What Is Data Cleaning and How Could It Benefit You?

What Is Data Cleaning, and How Can It Optimize Your Data?

Data cleaning, also known as data cleansing or data scrubbing, is a process where information is organized and optimized in ways that support an organization’s objectives, often with the goal of creating consistency in the data as presented.

This can take many forms. For example, data can naturally fall out of date over time, because contact information changes, because people change jobs or because people move. During the early days of the pandemic, for example, one common issue that mail-focused organizations faced was that many customers, clients and employees no longer worked in an office, meaning that mail sent to those offices, such as bills or magazines, were going unseen.

But other issues may arise as well. A person who fills out a form multiple times may create duplicative data, or users may make mistakes such as misspelling things. There can be differences in organization and measurement of data between countries.

“For example, one set of data could be measured in pounds, and the other one may be metric,” Metten says.

Another common need for data cleaning is to optimize data for a specific use case. For example, data may come with additional fields that are unrelated to or unnecessary for data analysis. Ultimately, the goal with data cleaning should be for the data to be focused, up to date and accurate.

There are significant business benefits to getting data cleaning right. The credit reporting firm Experian recently noted that 85 percent of businesses say poor-quality data can have a negative effect on organizational processes, while 89 percent say improving data-quality best practices improves overall business agility.

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How to Begin the Process of Cleaning Data

Data cleaning can be done in numerous ways, including through both automated and fully manual processes.

The process may require going through a database and removing or changing records so they follow a consistent format. Traditionally this process has been manual, but some shortcuts can be taken, such as the use of regular expressions, a semi-programmatic technique for searching for specific patterns in a code or database. However, organizations that employ manual processes risk introducing human error into their data cleaning efforts.

Some vendors offer automated tools for data cleaning. For example, the Tableau Prep data-preparation tool can ease the process of cleaning and analyzing data across sources, whether the data is stored locally or in a cloud (or even multicloud) environment. The tool can identify data inconsistencies and gaps, which can help in the process of narrowing down a data set to what’s needed to tell a broader story — a process Metten compares to breaking down an iceberg.

“The bigger part of this exercise is not to build the visualization. In the end, the bigger part of this exercise is to actually arrive at the data and the understanding of the data and what you want to do with it,” he says. “And then, the last part of it is to actually build on top of what you have now claimed as a data source to build your visualizations.”

This is a complex process even for large enterprises. For example, Microsoft has an entire team dedicated to researching the issue, while Amazon Web Services released a technology a few years ago called AWS Glue Databrew, which aims to address the complexities of data preparation for data analysts and researchers.

Can You Prevent ‘Dirty Data’?

While data cleaning is an effective solution for repairing data issues that may emerge, the best way to deal with dirty data is to avoid it in the first place as it is collected and organized.

Salesforce’s Metten suggests building data inputs in a structured way whenever possible, rather than relying on unstructured inputs.

“Giving users a choice of radio buttons and drop-downs and select boxes is always better than giving them free text,” Metten says.

The more options users have to enter data, the more chance that data cleaning may be needed. For example, Metten notes that many address forms have two lines for addresses, which can create confusion among users about where to put an address.

“How you collect data has an immediate impact on how you process data,” he says.

Naturally, data is going to fall out of date over time. To avoid this, Forbes recommends that businesses follow best practices for data hygiene. These include setting rules for data standardization, breaking organizational silos that can prevent data from remaining current, and conducting audits to determine whether data should be maintained or eliminated.

“Companies have a tendency to want to amass as much information as possible on their customers. More is not better,” writes Forbes contributor Falon Fatemi. “If input fields aren’t relevant or impactful for converting leads or advancing sales conversations, they should be deprecated.”

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Data Cleaning vs. Data Transformation

While data cleaning is an important process to help build a strong set of data, it differs significantly from data transformation, which refers to the concept of changing data from one format to another — a common practice  for analyzing data using different models.

While an organization may need to clean data to prepare for data transformation, both of these separate processes may be valuable for data analysis.

“The data transformation at some point can create the right quantity of data,” Metten says. “Your dashboard works because you’ve created the right quality of data with the right information.”

Even when data has been cleaned and transformed to meet an organization’s needs, organizations shouldn’t stop. Ensuring high-quality, clean data is not a one-time process, and organizations must strategize their approach to maintain data quality.

Data changes constantly, and getting the most out of it while ensuring the right processes are in place to maximize the results is paramount. Collaborating with an expert partner such as CDW can help an organization figure out how to maintain data quality and avoid headaches down the line.