If you ask anyone who deals with information management, they’ll tell you right away: their #1 challenge is making sure that their data is clean. How is that their biggest challenge? Because poor data quality doesn’t just equal lots of work cleaning it all up. It also means that your inputs won’t generate meaningful outputs. In other words, all the hard work won’t be paying up if it isn’t done properly to begin with.
While this is a challenge our clients face on a daily basis, when they are equipped with the right tools and processes, it becomes a lot easier to make sense of all this information. Here are some tips our team uses (and that you should also put into practice) in order to ensure there is no mess in our data.
First things first: avoiding garbage in garbage out
When collaborating as a team, whether it’s a 3 person show or a group of 200 data entry clerks, information can be entered in the application in many different ways, by numerous colleagues. Add to that the various channels to import information, like manually capturing data in the application or with an Excel import, and you’ve got the perfect mix for data inconsistencies. But wait! This doesn’t mean that it’s what is ought to happen.
The key to success is quite simple: make sure that data going into the Boréalis application is accurate and clean to begin with, and you’ll be ensured that outputs are of quality. If you enter data that hasn’t been validated to begin with, the application won’t magically fix that for you. Well not just yet anyways 😉 Data management tools have an enormous potential, but you can’t expect them to take you to the moon if you didn’t punch in the right coordinates.
Streamline the efforts of your data entry team
Last month, I shared super tips and tricks to save time with our Excel import tool. While this is the most efficient way to upload large amounts of information, it’s very important to make sure the data is accurate before doing the import.
Do you want to make using these templates even more simple for your team? It’s easy: simply remove all the unnecessary columns. To do so, simply hide or event delete all the unwanted columns, while making sure all the required fields are still there. Secret hack: our team uses color labels for each column, therefore just a peek at the doc and we know whether information in a given column is mandatory, nice to have or not necessary.
Important note: while Excel imports are the best way to upload new records in the application, this method doesn’t work for existing records. If a record already exists, you won’t be able to modify its content with an import and the best way to edit its content is directly from it.
Dealing with a lot of data can be a tedious task to ensure a high standard of quality. It can take an tremendous amounts of time to validate every record, even when using the Excel templates. But did you know that Boréalis offers a convenient tool to manage quality from the application once it’s imported?
Boréalis Analytics to the rescue
We all know that Boréalis Analytics is a great reporting tool for your stakeholder engagement, environmental monitoring, public affairs, land access, and community investment programs. But you should know it’s also a fine technology that can help your team maximize its efforts. Among other things, Boréalis Analytics can help you:
- Keep track of users’ performance – Why not add a little friendly competition to ensure data quality is consistent? Make internal contests for team members with the best track record.
- Clean up your data – Use widgets to compare entries in the application. Use this comparison to set data standards.
- Building custom reports – Choose which data you want to track closely, and what your team should focus on.
- Quickly remove duplicates for individuals and organisations with the quick filters – While you can already manage this from any list, push quality management to the next level by using Analytics to verify records in bulk.
As you can see, there are several ways you can use Boréalis Analytics to assist your efforts to maintain a clean, consistent and up to date data base. I really hope this article has provided great tips to help your team. Should you require assistance regarding data quality management or anything else, you know we’re always happy to help!