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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsDo not trust an AI-generated dashboard because it looks polished. Verify it by defining each claim, tracing the data to an authoritative source, independently recomputing high-impact metrics, testing edge cases, comparing every chart with its underlying values, and recording what remains unverified. There is no universal certification procedure for AI dashboards; a defensible review is a documented, purpose-specific set of checks.
What “verified” means for an AI-generated dashboard
Verification is not one pass or one button. It is evidence that a particular number or visual answers a clearly defined question for a defined population, period and decision. The U.S. Government Accountability Office describes data reliability in relation to accuracy, completeness and applicability to a specific audit purpose. That means a dashboard can be accurate for one use and unsuitable for another if its filters, timing or definitions do not match the decision.
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Separate three kinds of review:
- Numerical verification: Does the value equal a correct calculation from the intended records?
- Data-lineage verification: Did the value come from the right systems, fields, joins, filters and refresh?
- Communication and accessibility verification: Does the rendered chart represent the value honestly and remain usable for the intended audience?
A pass in one category does not prove a pass in the others. A perfectly calculated rate can still be misleading if its denominator is hidden, and an accessible chart can still display a wrong total.
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Write down what every important card, table and chart is claiming before checking its arithmetic. Natural-language prompts often leave crucial details unstated, so a reviewer should turn each claim into a testable specification.
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| Item to specify | Question to answer | Example |
|---|---|---|
| Business question | What decision should this answer support? | How many orders were delivered on time? |
| Metric definition | What is counted, and how? | Orders delivered by the promised date, counting each order once |
| Population | Which records are included? | Completed domestic orders, excluding cancellations |
| Numerator and denominator | What are the exact parts of a rate or percentage? | On-time orders divided by eligible completed orders |
| Units and currency | What unit, scale and currency apply? | U.S. dollars, nominal value, rounded to the nearest dollar |
| Time window | What start and end instants are included? | January 1 through January 31, inclusive |
| Timezone | Which timezone defines a day? | UTC, not the viewer’s browser timezone |
| Filters and exclusions | Which selections or removed records affect the result? | Region = West; test accounts excluded |
| Freshness | When was the source last updated? | Warehouse refresh completed at 06:00 UTC on the stated date |
| Rounding | At what stage is rounding applied? | Calculate from unrounded values; display one decimal place |
Save this specification beside the dashboard version. If you cannot state a denominator, time boundary or population, label the claim undefined, not verified. “Revenue,” “active user” and “conversion” can each have several legitimate definitions; choosing one silently is a calculation error even when the arithmetic is flawless.
Follow the data from source to screen
Begin with the authoritative system, not the visual. Identify the source tables, files or services; the owner responsible for them; the extraction or refresh timestamp; and every transformation between source and dashboard.
- Record the source and as-of time. Capture the exact table or report name, environment, query date and refresh status. A dashboard showing yesterday’s label over last week’s data is not current merely because its page loaded today.
- Inspect the grain of each source. Determine whether one row represents an order, line item, customer-day or another unit. Summing a value stored at customer level after joining it to line items can multiply it.
- List joins and their keys. Check whether each join is one-to-one, one-to-many or many-to-many. Count rows before and after the join and investigate unexpected increases.
- Reproduce filters. Compare the dashboard’s inclusion rules with the source query. Look for hidden filters, default date ranges, null handling, test records and deleted or late-arriving data.
- Reconcile to an independent control. Compare totals with a trusted operational report, accounting extract or direct source query for the same population and period. Explain every difference instead of accepting a close-looking number.
Government dashboard guidance recommends quality-assuring combinations of data and offering the underlying data in an alternate form. A downloadable table or machine-readable extract lets another reviewer check values without relying on the chart’s rendering.
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Join and duplication checks
For each key, calculate the number of source rows, distinct keys and rows after every join. A useful control table is:
| Checkpoint | What to compare | Warning sign |
|---|---|---|
| Source extract | Total rows and distinct business keys | Unexpected duplicates or missing keys |
| After dimension join | Rows and distinct keys versus source | Row count rises when a one-to-one join was expected |
| After filters | Included and excluded counts | Filter removes nulls or categories unintentionally |
| Final aggregate | Sum of groups versus grand total | Subtotals do not reconcile |
Recompute the calculations independently
Recalculate every metric that could change a decision, preferably from a source extract in a spreadsheet or a separate query. Do not copy the dashboard’s formula into your checking tool; an independent method is more likely to expose a shared mistake.
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Totals, averages and rates
- Total: Sum the intended rows once, at the correct grain. Confirm whether negative adjustments, refunds or credits are included.
