The Basics of Data Cleansing

Data is a $60 billion industry in the United States, and that number has quadrupled in the last decade. 90% of businesses now report that data plays at least some role in their business; nearly two thirds of those describe data as “very important in daily operations.”

For most businesses it’s no longer a question of whether or not you're using data, or even how much of it you’re collecting. Instead, it’s about ensuring that the data you're collecting is quality enough to use for important decisions.

Studies suggest that bad data costs businesses upwards of $3.1 trillion dollars globally each year. In fact, in most cases, bad data is worse than no data at all. It can take years to recover from decisions made on bad data.

The good news is that the process of turning bad data into good data is simpler than ever. Data cleansing allows you to catch and fix the errors in data collection, and it can help your business make decisions, streamline processes, and retain customers confidently. 

What is Data Cleansing?

Data cleansing is the process of fixing errors and irregularities in your data so it can be used for its intended purpose.

The most common issues in a set of data include things like misspellings, missing or duplicate entries, and formatting errors. While these might seem like relatively small hurdles compared to the task of collecting the data itself, these small quips can throw off an entire data set.

The goal of data cleansing is to correct these errors while keeping as much of the original data set intact as possible. This can be performed manually, or with a data cleansing program. 

How Much Time is Wasted Cleaning Data?

While the job title “data analyst” suggests that the job is to analyze the data, the reality is that for most data analysts, janitorial work constitutes a bulk of their time. 

60% of data analysts say that data cleansing is their most time consuming task. Additionally, 57% report it as the aspect of their job that they least enjoy.

By outsourcing or automating these tasks, analysts can spend more time focusing on collecting, analyzing, and strategizing with their data, instead of spending hours trudging through the grunt work. 

Pain Points: If One Thing is Off, Everything is Off 

Data is a lot like tipping dominos – if just one domino is misaligned, it can ruin the entire process. 

For example, if a digit of a customer's phone number is being formatted as a character instead of a number, or their email address was entered as a “.cmo” instead of a “.com”, they probably aren’t receiving communications from you.

This results in a lot of wasted money that could have been saved upfront with a proper data cleansing process.

 

What You Need to Know Before You Clean Your Data

Before you clean your data, it’s important to have an understanding of what exactly you want to achieve.

Here are common challenges that data cleansing seeks to improve:

1. Validity

Data validity is determined by how well the information answers the question. For example, if the question is what state you are from, the answer “CA” would be very valid, “Cali” would be less valid, and something random, like your email address, would be not valid at all.

While many modern data collection methods include restraints that help minimize invalid answers, errors can still occur. 

Data cleansing can ensure that all of the data for a specific response is valid by correcting some responses and deleting others. 

2. Consistency

Duplicate data is an unfortunate reality of data collection. 

The problem is worse, however, when that data contradicts itself. Having multiple emails or phone numbers on file can lead to businesses wasting resources. 

Data cleansing can help tighten up inconsistent data by figuring out which data point is correct. This might mean analyzing which email account a user is most active on, or which phone number was input more recently. 

Once you have an idea which of these metrics you’ll need to improve within your set, you can begin to create a cleansing process that will optimize your data. 

3. Perfecting the Process

Perfecting your data is an ongoing process.

It’s important to understand what has worked for you in the past. So, before you begin the process of data cleansing, it is crucial to communicate with others within your organization who work with that data. Their insights can help you understand what areas need to be specifically targeted, and ways that your current process might be coming up short. 


How Toric Can Help

A decision is only as good as the data that informs it. For businesses, the cleaner the data the clearer the direction.

As we continue to understand the importance of clean data, many companies are shifting more resources into data cleansing. 

Unfortunately, cleaning data manually is time consuming and can be prone to errors. This is where Toric comes in. Toric is an all-in-one, no-code data solution that includes data cleansing capabilities.

Toric’s data cleansing service finds inconsistencies and autocorrects them for you. For example, Toric can identify that "CA," "Ca," "Cali," and "California" are all the same value, and it can rename them to all read "CA." This allows data analysts to get back to doing what they do best – analyzing data.

Toric’s system also provides time-saving preventive features. It shows exactly what happened to the data, so the processes can be fixed immediately. Toric will even send you warnings when there are potential issues with the dataflow itself – allowing you to make changes early and avoid even having to clean bad data in the first place. 

Plus, this is all done in a no-code interface, which means that it can be used by anyone. With Toric, you don’t need to be an expert in SQL (or an equivalent) to have clean data.

To learn more about how Toric is helping businesses use their data more effectively, subscribe to our newsletter. It’s the best way to stay up-to-date on the latest news, resources, and articles about data analytics and no code. 


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