Data Quality Issues – Who is Responsible for Resolving Them?

One of the first processes, I believe that you should introduce in your Data Governance initiative is a Data Quality Issue Resolution process. In fact a recent blog covered what you should include in a Data Quality Issue Log to help you get started with collating and resolving issues.

But the log itself is only part of the answer and I have been asked on numerous occasions to clarify what the Data Governance (or Data Quality) Team are responsible for doing when managing and resolving data quality issues.  This often gets asked by newly formed teams.

There seems to be a lot of confusion around who does what and I have often come across the expectation that the Data Governance Team will do or solve everything.  There are times when I truly wish that I had a magic wand and could simply fix all the data problems, but sadly that isn’t the case.  Over time, of course your Data Governance Team will develop knowledge and expertise about the data your organisation creates and uses, but they are not responsible for deciding what the remedial actions should be and especially not for undertaking any manual data cleansing that may be required.

However, I am not saying that they have no part to play in the process.  The best way to understand what the Data Governance Team are responsible for is to look at a high level simple data quality issue resolution process:


It will usually be a Data Consumer (business user of that data) who spots an issue and will be the ones to notify the Data Governance Team.

The Data Governance Team will then log the issue on the Data Quality Issue log and identify the data owner(s) of the data concerned.

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The Data Governance Team notifies the Data Owner of the issue, who will advise whether or not they are the correct owner of the issue.

In addition, the Data Governance Team reports the current status of all open material data quality issues to the Data Governance Committee (usually as part of their regular agenda).

The Data Governance Committee reviews the open material data quality issues and prioritizes/directs on the remedial activities if needed.


The business user who notified the issue, the Data Governance Team, and the Data Owner(s) assess and agree about the impact of the issue.  If the issue is agreed to have a material impact its resolution will be prioritised.

The Data Governance Team works with the Data Owner(s) to identify the cause of the issue.

The Data Owner(s) consider possible remedial actions to rectify the issue.


The Data Owner(s) proposes an approach to resolve the issue and prevent it from re-occurring.

The business user who raised the issue agrees whether the proposed action plan is appropriate (the Data Governance Team can facilitate discussions between the parties if needed).

The Data Governance Team updates the Data Quality Issue Log with the agreed actions and target dates.

The Data Owner(s) plans how and when the remedial activities will take place.


The Data Owner(s) and their team(s) undertake the agreed remedial actions (N.B. this may need the support of IT).

The Data Governance Team monitors progress on remedial actions against agreed target dates and reports on progress to both the impacted business user(s) and the Data Governance Committee.

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The impacted business user(s) advise if timescales for resolution are not appropriate.

The Data Governance Committee then reviews progress and prioritizes/directs on the remedial activities if needed.

Of course, in practice, solving data quality issues takes more than the four steps listed above, but the additional steps will be sub-sets of the stages discussed above.  In addition, keeping it simple like this will help your stakeholders quickly understand who is responsible for what.

As with all things Data Governance, communication is key. I would recommend creating a simple high-level diagram of your data quality issue resolution process and using that in your communications to help people not only understand the process but also everyone’s role in it.

The Data Governance Team’s role in the data quality issue resolution process can be summarized as followed:

  • Identifying which Data Owner is responsible for the data which needs fixing and liaising with them
  • Maintaining the Data Quality Issue Log
  • Monitoring and reporting on open issues and associated action plans

I hope that has clarified the situation for you and remember you can find out more about what to include in a Data Quality Issue log here or download a free template for an issue log here.

Setting up the Data Quality Issue Process is just one of many things you need to do when starting a Data Governance Initiative – you can get a summary of all the things you need to consider by downloading my free Data Governance Checklist here.

Nicola Askham

I am an independent data management consultant. My experience in coaching both regulatory and non-regulatory organisations to design and implement full data governance frameworks, is unique within the Data Governance field. The coaching approach enables organisations to self manage the process beyond initial implementation, leaving them with a sustainable data governance framework. My coaching and Data Governance training workshops ensures that your data governance framework is embedded as an integral part of your business as usual policy. The benefit for you is that once the framework is in place your organisation will be confident, competent and compliant. I have worked in Data Management for twelve years, initially for a leading UK Bank, before becoming a consultant at the beginning of 2009. Most recently I have spent most of my time delivering data governance for Solvency II, but I also run Data Governance Training courses and coach organisations to implement data governance themselves. I am a Director and Committee Member of DAMA UK (I lead Phase 1 of the Data Quality Dimensions Working Group), on the Expert Panel of and regularly write and present internationally on data governance best practice. I regularly present both at home and internationally. I presented at the MDM Summit, the Business Analysis Conference and the Collibra European User Group in London and Enterprise Data World, Enterprise Dataversity and the IDQ Summit in the US in 2014. I presented at the Experian Data Quality Insurance Summit in March, deliverd tutorials and presentations at the Data Governance Conference in London in May, at DGIQ in San Diego in June and presenting at the Stibo MDM event in Berlin in October. I will be presenting next at the IRMUK EDBI Conference in London in November. Specialties: Data Governance, Master Data Management, Data Quality

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