How do you report data quality results to your audience?

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

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

Sorry, don’t agree with above statement. Data quality report at attribute or attribute + table/file level should be available for whole organisation transparently. Rather than making data quality report tailor made for specific audience organisations should spend time in fixing those at source or other possible layers. Data quality report should not be considered confidential or private.

Determine quality thresholds that are important to your key stakeholders. Develop charts and graphics that compare your results to those thresholds. This helps communicate visually if there is anything to be worried about.

Understanding audience is essential for effectively communicating data quality results. It ensures that presentation is relevant, clear, engaging, and actionable, ultimately leading to better decision-making and problem-solving related to data quality.

In an era where stakeholders are inundated with reports distilled into KPIs and graph formats, the role of a data quality engineer transcends the delivery of standard presentations. The expectation of 'WOW' reports must be rooted not in the glitz of their design but in the robustness and ingenuity of the information they convey. The essence of what data quality engineers should strive for is the creation of insightful narratives that extend beyond conventional analytics. By leveraging cross-referencing of datasets, we can unveil interdependencies and correlations that may not be apparent superficially. This approach enriches the data's story and underpins strategic decision-making with a layer of depth that traditional reports lack.

Agree, the audience might vary across organisation, depending on the business units that are involved. Usually this is divided into three categories - Data analyst and scientists Business users IT teams

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

Your data quality dashboards should be an inverted triangle. The first section should show the summary - the data quality KPI score. The next section should show the KPI or a count of data quality issues for each data source or for each type of issue. The final section should show the details: columns with issues and issue details. If your dashboard shows incomplete tables, the last section will show the list of tables, their current row count, and the expected row count, which is the minimum number of rows that this table should store.

Dashboards, created in tools like Power BI, are fantastic for a dynamic overview, allowing users to explore data in real-time. They're especially effective for audiences who need regular, up-to-date insights. For more detailed, comprehensive analysis, traditional reports are my go-to. They're ideal for documenting findings and recommendations in a format that's easy to distribute and archive. To persuade decision-makers or highlight key achievements and challenges, a well-crafted presentation is invaluable. It allows for a narrative style that can communicate the story behind the data, driving home the importance of action. The choice of tool and format can influence the effectiveness of your communication in data quality projects.

The secret to powerful data quality reporting? It's in the presentation. Dashboards bring data to life, enabling a hands-on exploration that empowers stakeholders to dive deep on their own terms. But when detail is the demand, reports stand unmatched in their ability to weave together comprehensive analysis and actionable insights. And for the moments that call for a collective push towards improvement, presentations transform data points into a compelling narrative. Whether it’s Power BI's dynamic visuals or the classic depth of Excel, choose the medium that turns data quality from a concept to a call to action.

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.

Being able to visualize data quality can be a great way to give a high-level view of the quality of the data. However, don't get mesmerized by a pretty picture. There are people who use the data and need to know exactly what's wrong if it's not 100% good. That detail can tell them what data to avoid and what they can use in their job. The detail can give them a level of trust in what they're using. The detail is also needed by the people who have to resolve the data quality issues. It's fine having that pretty picture, but also have the detail.

Creating data visualizations that are easy to understand and easy to read will have an impact on business units for evaluating data processing that has been carried out as well as highlighting key data quality metrics that need to be improved based on a predetermined priority scale.

The key to effective data visualization is simplicity and clarity. Choosing the right type of chart or graph is crucial; for instance, bar charts for comparisons, line charts for trends, and heat maps for highlighting outliers or patterns. Consistent use of colors, labels, and legends not only enhances understanding but also makes the visualization more visually appealing. Avoiding clutter is vital to maintain the integrity of the data. Misleading scales or too many elements can confuse and detracte from the main message. Lastly, feedback is a critical step. Testing and refining visualizations with input from others ensures that your visuals are not only accurate but also resonate with your audience.

Visuals speak louder than words in data quality narratives. A well-crafted chart can illuminate the hidden stories within your data, spotlighting trends and outliers with clarity that commands attention. Yet, the art of data visualization is in its honesty -- choose graphs that mirror the truth of your numbers, employ colors and labels that clarify, not confuse, and banish misleading scales to the realm of distortion. Context is the golden thread that ties your visual story together. without it, even the most striking visuals fall flat. So, refine with feedback, for a visualization that doesn't just show data, but tells its story.

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.

Knowing the audience is critical while communicating the data quality results. The data consumer may be interested in the metrics of the data quality while the data generator need to understand causes and impact of data quality. Dashboard with percentages/scores for the data quality is good but supporting evidence and examples are what drives home the impact desired.

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.

It's crucial to structure your report with a clear introduction, informative body, and a concise conclusion. Start by setting the scene in the introduction, clearly stating the main point. Then, in the body, dive into your data quality results, explaining your analysis with supporting evidence and examples. Finally, wrap up with a conclusion that summarizes key findings and suggests actionable recommendations. Tailoring your message to be audience-specific is key. Use language and terminology that resonates with them and anticipate their questions and concerns. This approach not only makes your report informative but also ensures it's persuasive and impactful, driving your data quality initiatives forward.

Data Quality: Beyond Numbers, It's a Narrative That Commands Change. Drawing from my own narrative, let's delve into the world of logistics. Imagine an introduction setting the stage of a logistics company plagued by delayed shipments. The body unravels a tale of incorrect vendor data leading to these delays. I weave through the plot, showcasing graphs that pinpoint where and how data errors creep in. The climax reveals a stark connection between data quality and on-time deliveries. Concluding, I present a strategy that calls for precise data handling, turning the page towards improved efficiency. This story doesn't just inform; it engages and calls for a collective leap towards better data practices.

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👥

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.

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.

Data Quality discussions should start well before implementation cycle and should be part of the Design process. Sample subsets of data should be thoroughly profiled. Though it is impossible to detect all anamolies initially, at least a good number can be documented and used to formulate solutions. Data Quality should be discussed and driven by client requirements on how to handle the issues and the level of complexity of the process determined. Proper tool selection or customised solutions can be recommended. The DQ life cycle is established and automating the process has great benefits.

One thing I have found helpful is that I should tailor my results dissemination to the targeted audience(s). These makes it easy for adoption and scale.

In an organization, a dedicated data quality initiative was undertaken. Rigorous assessments were conducted, identifying and rectifying inconsistencies. The team meticulously documented the process and outcomes, showcasing a substantial improvement in data accuracy. Through a clear and concise presentation, stakeholders were informed of the enhanced data quality, instilling confidence in decision-making. The success of the initiative underscored the importance of prioritizing and regularly monitoring data quality for reliable business insights.

It's essential to continuously engage with your audience post-reporting. Feedback loops are key. Encourage your audience to provide input on the report's effectiveness and areas for improvement. This not only enhances future reports but also fosters a culture of continuous improvement in data quality practices. Additionally, ensure your report remains accessible and revisit it periodically to update with new data or insights, keeping it relevant and useful. #DataQuality #ContinuousImprovement #EngagingReports