What is Data Cleansing? Guide to Data Cleansing Tools, Services and Strategy

The amount of data available to us has continued to increase, and so have opportunities for error. As a result, we rely on data cleansing to optimize our data management processes. Data cleansing boosts the integrity and relevance of our data by reducing inconsistencies, eliminating errors, and allowing companies to make accurate, informed decisions. In this article, you'll learn the basics of data cleansing, why it's critical for your business, and how to go about implementing a data cleansing process.

What is data cleansing?

Data cleansing is the process of identifying and resolving corrupt, inaccurate, or irrelevant data. This critical stage of data processing — also referred to as data scrubbing or data cleaning — boosts the consistency, reliability, and value of your company’s data.

Common inaccuracies in data include missing values, misplaced entries, and typographical errors. In some cases, data cleansing requires certain values to be filled in or corrected, while in other instances, the values will need to be removed altogether.

Data that contains these kinds of errors and inconsistencies is called “dirty data,” and its consequences are real. It’s estimated that only 3% of data meets basic quality standards and that dirty data costs companies in the U.S. over $3 trillion each year.

The power of clean data

A decision is only as good as the data that informs it. And with massive amounts of data streaming in from multiple sources, a data cleansing tool is more important than ever for ensuring accuracy of information, process efficiency, and driving your company’s competitive edge. Some of the primary benefits of data scrubbing include:

Improved Decision Making — Data quality is critical because it directly affects your company’s ability to make sound decisions and calculate effective strategies. No company can afford wasting time and energy correcting errors brought about by dirty data.

Consider a business that relies on customer-generated data to develop each new generation of its online and mobile ordering systems, such as AnyWare from Domino’s Pizza. Without a data cleansing programme, changes and revisions to the app may not be based on precise or accurate information. As a result, the new version of the app may miss its target and fail to meet customer needs or expectations.

Boosted Efficiency — Utilising clean data isn’t just beneficial for your company’s external needs — it can also improve in-house efficiency and productivity. When information is cleaned properly, it reveals valuable insights into internal needs and processes. For example, a company may use data to track employee productivity or job satisfaction in an effort to predict and reduce turnover. Cleansing data from performance reviews, employee feedback, and other related HR documents may help quickly identify employees who are at a higher risk of attrition.

Competitive Edge — The better a company meets its customers needs, the faster it will rise above its competitors. A data cleansing tool helps provide reliable, complete insights so that you can identify evolving customer needs and stay on top of emerging trends. Data cleansing can produce faster response rates, generate quality leads, and improve the customer experience.

Data cleansing: step-by-step

A data cleansing tool can automate most aspects of a company’s overall data cleansing programme, but a tool is only one part of an ongoing, long-term solution to data cleaning. Here’s an overview of the steps you’ll need to take to make sure your data is clean and usable:

Step 1 — Identify the Critical Data Fields
Companies have access to more data now than ever before, but not all of it is equally useful. The first step in data cleansing is to determine which types of data or data fields are critical for a given project or process.

Step 2 — Collect the Data
After the relevant data fields are identified, the data they contain is collected, sorted, and organised.

Step 3 — Discard Duplicate Values
After the data has been collected, the process of resolving inaccuracies begins. Duplicate values are identified and removed.

Step 4 — Resolve Empty Values
Data cleansing tools search each field for missing values, and can then fill in those values to create a complete data set and avoid gaps in information.

Step 5 — Standardise the Cleansing Process
For a data cleansing process to be effective, it should be standardised so that it can be easily replicated for consistency. In order to do so, it’s important to determine which data is used most often, when it will be needed, and who will be responsible for maintaining the process. Finally, you’ll need to determine how often you’ll need to scrub your data. Daily? Weekly? Monthly?

Step 6 — Review, Adapt, Repeat
Set time aside each week or month to review the data cleansing process. What has been working well? Where is there room for improvement? Are there any obvious glitches or bugs that seem to be occurring? Include members of different teams who are affected by data cleansing in the conversation for a well-rounded account of your company’s process.

Data quality is now increasingly becoming a company-wide strategic priority involving professionals from every corner of the business, and a robust data cleansing programme is one part of that larger effort. To succeed, working like a sports team is a way to illustrate the key ingredients needed to overcome any data quality challenge. As in team sports, you will hardly succeed if you just train and practise alone. You have to practise together to make the team successful.

Clean data means clear direction

Good decisions, bad decisions: they all hinge upon the quality of the data that informs them. Errors cost money, take time to correct, and can damage your brand. Data cleansing is one way to make sure that you can trust the data that your business relies on. And when you trust your data, you can make decisions with accuracy, precision, and confidence.

Get started with clean data

Manual data cleansing is both time-intensive and prone to errors, so many companies have made the move to automate and standardise their process. Using a data cleaning tool is a simple way to improve the efficiency and consistency of your company’s data cleansing strategy and boost your ability to make informed decisions.

Data Quality from Talend helps assess and improve the quality of your data. It alerts users to to errors and inconsistencies while streamlining all stages of the process into a single, easy-to-manage platform. Data Quality connects to hundreds of different data sources, so you can be sure that all of your data is clean, no matter where it comes from. Get started today with a free trial of Talend Data Quality, or by downloading Talend’s open source solution, Open Studio for Data Quality.