One of the first steps on the path to data quality enlightenment is to audit the quality of your data. It’s useful to know the current state of play, to work out which data sources need your attention the most. There are a few different approaches you can take to auditing:
Manual audit
You will have staff who work closely with your data, and they will have a pretty good idea of where poor data quality might impact on their job performance. This might be customer service staff who, in looking up customer records, have a good feel for the level of account duplication. It could be marketing staff, who know that their response rates take a dive if they include email contacts from a certain source in their campaigns.
The point is that your staff already have a wealth of knowledge about how poor data quality impacts on their jobs. By working backwards from there, you can start to uncover some of the underlying data quality problems.
However, no single person or group of people can possibly have an in-depth understanding of all of your data sources and the quality of each one. This is where an automated auditing process can reap rewards.
Automated audit
An automated data quality audit has a number of advantages that will help you to understand the broader picture:
- An automated audit can cover a lot of data sources at once, highlighting quality issues across multiple data sets and potentially millions of records. Many of our clients are already seeing the benefits of this type of large-scale audit.
- Automation can also apply consistent checks to every source, resulting in a set of metrics or KPIs that you can use to get a good understanding of your overall data quality score. We use traffic light indicators and a system that takes account of priority fields within each source to make that score as clear and meaningful as possible.
- If you want the detail as well as the overview, automated auditing and reporting can allow you to drill right down to see problem values in individual fields. Which emails are invalid and which names are junk entries are just two of the many types of data error we report on.
- Automated auditing can also be repeated, so that you can track your data quality profile over time. We repeat the audit each month and provide twelve months of past data as standard in MasterVision DQ.
It’s important not to discount the manual approach to uncovering data quality issues, but to get a truly comprehensive picture of the quality of your data, an automated audit like the one offered by MasterVision DQ is the way to go. You can find out more about MasterVision DQ by taking the tour.
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