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Reporting assurance - assertions

As a senior leader, you already know about the five financial audit assertions. 

Outside of financial statements, we can apply a variation of those assertions to operational reports.

In fact, four of the five assertions are directly relevant, and we can add a new specific one.

Here’s an overview of what we’ll cover in this article. Focus on the column “Reporting assertion”.


FS assertion

Reporting assertion

Definition (adapted from standards)


Accuracy and valuation

Accuracy The correct values are included


Existence Existence Records relate to activities that have occurred during the period


Completeness Completeness All records are included


Rights and Obligations Relevance


Presentation and Disclosure Presentation Proper classification, description and disclosure

Quite a strong fit. “Rights and Obligations” is quite specific, and there may be a link. But relevance is more important for operational reports. 

1. Accuracy

Some things that we need to have confidence in:

  • we can trace the data back to its source
  • we trust the data provider
  • we know how the data has been collected
  • we trust how the data has been processed or transformed
  • we trust that the data has been stored securely, and can't be tampered with.

2. Existence

Possibly something that you have been able to avoid. But it does happen.

Here are things to look out for:

  1. incorrect joins - where two or more tables are merged incorrectly
  2. incorrect filters - for example, where cancelled/voided transactions are included
  3. hallucinations - maybe, but not common, unless you’re somehow using an LLM for your reporting.

3. Completeness

Deliberately leaving out some data, without explaining why or even saying that it is excluded.

There are sometimes legitimate reasons for leaving data out of reports and dashboards.

For example, you many have some technical data that is captured in the backend but doesnt need to be in the report, because it doesnt contribute to it.

But if the data that is left out could change the result, we need to have a good reason for leaving it out, say that it has been excluded, and explain why we have not included it.

We let the data tell the story. We don’t twist the data to fit a predetermined shape/narrative.

If we’re not happy with any of these confidence factors, we need to decide:

  1. Do we just pull the report together anyway
  2. Do we use the data, but explain that it is not completely reliable
  3. Do we delay reporting until we can get the level of comfort we need.

4. Relevance

Relevance is an important aspect of presenting non-financial data in dashboards and visualizations. Here are some examples of how relevance can be applied to non-financial dashboards and visualizations:

  1. Relevant Data: When selecting data to present, make sure that it is relevant to the topic and the audience. Avoid including unnecessary data that may confuse or distract the audience.
  2. Relevant Metrics: Choose metrics that are relevant to the topic and the audience. Avoid using metrics that are not meaningful or do not provide actionable insights.
  3. Relevant Timeframes: Choose timeframes that are relevant to the topic and the audience. Avoid using timeframes that are too short or too long and do not provide meaningful insights.

By focusing on relevance when presenting non-financial data in dashboards and visualizations, you can ensure that the data is presented in a way that is meaningful and actionable. This can help to ensure that the audience understands the data being presented and can make informed decisions based on the information provided.

5. Presentation

As important as it is to have the right data, presenting it properly is crucial.

Here are 5 tips to help avoid presentation errors:






Language and Clarity

Use everyday words to make your report easily understood.

Use terms like "likely" or "probable" to express uncertainty.

Use vague language or jargon.

Use words or phrases that imply certainty, if you're not certain.


Simplicity Keep charts and graphs simple. Make it complex - this can confuse rather than clarify.


Context Provide the necessary background and context for your data.


Disclosure Be transparent about data sources, assumptions, and limitations. Address outliers or anomalies in your data. Hide information - this erodes trust.


Audience Tailor your report to your audience's needs, and their level of understanding. 

This article is a work in progress, periodically updated. Last update: 23 Oct 2023.


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