How do you share data quality results with stakeholders?

When defining the scope and criteria for data analysis, it's crucial to consider various factors Scope - Visualizing Data: Create clear charts and graphs to understand data quality results - Business Impact: Identify how data quality affects business operations - Recommendations: Offer practical suggestions for improvement - Ongoing Communication: Maintain a dialogue with stakeholders Criteria for Data Quality - Data Source: Determine if data comes from internal or external - Data Domain: Identify the field, like finance - Stakeholder Needs: Understand what information is important to your audience By combining these elements, you ensure that data quality insights are well-understood and can drive meaningful actions in data analysis.

In addition to defining the scope and criteria, there are several other important considerations when sharing data quality results with stakeholders: 1. Understand Stakeholder Needs; 2. Visualize Data Quality Findings; 3. Provide Context and Explanation; 4. Link Data Quality to Business Impact; 5. Offer Actionable Recommendations; and, 6. Foster Ongoing Communication. Effective communication of data quality results requires understanding stakeholder needs, visualizing findings, providing context, linking to business impact, offering actionable recommendations, and fostering ongoing communication. By following these practices, you can ensure that data quality insights are effectively shared, understood, and acted upon by stakeholders.

Data Quality Assessment is based on the completeness, reliability, accuracy, and consistency of the data. It can't be used if not passing these points. But we have to consider different points too before defining the scope and criteria with stakeholders: Criteria for Data Quality: 1. Source of Data (Internal/External/Public/Various) 2. Domain of Data (Financial/Medical) 3. Size of Data 4. Understand stakeholders and their needs Scope for Data Quality: 1. Visualizing and Interpreting Data Quality results 2. Impact on business 3. Help in providing recommendation 4. Ongoing Communication Scope and Criteria of data analysis change but keeping these points in mind data insights are easily understood and can be acted upon.

We should look for are these three things while data quality assessments: 1. Is the data complete in all aspects without repetition. For example, in large data sets multiple people may be collecting data and they might cut and paste data to bring it together. Some portion of data can be missed out during this process of collation and another common mistake is repetition. Pasting it in two places. 2. Proper differentiations in values for each variable. For example, missing values could be 99. Leaving it blank or giving the value 0 is not permissible. 3. Doing simple mean, median, range calculations. This will help identify outliers. Examining the outlier is an important data assessment tool to find mistakes.

In my opinion,here are some practical tips on how to share data quality results with stakeholders: 1. Define the scope and criteria: Define the scope and criteria of the data quality results that you want to share with stakeholders. 3. Collect and analyze the data to ensure that it is accurate and reliable. 4. Present the data in a clear and concise manner: 6.Engage stakeholders in the process of sharing data quality results by soliciting their feedback and input. 7.Establish a data quality improvement program that clearly defines the steps that will be taken to improve data quality. By following these tips, you can effectively share data quality results with stakeholders and ensure that data is accurate, reliable, and useful.

Final results can vary widely depending on how you clean the data! I would absolutely be documenting and discussing how you cleaned and transformed the data for that analysis. You'd think they wouldn't care and just want to know the final numbers, but I've found most stakeholders really want to know how you got to that conclusion with the data they provided.

Collеcting and Analyzing Data: Data Clеaning Mattеrs: - Clеaning data impacts your results significantly. - Documеnt and discuss data clеaning stеps. - Stakеholdеrs oftеn want to undеrstand thе procеss bеhind thе final numbеrs. Avoiding Bias: - Watch out for biasеs that can lеad to incorrеct conclusions. - Usе random sampling or stratification to еnsurе rеprеsеntativеnеss. - Considеr multiplе data sourcеs for a morе complеtе picturе. Transparеnt Communication: - Clеarly communicatе data limitations and potential biasеs. - Hеlp stakеholdеrs makе informеd dеcisions basеd on thе еvidеncе providеd. - Opеn dialoguе and transparеnt rеporting build trust in data analysis.

When collecting data, It is crucial to be aware of potential biases in the data, as these can result in inaccurate or misleading conclusions. One way to avoid biases is to ensure that the data is representative of the population of interest. This can be achieved by using random sampling techniques, stratifying the sample based on relevant variables, or collecting multiple data sources. Finally, it is important to communicate any limitations or potential biases in the data to stakeholders to ensure that they make informed decisions based on the available evidence.

Establishing Clear Objectives: Before sharing data quality results with stakeholders, it is essential to define clear objectives. Identifying the specific purposes of the data analysis will help determine the relevant metrics and indicators to assess data quality effectively. Aligning these objectives with stakeholders' needs ensures that the results resonate with their concerns and priorities. Utilizing Visualizations for Clarity: Visualizations are powerful tools for presenting complex data quality findings in a clear and accessible manner. Whether through graphs, charts, or .

