Time for a Data Cleanse
As we reach the end of 2021, you might be hearing the term, “data cleansing,” a lot more in the business industry. But, what is data cleansing? Why is it important? How do you do it?
These three questions will be addressed in this post, so stick around, and we’ll help your firm get to where it needs to be for the new year!
First off, data cleansing, also known as data cleaning, “is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.”
In other words, it’s updating your data records and making sure everything is efficient before going into the new year and adding more data.
Cleaning your data every year or even every few months is extremely important because having poor or inaccurate data will lead to false conclusions, analyses, tests, etc. This will hinder your firm’s ability to draw from data, which is why you have data in the first place.
Additionally, there’s a popular proposition in the data world: the 80/20 rule. This rule states that 80% of a data analysts’ time is spent cleaning data and 20% of their time is actually performing analyses.
Whether or not this rule holds true, a proper data analyst needs clean data in order to draw valid conclusions and consult businesses in future campaigns.
So, now that we have established the significance of a data cleanse, let’s get into five helpful steps in actually doing one:
- Remove irrelevant data. Do you have consumer information that is no longer needed for a marketing campaign you ran last month? If you don’t need a piece of data, remove it. This could be items like addresses, a consumer who’s been inactive for years, or anything else.
- Treat missing values or blank spaces. Is there information floating around that isn’t connected to a user id? Either find out who this information belongs to or scrap it. You don’t want blank values to throw off your algorithms.
- Remove duplicates. Do you have two of the same database? Sometimes, this can unnecessarily take up the space that you need for other pieces of data. If this is the case, merge your duplicate data to make it easier to find.
- Unify data types. If a column is supposed to be all numbers, make sure its values are all numbers. The same goes for categories, dummy variables, and other variables you might have.
- Spellcheck. Similar to unifying data types, make sure what you enter in as values has no typos to eliminate human error.
There you have it! Make sure to follow these five steps to clean up your firm’s data in time for the new year.