10 Data Hygiene Best Practices to Keep Your Data Clean

According to a survey conducted by TDWI, 83% of organizations make decisions largely based on data.

Businesses today have unprecedented access to vast amounts of customer information; however, the quality of that data can make or break marketing efforts.

A high-quality marketing database ensures that the right message reaches the right audience.  As a result, businesses can build stronger customer relationships, make data-driven decisions, and optimize marketing strategies for better results.

In fact, 91% of leaders surveyed by Experian report that data hygiene and data quality investments have positively impacted business growth.

What is Data Hygiene?

Data hygiene is all about keeping your data clean, accurate, and reliable. It gets rid of errors, duplicates, and outdated info so your data stays trustworthy for analysis and decision-making.

This includes tasks like validation, cleansing, deduplication, audits, and updates. Good data hygiene is crucial for organizations to get valuable insights and maintain data integrity.

The Consequences of Bad Data

Without clean marketing data, marketing efforts can become inefficient and downright costly, ultimately hindering a company’s ability to achieve its marketing objectives and thrive in a competitive landscape.

How to Improve Data Quality

The average business today has 17 unique technology applications housing customer data, and on average, businesses receive data and metrics from 28 unique sources.

With the explosion of so much information, managing data can often be a cumbersome task for businesses. According to Experian’s research, the top issues faced by businesses included:

  • A lack of data quality monitoring (37%)
  • The volume of data or number of databases (35%)
  • Significant data duplication (35%)

Tech solutions that companies plan to implement include implementing regular monitoring, reporting, and visualization, according to 55% of respondents. This was followed by using easy-to-use tools for business users, according to 50% of survey respondents.


10 Best Data Hygiene Practices to Keep Your Data Clean

Simply put, keeping data clean and accurate is crucial for effective decision-making and operational efficiency.

Here are ten best data hygiene practices to help you maintain clean data:

1. Establish Data Governance Policies

Create clear and comprehensive data governance policies that define data ownership, data entry standards, and data maintenance procedures. Ensure that everyone in your organization understands and follows these policies.


  • Assign a data steward or team responsible for overseeing data governance efforts.

2. Standardize Data Formats

Enforce standardized data formats and naming conventions to ensure consistency across all data sources. This includes date formats, phone numbers, addresses, and other data fields.


  • Document your data standardization processes, including the steps, tools, and guidelines used. This documentation serves as a reference for data handlers and ensures consistency in your standardization efforts.

3. Data Validation and Verification

Implement data validation rules and verification processes at the point of data entry. This can include using dropdown menus, date pickers, and validation checks to minimize errors.


  • Set validation rules that match data types to the expected input. For example, ensure that numeric fields only accept numbers and that date fields follow a specific format.
  • Validate numeric data for appropriate ranges, such as minimum and maximum values. This is useful for fields like age, price, or quantity.
  • Implement email validation rules to ensure that email addresses entered are correctly formatted (e.g., user@example.com).
  • Use validation rules or regular expressions to validate phone numbers, ensuring they follow the expected format for your region or industry.

4. Automate Data Entry

Use automation tools and software to reduce manual data entry errors. Automation can streamline data capture processes and minimize human error.


  • Invest in tools and platforms that allow you to seamlessly connect different systems and databases. These tools can automate the transfer of data between applications, eliminating the need for manual data entry. Ensure that these integrations are well-configured to handle data accurately.

5. Regular Data Audits

Conduct regular data audits to identify and correct inconsistencies, duplicates, and inaccuracies. This proactive approach helps maintain data quality over time.


  • Create a regular schedule for data audits. The frequency of audits may vary depending on your organization’s needs, but it’s essential to conduct them periodically.
  • Use data quality software or custom scripts to automate parts of the audit, especially for large datasets.

6. Data Deduplication

Implement deduplication processes to identify and merge duplicate records within your database. This helps prevent redundancy and ensures that records are up to date.


  • Implement software that utilizes matching algorithms to identify potential duplicates with greater accuracy. These algorithms can consider variations in data, such as typos or abbreviations, to find matches.

7. Regularly Update Data

Keep your data up to date by regularly updating contact information, product details, and any other relevant data. This is particularly important for customer and vendor databases.


  • Consider data enrichment services to enhance your existing data with additional information. This can include appending missing contact details, demographic information, or behavioral data to your records.

8. Implement Data Quality Tools

Invest in data quality tools and software solutions that can automate data cleansing, validation, and enrichment processes. These tools can save time and improve accuracy.


  • Consider using a marketing platform with built-in data quality measures.  Customer data platforms and other types of marketing platforms are often easier for those with marketing roles to use as compared to solutions that require a heavier list by a data science or technical team.

9. Train and Educate Staff

Provide training to employees who handle data to ensure they understand the importance of data quality and how to maintain it. Encourage a culture of data stewardship within your organization.


  • Create easy-to-understand documentation that outlines data quality guidelines, standards, and procedures. Make these documents readily accessible to employees as references.
  • Offer ongoing training sessions rather than one-time events. Data quality is an ongoing effort, and continuous education helps employees stay current with best practices.

10. Monitor Data Quality Metrics

Establish key performance indicators (KPIs) and metrics to monitor data quality regularly. This allows you to measure the effectiveness of your data management efforts and make improvements as needed.


  • Whenever possible, automate the collection and monitoring of data quality metrics. Use data quality tools and software that can generate reports and alerts when metrics fall below predefined thresholds.

Maintaining clean and accurate data is a fundamental necessity for businesses in today’s data-driven landscape. By prioritizing data hygiene, you can make informed marketing decisions, build trust with customers, and gain a competitive edge in a rapidly evolving digital world. Clean data is not just a best practice; it’s a strategic imperative for success.

Data Hygiene Solutions

Meaningful consumer engagements can only be achieved with a foundation of high-quality data. Porch Group Media has a long history of delivering data quality and management solutions for hundreds of clients over the past 20 years. Our data hygiene solutions help you tidy up your database and drive better marketing results.

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