When you process large amounts of data, it’s the quality thereof that has the greatest impact on your organisation. It is thus imperative to train yourself and your staff to care about data quality. Do this from the first step, and prevent adverse effects from poor information.
Get your data right from the beginning
Start your capturing processes on the right foot. Educate your teams to respect data capture best practices, and have built-in quality checks. Get people to enjoy the procedures by turning it into a rewarding experience.
There are many ways to capture information, such as the ones listed in this useful article. Select only the ones you can confidently apply and manage. This will prevent errors from creeping in, and help you avoid complicated amendment routines.
Be careful when automating
If you’re considering automated tools for handling the information you process, be wary. While they can and do speed things up, they also remove many positive aspects of human interaction as well as the negative ones. For example, having someone fill in a form means they are responsible for the accuracy of what they enter. They miss out, however, on interacting with your personnel. What’s more, this kind of capturing is usually bereft of quality checks. This can lead to a lot of frustration if left unattended in the long run.
Have a solid data strategy
It won’t matter if you have lots of small efforts if they aren’t acting in sync with one another. You should try to either source or create a strategy that enables proper control of your data quality. Furthermore, make sure that you share this strategy with your organisation, and repeat it as often as you need for it to sink in.
In addition to this, enforce a regular occurrence of proofing or cross-checking the quality. This may sound like a tedious task, though you can offset this by explaining the net gains and importance thereof.
Common errors
While by no means comprehensive, this list highlights some of the more prevalent gremlins found within typical corporate client-based data:
- Placing company details in personal details sections and vice-versa
- Incorrect use of company trading names versus registered company names
- Including addresses or deal details in name fields
- Creating multiple profiles for the same user or client
- Placing individual issue reference numbers in the user / client profile
- Entering data all in uppercase or lowercase
- Entering irrelevant data
Adept strives to keep data accurate, reliably safe and retrievable, through ongoing staff education and constant improvement in our own processes.