How do you report data quality results to your audience?

If your grandma understands the reporting, then that is at a good level. No missunderstaing or false decision making based on the data.

Some of the key elements of getting to know your audience is: 1. Reconstructing the State of the Data by understanding how it got to where it is. 2. Understanding the audience's expectation of where it should 3. What is the gap between what they expect and where it is By understanding these 3 elements can create a story that they relate to and are interested in resolving. If I understand their expectations, I like to challenge myself to answer the following questions before presenting the report: 1. What is happening? 2. Why is it happening (RCA)? 3. What is likely to happen? 3. What should we do about it?

For effective impact, data quality results need to answer the question "so what." What is the impact of non-compliant data to the business? It is relatively straightforward to generate a large library of data quality indicators that expose non-compliant data. What is harder is linking them to business impact. At Civica, we are working with customers to create data quality dashboards that link data quality indicators to core business processes, so that senior leaders can see whether their core business process are driven by data that is fit for purpose. We find that highlighting core business processes that are running on data that is *not* fit for purpose is a great way of getting attention and driving improvements.

The biggest factor to consider is how it impacts them, their business goals, their risk profile, or their cost/profitability. It needs to be tangibly tied to something that impacts them.

Report to internal business stakeholders like Marketing, Sales, Supply Chain and etc .. you build dashboard for your KPIs and measure the data quality and review the same in Change review board or Steering committee meetings or Data Governance committee meetings to address then improvements and challenges to take the better decisions to correct the data . Ex: Address data , how ammu accounts have missing addresses which might impact the billing and shipping . What are the correct actions and etc

Data Quality results to include > Quality% (Defective v/s good data) based on number of occurance of defect of a transaction. > Quality feedback defect wise. > Good quality remarks. > Quality to be driven by client requirements.

Written Reports: Use documents, presentations, or emails to convey your findings. This allows for detailed explanations and visual aids. Dashboards: Interactive dashboards provide a visual representation of the data quality metrics and allow users to explore the data themselves. Meetings/Presentations: Sometimes, a face-to-face or virtual meeting can be the most effective way to communicate results, especially for complex or sensitive topics.

Dashboards and presentations are powerful way to share the data quality results. Dashboards with drill down and export options will be helpful for data stakeholders. Excel may also be helpful. Presenting the data quality issues in the format that business teams and data stakeholders is very important.

To choose the format of visuals, consider the type of data, the purpose of the visualization, and your audience. Choose the format that best suits the data, purpose, and audience.

I create a dashboard or report in the same file as the data collection or data processing location, so that the data quality measurements carried out can be seen in real time

Include proper data visualization to communicate the data quality report to stakeholders. Prepare a story based on the visualizations to effectively communicate the data quality assessment report. Before preparing the report, finalize the visualization type to be used for different categories of data quality dimensions.

Stunning data visuals, compelling stories, and underscore how top-notch data quality propels us towards triumph. Ignite action and inspire excellence.

Data visualization plays a critical role in reporting data quality results. By using visual representations of data, such as charts, graphs, and maps, data visualization can help audiences quickly and easily identify trends and patterns. Data visualization can make it easy to see how data changes.

Visualizations serve as a bridge between complex data & human comprehension, making DQ issues immediately apparent and actionable. They provide a clear, intuitive representation of anomalies, patterns, trends, allowing stakeholders to grasp the significance of data quality problems without delving into technical details. It enhances engagement, memory retention, and decision-making by transforming abstract data quality metrics into meaningful insights. Furthermore, they facilitate effective communication by enabling all stakeholders, regardless of their technical background, to participate in discussions about data quality, fostering collaboration and ultimately leading to more informed and timely resolutions to data quality challenges.

Pictures make complex things simple than explaining words. Considering data visualization to present data quality issues can be helpful; Choosing right visualizations may be challenging. Visual representations should be complete with legends, labels and titles that would help audience to understand.

It's important to directly relate the data quality results to the downstream information that's presented to the business. How does it impact the accuracy, integrity, and validity of business results that drive business decisions? The quality of the data that underlies the information and dashboards need to be visible and clear in the consuming applications and users who rely on the data to make business decisions.

Data stories are very important for Data Quality presentation. Data Quality practitioners should be able to effectively walk through the data quality results with findings, that came through analysis, and with examples. Scenarios should be explained in a way to build trust. Short and specific stories will yield expected results.

It is important to humanize & contextualize what might otherwise be dry and abstract statistics. Stories have the power to engage & empathize with the audience, making the problems relatable and memorable. By weaving a narrative around data discrepancies, inaccuracies, or inconsistencies, we can help stakeholders understand the real-world impact and urgency of addressing these issues. This approach fosters a sense of purpose and a call to action, encouraging stakeholders to collaborate and find solutions to improve DQ. A well-told story not only conveys the "what" of data quality issues but also the "why" and "how," helping stakeholders better grasp the significance and providing a compelling reason to take action and make improvements.

When using data visualization to share stories, it is important we choose the right visual format, use clear and concise labels, highlight important insights, and tell a story that helps user. By following these tips, we can use data visualization to create more compelling and engaging stories that resonates with the audience.

when I make a report on the quality of data carried out by the business unit in data processing and data collection. I use a before and after format, so that the impact and changes felt by the business unit are more clearly visible and have a direct impact on the presentation I make.

There are few additional aspects to consider when reporting on Data Quality - 1: Understand your data quality KPIs; the most common KPIs for DQ are - completeness, freshness, validity, uniqueness, accuracy, and consistency 2: Define how you want to measure these KPIs and report on them 3: Continuously analyze the data against these KPIs; IMO, the reporting format is less important than the explainability of the KPI and its impact on business.

I?may?display?the?quality?of?my?data?in?an?interactive?dashboard?or?report ?? However,?the?most?crucial?step?is?to ?identify?the?target?audience?which?? will?influence?the?visuals,?dashboard,?and?storytelling?that?you?choose. While?the?decision-makers?concentrate? on?the?KPI's?and?Recommendations, the?technical?audiences?requires?more ?information. Thus,?give?attention?to?the?audiences?and?base?every?narrative?technique?on?them??

Due to my experience; One of the effective approaches to represent data quality issues on top of visualization is to financialise the results if possible. Giving the possible financial effects (the possible gains or current losses which will touch mostly customer activities or P&L) attracts more stakeholder attention as these ratios become more tangible for most of the departments and top management as well.

Data quality reports should not take anyone's time unless there is an issue to address. If there exist any, it should be reported only to those for whom this issue may be relevant for preventing alert fatigue. The report should be in a informative way, with a detailed description of why someone should look at it and with context (how the data usually looks like?). Data observability provides tools for context and root cause analysis.

Apart from the fact that the measure of KPIs for DQ is so relevant, it is essential to understand the impact on the business results, the risks the company faces, and the action plan to improve in the short and the middle term.