data quality top-level management involvement

You need top-level management involvement to improve the quality of your data. Fact! It's not enough for the IT manager to support your initiatives in improving the quality of your data. Thinking otherwise, won't get your data quality efforts too far.

Even if you're not part of IT, your department's manager understands the need to have you cleanse your data so you can provide them with more accurate reporting and to support their decision making process. If you're thinking "Let's not bother top-level management and involve them with our data quality endeavors", then that's the wrong way of thinking and here's why.

I see this very often: you put in the effort and just cleanse the data that you need for your processes, for your reports, for your software…maybe you do more than only being reactive - because let's be honest, if all you do is cleansing it - that's a reactive action that you will keep on doing it. So let's say that you're not just being reactive and not only cleansing your data, but you're actually putting the effort to tackle the root cause of the data quality issues. Without top-level management involvement this can go south quickly.

Root cause analysis

Here are some techniques for root cause analysis: barrier analysis, fault tree analysis, fishbone diagram, and the 5 whys

First of all, a lot of data quality issues are only solved by having a cross departmental view, by understanding the issues and cause of the issues from an organization perspective, not just a department perspective. Otherwise whatever fixes you will employ, might actually create bigger issues for other departments or have a negative impact to the entire organization. 

And if you agree or if you don't, I would like to give you an example:

This one bank had multiple organization entities in one of their databases and their mortgage loans and lines of credit unit was depending on this data in order to record the employers of their customers. One of the reports they had to put together was one where management could see how many customers they had from a specific employer. For example, how many were customers that were Apple employees. This would help the bank provide Apple a good offer for personal banking that they could provide to their employees as another perk for working at Apple.

(By the way, I'm extrapolating from a real example into a fictitious example in order to hide the identity of the organization that I'm referring to as that's not important, but the lesson is).

Back to the story. The mortgage loans and lines of credit unit noticed that there were multiple entries for Apple, one for each store, plus different headquarters and office buildings across the world. They decided to standardize it and combine all of them under the one record, because that's what they were manually doing before anyways… an individual was collating all the organizations that had Apple in their name. Plus sometimes some would drop through if they were recently added or had spelling errors and so on. So in order to improve the quality of their data they standardized it under Apple Computer Inc. as that's what it was called at the time.

They did this at department level, without top-level management support. This created a lot of issues with billing, with marketing, and so many processes from other departments that depended on having in their database, different organization entities for the specific office location of these Apple stores and offices.

Data quality issues need to be addressed at enterprise level and requires top-level management involvement

Top-level management involvement is a must in order to provide a more organization-wide view of data quality initiatives. That's why a data quality management program needs to happen at the organization level, not only at department level.

Let's also not forget that data quality needs ongoing support and resources and you can't achieve that without top-level management buy-in and involvement. It's not enough just to have the support of your department. It's a start if that's all you have, but don't stop there. Not needing top-level management involvement for your data quality efforts is a myth.

Do you benefit from top-level management support? Please let me know in the comments below if you do or if you don't. 

  • Lisa Sikkema says:

    Great article George. Collaboration and fixing upstream can will save many people time and result in improved decisions.

  • {"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}

    About the author 

    George Firican

    George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

    You may also like:

    How to Become a Data Science Freelancer

    George Firican


    Data Governance in 2024

    Data Governance in 2024
    5 Steps to Achieve Proactive Data Observability – Explained Over Beers