Typical data quality issues include: Missing values Incorrect values Inconsistent formatting Inconsistent reporting standards Noise introduced into data A common response to data mangled by the technical process," That is not really a data quality issue. It is more of a technical issue" Showcase leadership commitment and cultural change to improve quality of data while keeping stakeholders in the loop Maintain an issue management process based on accountability to improve stakeholder's satisfaction Implement DACI framework for stakeholder management: Driver: ensures timely decision making Approver: makes final decisions Contributors: offer expert opinion Informed stakeholders: receive notifications of decisions

Quick action and clear communication are key to keeping stakeholders satisfied, here are the different ways to handle the situation - 1. Identify what’s wrong and focus on fixing the most urgent issues. 2. Need to involve everyone to know what’s happening and how you are addressing it. 3. Get the right people or tools to clean up the data quickly. 4. If the timeline has to change, explain why and how you will still deliver good results. 5. Need to put checks in place to catch data problems early next time.

The first step would be to communicate to the stakeholders on the issues and agreeing with them a timeline by which initial assesment on the root cause would be given. This should be followed by root cause analysis and identifying any possible automated/manual process to alleviate/stop the business impacts. Based on the outcome of the findings, the next step could be decided. Feedback to the stakeholder community on the findings and possible options of interim and strategic fixes. Get them onboard with the plan and then start executing it.

In my opinion when it comes to data quality issues ( from a data warehousing data lake perspective) the best thing you can do is open communication with your stakeholders. As consumers of data from upstream applications ( mostly source systems) you are not in complete control of the data you receive. It is best to make stakeholders aware of data quality issues as early as possible if possible clearly communicating its impact on business (this will get their attention) and come to a common plan of action to correct these issues. These changes to improve data quality might also involve change in business proceses and would not be only limited to changes in system or changes in your Data Warehouse. As a note You could implement data contracts.

Will plan the following steps: - Identify the sources of issues: Conduct a thorough analysis to determine which specific data is causing problems and at what stage they occur. - Seek to improve data processing procedures: Implement additional data quality control measures and automated checks to identify errors at early stages. - Corrective actions: Resolve existing issues and introduce measures to prevent their recurrence. - Stakeholder communication: Regularly inform stakeholders about the current status and the actions being taken to maintain transparency and trust. - Resource reinforcement: If necessary, will allocate additional resources to expedite data correction processes and adhere to the project schedule.

The first thing recommend is actually understand the impact of the issue on business . The business impact can vary based on use case to use case. Hundred percent accuracy may not be necessary in some usecases where the information can be used for directional purposes. So engage the stakeholders with clarity on the issue what percentage of accuracy can be gauranteed if indeed the project needs to be delayed or can we take incremental approach to dpeloy fixes after going live.

First point is to find out what the data quality issues are and root cause before engaging the stakeholders. The more informed you are the better you can manage your stakeholders. Key is communication. Open and honest communication!

My approach would be - 1) Analyzing the impact and root causing the issue is my immediate first step. 2) If possible, identify short term or temporary workarounds to keep the timelines in check, while parallelly fixing the underlying quality issues. 3) Align with the business and keep the stakeholders updated about the progress. Transparency is always the key! 4) Finally, commit and implement robust quality controls, strict compliance and governance policies to prevent future risks.

1. Triage data quality issues 2. Honest communication with stakeholders 3. Develop a mitigation plan 4. Build trust 5. By leveraging data quality framework and methodology