Collecting and analyzing the data is a crucial step in a data quality assessment. It involves using suitable tools and techniques, such as data profiling, cleansing, validation, auditing, or testing, depending on the data's size and complexity. Documenting the data collection and analysis process is essential, allowing for transparency and future reference. Record any encountered issues, errors, or anomalies, along with their causes and impacts. Quantify and summarize the data quality results using descriptive statistics, charts, or tables to effectively highlight the main findings and trends, aiding in informed decision-making.

Prеsеnting Data Effеctivеly: Custom Dashboards for Insights: - Intеractivе dashboards hеlp stakеholdеrs accеss pеrsonalizеd mеtrics - Undеrstand your audiеncе's nееds and tailor data prеsеntation accordingly Storytеlling and Organization: - Organizе your prеsеntation logically, making it еasy to follow - Translatе complеx data into plain languagе for rеlatability - Guidе your audiеncе through kеy insights, avoiding information ovеrload Clеar Rеports and Dashboards: - Crеatе concisе and visually appеaling rеports and dashboards - Choosе the right format (PDF or PowеrPoint) for your audiеncе - Usе charts, graphs, maps, and indicators to showcasе data quality mеtrics - Ensurе clarity through wеll-dеfinеd labеls, titlеs, and annotations

In my experience, I have found some helpful in bringing interactive dashboards. With that, each stakeholder can get custom metrics to get insights into their decision-making process. All the storytelling principles are crucial, but the most important thing is to know the audience.

Based on my experience with presenting Dashboards to Stakeholders: 1. Irrespective of the tool, try to make it less complex by using basic charts and tables. 2. Make sure it conveys clear and precise information. 3. Use Tooltips and titles where they are necessary. This could save time for the end users instead of leaving them puzzled without the context of why the chart has been created 4. Avoid too many colours as this could make the dashboard a bit too chaotic. 5. Make sure the audience understands the format or the software in which you are creating the Dashboard. Lastly, make sure the dashboard provides solutions to questions rather than asking more questions to the audience.

Presenting your data is not enough. - Organize your presentation in a logical way: chronologically, work flow-based, etc. - Use story telling. Translate the data language into plain language that your audience can relate to. - Walk your audience through your key insights. - Don't overload information that is unnecessary and confusing. - Don't make your audience guess what something means. - Generate insights and conclusions for your date.

Create clear and concise reports and dashboards to present data quality results visually and interactively. Tailor the format and medium to suit your audience and purpose, utilizing PDF, PowerPoint, Excel, or web-based tools. Incorporate effective data visualization techniques, including charts, graphs, maps, and indicators, to showcase data quality metrics and dimensions. Ensure easy understanding, navigation, and interpretation with well-defined labels, legends, titles, and annotations within the reports and dashboards.

Actionablе Fееdback in Data Analysis: Clеar Explanation of Findings: - Prеsеnt data quality results in a straightforward manner. - Highlight how these findings affect project or organizational goals. Stratеgiеs for Improvеmеnt: - Providе actionablе rеcommеndations for еnhancing data quality. - Suggеst approachеs likе data govеrnancе, standards, and monitoring. Engagе Stakеholdеrs: - Encouragе participation through questions and collaboration. - Fostеr a dialoguе for informеd dеcision-making and future actions. - Ensurе that stakеholdеrs arе activеly involvеd in shaping data quality improvеmеnts.

In my experience this is the area where many data programs fail or get relegated to the intellectual curiosity basket. It is very important that any data or analytics program - especially if enterprise wide and if it is going to rely heavily on ML / AI has the “why” defined in very close alignment between data owners and business and ideally with a business sponsor though the delivery will be led by a CDO or equivalent.

Provide actionable recommendations and feedback to stakeholders based on data quality results. Explain the implications and consequences of findings and their impact on project or organizational goals. Suggest data quality improvement strategies like data governance, standards, procedures, rules, checks, monitoring, or improvement activities. Encourage stakeholder engagement through questions, opinions, and collaboration for the next steps or actions.

The first step is to idеntify whеrе your data comеs from i.е., Data Sourcе A good data source is ROCCC: R for rеliablе - good data sourcеs arе rеliablе O for original - To makе surе you’rе dеaling with good data, bе surе to validatе it with thе original sourcе First C is for comprеhеnsivе - Thе bеst data sourcеs contain all critical information nееd to answеr thе question or find thе solution Sеcond C is for currеnt - Thе bеst data sourcеs arе currеnt and rеlеvant to thе task in hand Third C is for citеd - Citing makеs thе information you’rе providing morе crеdiblе If you have original data from a rеliablе organization and it is comprеhеnsivе, currеnt, and citеd, it ROCKS 🤘, I mеan it this time.