Did you just get moved over from one data warehouse support group to another? Do you know nothing or very little about the data in your new data warehouse? Or are you new to data quality analysis and want to get started on some solid footing?
The following post by Sylvia Moestl Vasilik “9 things to do when you inherit a database” at SQLServerCentral.com is an excellent article for anyone jumping into a new database environment, regardless of the environment or vendor, or the type of database (relational or columnar), Sylvia’s 9 things to do can be applied anywhere.
Building on those “9 things,”if you are less technical and more into data quality analysis or into a data steward role, I recommend adding a 10th thing to do… begin and complete a data profile.
A solid data profile will provide you with a wealth of information and more. A solid data profile will provide you with some interesting insight into the data. Here are a few items that you should be able to capture with a good data profile project.
- You will gain an understanding of the completeness of the data, you’ll see what’s missing and you can begin to ask the questions to the business users why are we missing this component of the data set(s).
- How accurate is the data, does it meet the initial requirements or not. How often does a job fail because of bad data; have you lost customers, revenues or received fines due to bad data? You’ll discover soon enough how inaccurate data affects your organization.
- How timely is the data? Do you have real-time, near real-time or less timely data. Is your data arriving late, on time or not at all? How long is the data relevant for, this will be important for you, your users and maintaining the environment.
Just remember focus yourself first on the most important data, the highly used data, then you can spread out and tackle the rest of the data warehouse. Make sure you have senior management approval, and are able to prioritize the other 9 things to do along with this one.
Other items you can gather while running a data profile project can be identified from the following post, 5 Non-Quality Items to Consider in Data Profiling.