- Average: Establish whether the dashboard means an average of records, an average of groups or a weighted average. The average of daily averages is not necessarily the monthly average.
- Rate: Recompute
numerator ÷ denominator × 100using the specified population. Verify that numerator records are a subset of denominator records unless the definition explicitly says otherwise. - Distinct count: Count distinct entities after applying the same filters. A count of rows is not a count of customers, devices or orders.
- Change over time: Check the baseline period, sign and formula. A percentage-point change and a relative percentage change answer different questions.
Retain the raw numerator, denominator, unrounded result and displayed result. This makes rounding and formatting errors visible. For example, 12 successful cases out of 20 eligible cases is 60%; 12 out of 24 is 50%, even if both cards show “12 successes.”
Prevent semantic aggregation errors
AI systems and dashboard tools can assign a convenient default aggregation that is inappropriate for a field. Tableau’s guidance warns: “While Tableau Agent can help create calculations through natural language, if a user asks for a calculation incorrectly or vaguely, an incorrect calculation can result.” Its recommended safeguards include defining common calculations in advance, using exact field names, documenting business terminology, verifying field roles and data types, and setting a suitable default aggregation.
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Check each field’s role before accepting a generated formula:
- Price, rate, percentage and unit-cost fields are usually not meaningful when summed.
- Amounts may be summed only at a grain that does not duplicate them.
- Dates should be treated as dates, not text labels that sort alphabetically.
- Identifiers should normally be counted distinctly, not summed.
- Boolean or status fields need an explicit rule for nulls and unknown values.
Use a second implementation
Implement the same definition in a different form: SQL versus spreadsheet formulas, or a hand calculation versus a small script. Compare intermediate groups, not only the final headline. If both methods produce the same answer but use the same flawed source or definition, the agreement is not proof; it only shows that the implementations are consistent.
Test AI-generated logic as draft work
Treat generated queries, formulas and calculated fields as proposals requiring human approval. The Australian Treasury’s evaluation resource states that “humans must check that AI outputs are correct and useful” when AI helps prepare or visualize data.
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Ask the system to explain, in plain language, the population, joins, denominator, null behavior, date boundaries and aggregation used by each calculation. Then compare that explanation with the claim specification and source schema. Do not accept an explanation that cannot be mapped to actual fields and records.
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Probe edge cases and surprising results
Normal-looking rows can hide a broken rule. Deliberately test cases at the boundaries and cases that contradict expectations.
- Date boundaries: Test the first and last instant of the period, daylight-saving transitions where relevant, and records near midnight in the stated timezone.
- Empty segments: Select a category or region with no records. Confirm that the dashboard shows zero, “no data” or an intentional omission rather than a misleading blank or stale value.
- Nulls and unknowns: Filter records with missing categories, dates or amounts. Verify whether they are excluded, grouped as unknown or imputed.
- Extreme values: Trace the largest and smallest values to source records. Check whether one correction, test account or duplicated transaction drives the result.
- Filter combinations: Apply filters one at a time and together. Confirm that the displayed population count and every dependent chart change consistently.
- Subtotals: Compare category totals with the grand total. Investigate overlaps, “other” buckets and categories that are not mutually exclusive.
- Trend anomalies: Investigate spikes, flatlines, sudden breaks, impossible rates and abrupt changes that coincide with a schema, code or refresh change.
Increase the depth of testing when the result controls a financial, safety, staffing, compliance or public-facing decision. A minor discrepancy in a low-risk exploratory chart may require explanation; the same discrepancy in a regulatory metric should block release until resolved.
Verify each chart against its data
Use the underlying table or query output as the comparison point. A polished visual is not evidence that its values or message are correct.
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| Chart check | How to test it | Failure to look for |
|---|---|---|
| Plotted values | Compare selected points, bars or labels with the underlying rows | Wrong series, stale cache or incorrect sorting |
| Chart type | Ask whether the encoding fits the question | Area or 3-D effects obscuring differences; a line joining unrelated categories |
| Axis scale | Read minimum, maximum, intervals and zero treatment | Truncated axis exaggerating a small change; scale inconsistent across panels |
| Units and labels | Check axis titles, currency, percentages and date format | “5” displayed without saying 5 dollars, 5%, thousands or millions |
| Categories and legend | Match colors, order and names to the data | Similar colors, omitted category or legend that changes meaning |
| Time range | Compare the title, filter state and plotted endpoints | Title says monthly while the query includes partial days |
| Annotations | Trace every callout to a calculated value or documented event | Text claims causation when the data shows only correlation |
| Totals and subtotals | Reconcile stacked segments and labels with the table | Segments overlap or are double-counted |
Titles and annotations must stay within what the data supports. “Orders fell 8%” is a numerical statement; “the promotion caused the fall” requires additional evidence. If two charts compare options, hold metric definition, population, period, units, denominator, scale and refresh time constant. Disclose any difference before drawing a comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test the rendered dashboard, not just the query
Open the final artifact at the size and device where people will use it. Interact with every filter, drilldown, tooltip and export.
