Data cleansing: A cheat sheet

Data cleansing is a process by which a computer program detects, records, and corrects inconsistencies and errors within a collection of data.

Data is at the foundation of many business projects and goals today, making data quality management one of the most important activities on data and IT teams. One of the earliest and most frequently repeated steps in the data management process is data cleansing. But what exactly is data cleansing and what does the process accomplish? Learn more in this comprehensive guide.

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What is data cleansing?

Data cleansing, also referred to as data scrubbing, is the process of removing duplicate, corrupted, incorrect, incomplete and incorrectly formatted data from within a dataset. The process of data cleansing involves identifying, removing, updating and changing data to fix it. The objective of data cleansing is to make reliable, consistent and accurate data available throughout the data lifecycle.

SEE: Electronic data disposal policy (TechRepublic Premium)

With the increasing complexity and abundance of data, data errors of all kinds are increasing across various business platforms and databases. The proliferation of data has made data cleansing an important component of data quality management.

Businesses that are able to maintain data quality can use the data to make informed and accurate decisions. Common issues with data include misplaced entries, missing values, ambiguous data, duplicate data and typographic errors.

Benefits of data cleansing

Data cleansing processes have moved from a “nice to have” to a “must have” for effective data-driven operations, especially as businesses growl increasingly reliant on data for decision-making. If data is not cleansed, it can lead to flawed business planning and missed opportunities, which can result in reduced revenue and increased costs. It can also compromise the ability of an organization to leverage their data analytics technologies.

With the sheer volume and variety of data available to organizations, data cleansing has become more important than ever. Not only does it support process efficiency and information accuracy, but it can also provide businesses with a competitive advantage over competitors.

A company that is able to meet customer needs faster than its competitors holds the advantage. Data cleansing tools help companies to identify ever-changing customer needs and keep up with emerging trends in the market.

Steps to performing data cleansing

Here is an overview of the data cleansing process framework. Keep in mind that these processes can vary depending on the type of data used by an organization and any specific data issue that is being analyzed.

Step 1: Remove irrelevant and duplicate data

The objective of this step is to remove unwanted observations from the dataset. This step includes several processes, including receiving data from multiple sources, scraping data and deduplication. Removing irrelevant and duplicate data will help you focus on data that fits into the specific issues and projects you’re working on.

Step 2: Fix formatting and structural errors

Fixing formatting and structural errors — such as typos — is an important step to complete in the data cleansing process. Such inconsistencies in data can lead to major problems and can be difficult to identify. However, using data cleansing tools can make this step easier and more efficient.

Step 3: Filter outliers

To maximize the performance of data, any data outliers must be removed. These outliers could be a result of importer data entry or data retrieval errors. This step also helps to establish the validity of the data.

Step 4: Address missing data

Missing data can’t be ignored, as many algorithms will not execute with null values. If you are not able to locate missing data, then you may need to rely on assumptions to repopulate the missing data. Keep in mind that you risk losing the integrity of data if your assumptions are not correct.

Step 5: Validate data

In this step, you will determine if your data makes sense and whether it follows the appropriate rules for its field. You must ensure the data conforms to the data quality standards and rules of your organization.

Step 6: Report results to appropriate stakeholders

The results of the data cleansing process should be saved and reported to relevant authorities in the company, which could be the IT department or certain business executives. The report should cover issues found and corrected by the data cleansing process.

One of the challenges of data cleansing is that it can be time-consuming, especially when pinpointing issues across disparate data systems. One of the best ways to make data cleansing more efficient is to use data cleansing tools.

There are a variety of data cleansing tools available in the market, including open source applications and commercial software. These tools include a variety of functions to help identify and fix data errors and missing information. Vendors, such as WinPure and DataLadder, offer specialized tools that focus solely on data cleansing tasks. And some data quality management tools, such as Datactics and Precisely, also offer helpful features for data cleansing.

The core features of data cleansing tools include data profiling, batch matching, data verification and data standardization. Some data cleansing tools also offer advanced data quality checks that monitor and report errors while processing data. There are also workflow automation features offered by some data cleansing tools that automate the profiling of incoming data, data validation and data loading.

Conclusion

Regardless of if you handle data quality management with a more manual or automated approach, it’s important to have several policies and frameworks in place to support the overall process. Whether it’s an electronic data disposal policy, a data governance framework or a simple checklist for data cleansing, documentation is key to a successful data management strategy.