What is Data Hygiene and Why Is It Important?

Data hygiene refers to maintaining clean, complete, and error-free databases using processes that ensure that organizational data is clean, accurate, and properly maintained. Collectively, it’s set of practices aimed at maintaining the quality and integrity of data throughout its lifecycle.

So, what’s the difference between data quality and data hygiene? Data quality refers to the overall health of data, ensuring it is accurate, complete, reliable, and relevant for its intended use. Data hygiene, on the other hand, is a part of data quality that focuses specifically on the day-to-day processes of cleaning and maintaining data. While data quality encompasses a broader spectrum, data hygiene deals with the routine activities needed to keep data error-free and current.

Benefits of data hygiene

Effective data hygiene helps organizations avoid errors, improve decision-making, support a better customer experience, protect your brand, and maintain operational efficiency.

Benefits of good data hygiene also include better customer relationships and reduced risk of compliance issues.

Ensures decision-making is more informed

Cleansing data for accuracy and quality helps yield accurate analytics that contribute to better, more informed, and more confident business decisions.

Improved efficiency

Data hygiene good practices improve your efficiency in many ways. For starters, it helps optimize resource allocation. With clean data, resources such as inventory, personnel, and capital can be managed more effectively. For example, accurate inventory data helps prevent overstocking or stockouts, thereby optimizing supply chain operations.

Data hygiene also enhances data integration. Clean and standardized data is easier to integrate across systems and platforms. This improves the efficiency of data migration, consolidation, and analysis, reducing the time needed to reconcile disparate data sources.

Data hygiene helps support IT across your organization. Consistently maintaining data quality reduces the burden on IT staff to address data-related issues. This frees up IT resources to focus on more strategic initiatives rather than routine data cleanup tasks.

Cost reduction

By following data hygiene best practices, companies don’t waste time or money by sending outreach based on outdated contact information. Data hygiene also helps maintain an accurate database of potential customers, making for a better ROI on advertising investments.

In addition, accurate data prevents losses from bad decisions based on faulty data. A study by Gartner estimates that poor data quality costs organizations an average of $12.9 million per year in losses.

Improved customer experience

Customer intelligence is the core concern of organizations, and data is essential to a positive customer experience. As companies increasingly rely on data to inform business decisions, the data must be accurate, complete, and “clean.” During each phase of a customer experience, from initial outreach and advertising, through sales and support, data comes into play. Poor data leads to bad experiences.

Proper data hygiene ensures that all marketing efforts are based on accurate and up-to-date insights.

Improved productivity

Clean data removes perishable information, providing data users with accurate insights to form a better understanding of business users and clients.

Ensures compliance

Data hygiene plays a critical role in ensuring compliance with regulatory requirements by maintaining the accuracy, completeness, and security of data. Compliance is obviously a huge topic, but here are the main components:

• Accuracy and reliability of data
  • Regulatory compliance: Many regulatory agencies and professional standards associations require organizations to maintain accurate and up-to-date records. HIPAA comes to mind as mandatory. Data hygiene practices, such as regular data cleaning and validation, ensure that the information stored is accurate, reliable, and secure.
  • Audit trails: Accurate data and solid documentation aid in creating reliable audit trails, which are essential for demonstrating compliance during regulatory audits and assessments.
• Data security and privacy
  • Protection against breaches: Effective data hygiene includes measures to secure data from unauthorized access and breaches. This involves implementing data encryption, access controls, and regular security audits.
  • Data minimization: Keeping only the necessary data and securely disposing of obsolete data helps minimize the risk of breaches and non-compliance with data retention policies.
• Data governance
  • Policy adherence: Data hygiene is an integral part of data governance, ensuring that data management policies and procedures are followed. This includes data classification, data lifecycle management, and adherence to regulatory guidelines.
  • Consistency and standardization: Consistent and standardized data management practices help meet compliance requirements by ensuring data is handled uniformly across the organization.
• Data integrity
  • Prevent data corruption: Regular data cleaning and validation helps prevent data corruption, which can lead to non-compliance if regulatory reports or records are based on incorrect data.
  • Maintain data quality: High data quality supports accurate reporting and analysis, essential for compliance with financial regulations and other industry-specific standards.
• Adaptability
  • Future flexibility: Good data hygiene practices enable organizations to quickly adapt to changes in regulatory requirements. Clean and well-organized data makes it easier to implement new compliance measures and update policies accordingly.
• Customer trust and reputation
  • Data privacy: Compliance with data protection regulations builds customer trust. Clean, accurate data ensures that customers' data privacy preferences are respected, enhancing the organization's reputation.
  • Avoid data breaches: Guarantee compliance through proper data hygiene helps avoid hefty fines and legal repercussions associated with data breaches or non-compliance.