- Confirm that a filter changes all dependent cards and charts, not just the one nearest its control.
- Check that drilldowns preserve the parent filter and do not silently switch date ranges or populations.
- Reload the page and verify that defaults, cached values and refresh indicators are consistent.
- Resize the window and inspect responsive breakpoints. Labels should not overlap, disappear or be clipped.
- Test exports and downloads. The exported table should carry the same filters, units, date range and as-of time as the screen.
- Inspect tooltips for precision, units, category names and accessible text; never rely on color alone to convey a value.
Accessibility checks
The Government Analysis Function states that “No chart is fully accessible.” Treat accessibility as a set of practical tests rather than a one-time declaration.
- Navigate with a keyboard only. Every filter, tab, control and drilldown needs a logical focus order, visible focus indicator and usable keyboard operation.
- Use a screen reader to identify the dashboard title, headings, controls, chart summaries and data tables. Make sure the reading order matches the visual order.
- Check color contrast and verify that status, category or trend is not communicated by color alone.
- Zoom to 200% and confirm that text, controls and data remain available without loss of content or functionality, subject to the exceptions in WCAG 2.1’s resize criterion.
- Provide an alternate table or machine-readable download when a chart cannot expose its values and relationships clearly.
Microsoft’s spreadsheet accessibility guidance similarly recommends descriptive chart titles and axis titles, data labels where useful, accessibility checking and screen-reader navigation. These checks improve access, but they do not validate arithmetic.
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| Symptom | Likely area | Required action |
|---|---|---|
| Dashboard total is larger after a join | Duplicate rows or wrong join cardinality | Fix the join or aggregate at the correct grain; rerun all dependent metrics |
| Rate differs but numerator matches | Denominator, exclusions or date range | Compare denominator records and filter logic explicitly |
| Only displayed value differs | Rounding, formatting or cached rendering | Compare unrounded query output and refresh the artifact |
| One category is missing | Null handling, filter or suppressed small counts | Document the rule or correct the filter; do not silently reassign records |
| Chart trend looks dramatic but table does not | Axis range, scale or chart type | Use an honest scale and state the unit and baseline |
| Values change between reviewers | Timezone, default filter, refresh or cache | Record the as-of time and make defaults explicit |
Do not “fix” a mismatch by editing a label or manually overriding a card. Correct the earliest faulty step, regenerate the dashboard and repeat the checks that depend on it.
Keep an evidence record with the handoff
A reviewer should be able to reproduce the decision without relying on memory. Attach a concise verification log to the dashboard release.
- Dashboard name, version or commit and rendered release date
- Source systems, table or file names, owners and refresh or as-of timestamps
- Metric definitions, populations, numerators, denominators, units, timezone and rounding rules
- Queries, spreadsheet formulas or other independent calculation logic
- Rows or samples traced through joins and transformations
- Filter, boundary, null, subtotal and anomaly tests performed
- Chart, responsive-layout and accessibility checks, including the 200% zoom result
- Discrepancies found, disposition, remaining assumptions and named approver
Mark unavailable evidence as unverified. Do not infer that a value is correct because the platform generated it, because two visuals agree, or because no one has reported a problem. State exactly what was checked and what was outside the review.
A practical release checklist
- Can every headline claim be written as a precise question with a defined population and period?
- Are source, owner, refresh timestamp, joins, filters and exclusions known?
- Have high-impact totals, averages, rates and distinct counts been recomputed independently?
- Do field types and default aggregations match the business meaning?
- Have date boundaries, empty segments, nulls, extremes, filter combinations and subtotals been tested?
- Do chart values reconcile with an underlying table or query?
- Are axes, units, labels, categories, annotations and scales honest and readable?
- Do filters, drilldowns, exports and responsive layouts behave correctly?
- Can keyboard and screen-reader users reach the content, and does it remain usable at 200% zoom?
- Is an alternate table or machine-readable data available where the chart cannot stand alone?
- Does the handoff identify verified items, discrepancies, assumptions and unverified items?
Release only the claims that pass the checks appropriate to their decision risk. Everything else should carry a visible qualification or remain out of the decision until its definition, lineage or calculation can be demonstrated.
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