
  • Highlight exceptions that need attention, not everything that exists
  • Good insight points to the next step, not just the number
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Common Mistakes That Kill Insight

  • Treating totals as truth without checking mix changes or outliers
  • Using averages that hide variability and edge cases
  • Showing every metric instead of the few that matter most
  • Missing definitions that cause teams to argue about what numbers mean

Best Practices for Turning Raw Data into Insight

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Start With the Decision
Write the decision someone needs to make and the question that supports it. This prevents you from collecting or displaying data that looks impressive but changes nothing. When the decision is clear, the report naturally focuses on the few metrics that matter.
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Add Context and Definitions
Label metrics with timeframe, unit, and inclusion rules so users interpret numbers consistently. Add short definitions for terms that can be misunderstood, like active customer or net revenue. Context turns a value into information that can be compared and trusted.
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Use Baselines and Benchmarks
Always compare against something meaningful such as last period, target, or historical average. Benchmarks help users judge whether a change is noise or signal. Without a baseline, even accurate numbers can lead to the wrong reaction.
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Summarise, Then Drill
Lead with high level signals like trends, rates, and top drivers before showing detailed rows. Drillthrough and detail tables should support investigation, not be the first view. This flow helps people understand the story before exploring the evidence.
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Explain the Why
Pair key changes with likely drivers such as mix shift, seasonality, or a process change. Where possible, show driver breakdowns so users can validate the explanation. Insight is not only what changed, it is why it changed and what to do next.
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Validate Before You Narrate
Check totals, row counts, and key ratios against trusted sources and prior periods before publishing conclusions. Validation avoids storytelling based on broken filters, missing data, or duplicates. Trust is easier to lose than to rebuild.