It's springtime! For many of us, that means it's time to do our annual spring cleaning both at home and at the office. While scrubbing the windows and sorting through your extra inventory is important, you might want to add data cleaning to your to do list this year!
According to Tableau, data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. This helps ensure the insights you're looking at for your flower shop are as accurate as possible, which helps you determine not only your business performance, but also helps you make decisions like where to cut or focus costs. While it's not an overly complicated process, data cleaning can be tedious, so make sure you have plenty of time and patience to tackle it!
The first step in data cleaning is removing all of the duplicate or irrelevant data in your system. This would be something like having two of the same email address for a customer or having an old or incorrect home address for an employee. It could also be removing unnecessary analytics you're gathering on your customer base. Data cleaning applies to any form of data! A way to speed up this process is by investing in a data cleansing tool. A couple that we'd recommend include Cloudingo and Tableau Prep.
Once you've removed all the unnecessary data, you need to focus on fixing any errors. Typos, capitalization, incorrect word use, anything like that should be corrected before moving on. Otherwise your insights will still be skewed in the long run. This also just helps to streamline your data entry process in the future. For example, “N/A” and “Not Applicable” should be analyzed as the same category.
At this point, your data should be as accurate as possible, and you'll want to keep it that way! To do that, you need to follow a consistent data format with any new data that you may add in the future. So rather than entering a date a different way every time, you'd stick to formatting it the same way with each entry. Not only does this just streamline the data entry process, it also makes it easier to analyze that data with different systems.
Data cleaning is something you should try to do at least once a year, but if you collect a lot of data you may try doing it biannually or quarterly so it's less time consuming. We hope these steps have made the process a little easier for you to understand. Enjoy your spring cleaning!