How can you improve communication of data quality issues?

When you identify a data quality issue, you need to communicate it. You should do some research to determine how significant it is so that can be communicated as well, but people need to know as soon as possible. Work is constantly being done using data, so if people need to know they're building reports or analytics off bad data, they need to know before they get too far into their work. Identifying the root cause is critical, but sometimes requires significant work before you know what the root cause is, much less fix it. The longer you delay in communicating the issue, the bigger the issue becomes and more time will be needed to do rework once the data quality issue has been resolved.

Once data quality issue is identified, a possible root cause analysis should be done before sharing with Data Stewards and Custodians. There can be more than one causes for the data quality issue occurring. I use Fish-bone diagrams to represent the root cause analysis. Set of examples can be prepared along with analysis, to share with stewards to move forward.

Before we identify the root cause , we need to understand and comprehend the existing issue, to form precise problem statement.First step is to know the problem statement and its contributing factors. Based on this analysis, we try to identify root causes through approches such as : Pareto Charts,5 Whys Analysis,Scatter Plot Diagrams, Fishbone Diagrams,Failure Mode and Effects Analysis (FMEA).

In my experience as a Data Governance Consultant, the first and most crucial step in effectively communicating data quality issues is to identify their root cause. This involves: ? Conducting a thorough analysis to pinpoint where and why data quality issues are occurring. ? Collaborating with data owners and users to understand the operational impacts. ? Leveraging tools and techniques for data profiling and anomaly detection. Remember, understanding the 'why' behind data quality issues is key to effectively communicating and resolving them. It's not just about treating symptoms; it's about diagnosing and addressing the underlying problems.

Typically a workflow mechanism is used to communicate data quality issues to the Data Community. It will be tedious in emails to follow up with people and review in future. Workflow tool can be configured to send right set of people; Workflow tool can send automatic email notification and helps share larger files. We can always use other channels for clarity purposes.

In my role as a Data Governance Consultant, I've learned that selecting the appropriate communication channel is critical when addressing data quality issues. This involves: ? Understanding the audience and selecting a channel that suits their preferences and accessibility. ? Considering the complexity of the issue to decide whether a direct meeting, email, or detailed report is most effective. ? Utilizing data visualization tools when necessary to convey complex information more clearly. The right channel ensures your message about data quality is not just delivered, but also understood and acted upon. It's about adapting your communication strategy to your audience's needs.

The communication on the data quality issue to the data community should be precise, flawless and consistent pattern that Stewards and Custodians should be able to understand; The language should be clear enough to move forward to fix. Any unclear message will lead to different route which may cause more issues to occur.

Using clear and consistent language when communicating about data quality issues involves: ? Avoiding technical jargon when addressing non-technical stakeholders, ensuring accessibility and understanding. ? Maintaining consistency in terms and definitions to avoid confusion. ? Articulating the issues and their implications in a straightforward manner to foster a shared understanding. Clear and consistent communication is vital for engaging stakeholders and driving collaborative efforts to resolve data quality issues. It's not just about what you say, but how you say it.

Excellent points on showcasing data quality issues with evidence! To enhance this, consider tying the evidence directly to business impacts, such as revenue loss or operational inefficiencies. Incorporating brief case studies or specific examples can also vividly illustrate the real-world consequences, making the case more relatable and urgent for stakeholders.

An evidence of a report or dashboard which shows the drop in Data Quality can be shared with the Stewards and Custodians. The screen shots of a dashboard can also help understand the quantity. The extracted list of records on which the Data Quality issues are found, can be helpful for fixing the issues at the earliest. The extracted list can also be uploaded to the Workflow tool.

Providing evidence and examples when discussing data quality issues is vital This approach includes: ? Using real data samples to illustrate specific issues, making the problem more tangible and understandable. ? Sharing case studies or past incidents where similar issues were successfully resolved. ? Presenting statistics and metrics to quantify the impact of data quality problems. By backing your communication with concrete examples and evidence, you not only clarify the issue but also emphasize its significance. This approach fosters a deeper understanding and drives informed decision-making.

Data Quality Practitioners can recommend the actions of what needs to be done to fix the issues and can also recommend solution for the issue not to occur again. The very purpose of the Data Quality program is to understand the issues and Find ways to prevent them happening again. Though the Custodians, Stewards, and Owners of the line of business have a course of action, Data Quality professionals can recommend actions and solutions.

In my experience as a Data Governance Consultant, I've found that the follow-up and updating process is crucial in managing data quality issues. This involves: ? Regularly checking in on the progress of resolutions and updates to data quality problems. ? Communicating any changes or improvements to all relevant stakeholders. ? Maintaining an open line of communication for feedback and further questions. This step is essential to ensure that the data quality issues are not only addressed but also continually managed. It's about building trust and demonstrating commitment to ongoing data quality improvement.

Whenever a data quality issue occurs, the impacted downstream data assets should be identified and the users of the affected dataset should be informed about the incident. They should expect a delay in the delivery of incremental data for the affected dataset. After this, the on-call data engineer can take the help of data lineage to check the cause of the issue and if it can be fixed by them or if they need to check with the relevant data source team to get the issue fixed. If the affected dataset is critical then business stakeholders should be kept in loop and informed as per the SLA. The engineers can share some bussiness context instead of technical jargon with stakeholders so that they can better understand the problem.

To enhance this process: Establish Clear Protocols: Define clear protocols for reporting and addressing data quality issues. Regular Training: Invest in ongoing training programs to keep your team updated on data quality standards and best practices. Centralized Documentation: Create a centralized repository for documenting data quality standards, common issues, and their resolutions. Use Visualization Tools: present complex quality issues in a more understandable format. Establish a Feedback Loop: Foster a culture of open communication by establishing a feedback loop Regular Audits: Conduct regular audits to proactively identify and address potential data quality issues.

The first thing one has to do is mitigate the data quality issue, and then do the diagnostic analysis of the issue. While mitigating you have time to understand the issue in a well-rounded way in terms of the upstream and downstream impact of the data being not available, this helps to inform the different teams involved and prioritize the same. How can we make this less impactful is the key with data on hand.