Maintaining good data hygiene is essential for meeting compliance requirements by ensuring data accuracy, security, and integrity. Good data hygiene supports robust data governance, aids in protecting sensitive information, and helps organizations swiftly adapt to regulatory changes.

7 Data hygiene best practices for your business

Best practices for data hygiene ensure, consistency and accuracy, efficiency, compliance and security, and better decision making. Find 7 top best practices below:

Perform audits

The most fundamental component of data hygiene includes regular and consistent audits. Regular data audits involve systematically reviewing your data sets to identify and correct inaccuracies, inconsistencies, and gaps.

An audit typically involves the use automated tools to scan for duplicate records, ensuring that each entry is accurate. An important task that an audit performs is the validation of data fields for consistency, such as checking that email addresses are valid, phone numbers are correctly formatted, and customer information is complete and up to date.

A solid audit requires thorough documentation of any issues found during the audit and explains the steps taken to correct them. This documentation helps track recurring problems and provides a record of data quality improvements over time.

Data governance

Establishing policies and procedures for managing data quality across the organization. This includes defining data standards, roles, and responsibilities.

A proper data governance framework can help maintain the integrity of your database and ensure your data stays high quality. It’s advisable to designate a data steward who is responsible for master data files and special data hygiene projects.

Set data standards

It’s important to set clear data standards, but the task is not as simple as you might think. Addresses, for example, are far more complex than one would expect. Zip codes as well. Do you know that some zip codes are not fixed to a specific geographic location? Data is notoriously variable and inconsistent, so it’s essential to set standards so that your data is clear and unambiguous.

Aside from resolving ambiguity issues, you must also set constraints on your data. What fields can be made mandatory? Can range constraints be established to prevent users from entering nonsensical values below or above a given threshold? Can input values be mandated to be of a certain data type? By establishing constraints, you’ll ensure that data is entered consistently, minimizing the potential for corrupt or unusable data.

Data validation procedures

Data validation is a huge topic, but there are key requirements that need to be met. Some of these include:

• Format, range, and constraint validations: Ensure data types and patterns (e.g., email, phone numbers) are correct. Verify numerical values and constraints (e.g., start dates are not later than end dates).
• Consistency and uniqueness checks: Ensure related fields are consistent and maintain referential integrity. Detect and handle duplicates.
• Presence checks: Data is next to useless if not complete. Confirm all mandatory fields are populated and handle null values appropriately.
• Domain validation: Validate against a predefined list of acceptable values.
• Business logic validation: Implement custom rules based on business requirements.
• Statistical validation: This is a sanity check. Identify outliers and compare data distributions to expected patterns.

Automate data cleansing

The sheer size and complexity of data formatting are beyond human capabilities without some additional help. Data cleansing is a process that must be automated.

Update data frequently

Data changes are fluid and rapid. It’s essential to update data very frequently, ideally in real-time.

Data silos

In most organizations, sales and marketing teams operate on distinct platforms. Sales teams tend to live and breathe in their CRM systems, while marketing professionals tend to spend large chunks of time in their marketing automation platforms. Sales and marketing teams tend to speak their own language and have their own data standards and formats.

Ail teams involved in updating and maintaining data align on data accuracy and standards for inputting and updating customer records. Many organizations fall into the trap of requiring that one central customer intelligence data platform be used to input customer information. This is dangerous and often counterproductive. Integration is often the best approach. Companies that integrate their databases boast conversion rate increases of up to 12.5%.

CData Sync levels up your data hygiene practices

CData Sync is your solution for handling all aspects of data hygiene. Leverage the data integration tool for:

• Full Integration: Connect seamlessly to a wide range of data sources, ensuring unified data management.
• Automated data cleaning: Automate processes like deduplication and error correction to improve data accuracy.
• Real-time access: Provide live data connectivity and synchronization, keeping data current and reliable.
• Scalability: Handle large data volumes efficiently.
• User-friendly data handling: Easily configure and manage your data hygiene using an intuitive interface.
• Compliance and security: Comply with all your regulatory requirements and ensure secure data handling.

These benefits collectively enhance data quality, consistency, and reliability.