How can you communicate data quality and accuracy to your audience?

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Data quality and accuracy are essential for any data analytics project, but how can you convey them to your audience effectively? Whether you are presenting your findings, creating a report, or sharing a dashboard, you need to communicate the reliability and validity of your data in a clear and convincing way. In this article, you will learn some tips and best practices for communicating data quality and accuracy to your audience.

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Mahdi Sheikhi

Cloud Engineer | 23x Microsoft Certified Professional | Azure | Power Platform | Data | AI | Developer | MCT |…

1 Why data quality and accuracy matter

Data quality and accuracy refer to how well your data reflects the reality of the situation, problem, or phenomenon you are analyzing. Data quality and accuracy can affect the credibility, relevance, and usefulness of your analysis and recommendations. Poor data quality and accuracy can lead to inaccurate conclusions, misleading insights, and wrong decisions. Therefore, you need to ensure that your data meets the standards of completeness, consistency, timeliness, accuracy, and validity.

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Kris Teece

Principal Investigator GWIC, Consultant for Complex Behavior Supports.- Disordered Sexual Behaviour, Abuse and Offending
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Most people understand the concept of data quality, the issue appears to be truly understanding processes from those entering data and the data systems used to ensure data quality. Simple aspects like being able to merge identities with the same name , or human errors related to spelling, are vital. It's important that not everyone accessing a system can adjust information unless they have the correct training, however everyone needs to be able to communicate changes needed to someone who can. Data itself is simple, its the processes behind the data entry that are often the fall down. The bar for quality needs to be 100%. It will generally fall short but the aim is should always be 100%.

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2 How to assess data quality and accuracy

Before you communicate data quality and accuracy to your audience, you need to assess them yourself. You can use various methods and tools to check and improve the quality and accuracy of your data, such as data profiling, data cleaning, data validation, and data auditing. You can also use metrics and indicators to measure and monitor the quality and accuracy of your data, such as error rates, completeness rates, accuracy rates, and validity rates. You should document and report the results of your data quality and accuracy assessment as part of your data governance and management process.

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Mahdi Sheikhi

Cloud Engineer | 23x Microsoft Certified Professional | Azure | Power Platform | Data | AI | Developer | MCT | Developer | Software Engineer
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To make sure your data is good and correct, it's important to check it regularly. This is not something you do just once. Everyone involved, such as data experts, business people, and others, must work together to look at data from different angles. This team effort helps to better understand the data. There are also computer programs that can help automatically find and correct errors in data. It makes it faster and easier, and everyone in the company needs to understand how important good data is and do their part to ensure it is preserved.

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3 How to communicate data quality and accuracy verbally

When you are presenting or explaining your data analysis to your audience verbally, you need to communicate data quality and accuracy in a concise and confident way. You should avoid using vague or ambiguous terms, such as "good", "bad", "high", or "low", to describe your data quality and accuracy. Instead, you should use specific and quantifiable terms, such as "95% accurate", "98% complete", or "99% valid", to demonstrate your data quality and accuracy. You should also provide evidence and sources to support your claims, such as data quality reports, validation tests, or external benchmarks.

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4 How to communicate data quality and accuracy visually

When you are creating or displaying your data analysis to your audience visually, you need to communicate data quality and accuracy in a clear and consistent way. You should use appropriate and effective visual elements, such as charts, graphs, tables, or icons, to show your data quality and accuracy. You should also use consistent and meaningful colors, symbols, labels, or annotations to highlight your data quality and accuracy. You should also provide legends, keys, or notes to explain your data quality and accuracy indicators and metrics.

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5 How to communicate data quality and accuracy in writing

When you are writing or publishing your data analysis to your audience in writing, you need to communicate data quality and accuracy in a detailed and structured way. You should use precise and objective language, such as "the data is accurate within a 5% margin of error", "the data is complete for the period of January to June 2020", or "the data is valid according to the industry standards", to describe your data quality and accuracy. You should also provide references and citations to back up your data quality and accuracy statements, such as data quality documents, validation reports, or external sources.

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6 How to handle data quality and accuracy issues

Sometimes, you may encounter data quality and accuracy issues that are beyond your control or influence, such as missing data, inconsistent data, outdated data, or inaccurate data. In such cases, you need to communicate data quality and accuracy issues to your audience in a transparent and honest way. You should acknowledge and explain the causes and impacts of the data quality and accuracy issues, such as data collection errors, data integration challenges, data update delays, or data measurement errors. You should also propose and implement solutions and actions to address and mitigate the data quality and accuracy issues, such as data imputation, data standardization, data refreshment, or data correction.

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Kris Teece

Principal Investigator GWIC, Consultant for Complex Behavior Supports.- Disordered Sexual Behaviour, Abuse and Offending
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Consultation with the people entering, using and analysing the data captured, should be done at regular intervals a few times a year. Understanding emerging trends and new areas to address or respond to is ongoing. If the cohort using the system have a clear understanding of how this data matters to them personally eg demonstrates the need for additional staffing, demonstrates a clear increase in this area requiring additional resources etc, they will invest more themselves. Policing data should not be the job of a certain role or team. Data is like OHS, we are all responsible to get it right.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you communicate data quality and accuracy to your audience?

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1 Why data quality and accuracy matter

2 How to assess data quality and accuracy

3 How to communicate data quality and accuracy verbally

4 How to communicate data quality and accuracy visually

5 How to communicate data quality and accuracy in writing

6 How to handle data quality and accuracy issues

7 Here’s what else to consider

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