Data is an absolute necessity in today’s business environment. It enables organizations to learn more about their customers, their competitors, and themselves. Nowadays, enterprises pride themselves on being data-driven in their approach to business, and the ability to leverage data appropriately is one of the most critical aspects of business development. 

According to Forrester, “many people’s jobs increasingly rely on the self-service availability of accurate, credible data people can use when they need it to perform their daily tasks.” Organizations are quite adept at collecting vast amounts of data from their customers, but the most valuable data needs to be accurate and credible. 

Therefore, to truly gain success from a data-driven culture, the data you’re working with must be clean. We spoke to data experts to understand the importance of data hygiene and how businesses can make sure their data is spotless. 

What Is Data Hygiene (And Why Does It Matter)?

Data hygiene is the process of ensuring that a company has clean data. This means that data is free of errors, consistent and accurate. Cleaning data prevents companies from struggling with the issues caused by dirty data. Data is seen as dirty when there is duplicate information, incomplete or outdated data. 

Dirty data is one of the most significant challenges companies can face, so data hygiene is essential. According to Meetesh Karia, CTO at Austin, Texas-based insurance company The Zebra, dirty data is a worse proposition than no data at all. “Having dirty data can lead people to make bad decisions by putting trust and faith in inaccurate information while ignoring other, better sources of information. It’s garbage in, garbage out.”

Data hygiene enables you to get the most accurate information when you need it, but that’s not all. “It’s about creating a bigger process around how you manage that data,” says Fredrik Forslund, VP of Cloud and Data Center Erasure at Austin, Texas-based Blancco. “By following data hygiene best practices, you can ensure your organization has taken the first step to holistic data management—meaning you’ll know what you have, where you have it and when it should be properly disposed of.” Ultimately, data hygiene can help you to save time and money. 

Yet, while starting a data hygiene process can help your company get its data on the right track, that doesn’t mean it’s something you only do once. Kirk Haslbeck, VP of Engineering at Brussels, Belgium-based data intelligence company Collibra, points out how he and his team approach the process. “Just like a credit score is an accumulation of many tests such as timeliness of bill payments, monthly cash flow etc., we look at a health score that is an accumulation of major tests of data quality.” 

Here are some of the best practices our experts provided. 

Related Articles: Why CIOs Need a Data Readiness Strategy

Set Your Baseline

Before beginning the process of cleaning your data, you should create a baseline that outlines your data’s current state. Start with an audit and locate all of your existing data. Whether it is found on old hard drives, laptops, on-premise servers or in the cloud, take an assessment of everything. Dirty data can come in several forms, but conducting regular audits can help you determine your data quality and what needs fixing. 

Create Quantifiable Metrics

The data hygiene process requires you to know what constitutes clean data to understand what you’re aiming for. Setting key indicators for your data can help to focus your efforts in the right areas. “Decision-makers should look at quantifiable metrics to assess how effective their departments are at managing data and build a program that constantly monitors and stress tests data health,” said Haslbeck.

Learning Opportunities

Classify Your Data

Classifying data into different categories can ensure that you can access your most important data when you need it, as Forslund points out. “Classify your current data sets into different categories. Start with business-critical data that is required right away, data necessary for compliance that you might need later or unnecessary data that is redundant or obsolete.”

By evaluating both internal and external systems, you can make sure you’re only collecting necessary information and not clogging up your data pipeline with unnecessary data from customer forms and surveys. 

Build a Data Governance Program

Classifying data can help everyone track data throughout its entire lifecycle, but a data governance program is essential if organizations want to ensure the quality of data everywhere. Data decision-makers should implement a governance program that provides guidelines for everyone managing data throughout the entire lifecycle, including creation, storage, sharing and erasure. 

Standardized metrics implemented through a data governance program can help you to streamline data collection and establish consistency by limiting unnecessary or nonsensical values from being inputted. 

Make Data Hygiene an Organization-Wide Effort

Even though cleaning data is traditionally a process reserved for engineering and data science teams, the reality is that everyone without an organization should be involved in the data hygiene process. This ensures a proper understanding of what data hygiene includes and can push an organization towards being truly data-driven.

Ensuring that everyone within the organization is aware of data hygiene practices can eliminate silos and improve alignment. Communication and collaboration between closely linked teams such as sales and marketing can also be improved as they would have a clearer understanding of what constitutes valuable data. 

Invest in the Right Data Monitoring Tools

Tools can help you observe and monitor your data to identify issues faster and respond to them accordingly, keeping your data in good health. Data monitoring tools can be used to automate data quality checks and alert your organization about issues, preventing employees from inputting incomplete data into the pipeline. Data cleansing systems can also pick up anomalies and duplicates, speeding up data cleansing efforts and maintaining data standards.