Database Hygiene: Essential Steps to Keep Your Data Clean

Common issues: what leads to poor-quality data?

Dirty data

Poor data quality can hurt your decision-making and efficiency. Dirty data emerges from many sources, such as data entry errors, inconsistent formatting, or merging multiple data sources.

Dirty data could include poorly formatted contact details, fake email addresses, spam or misspelt input fields.

Human error

Human error is an unavoidable factor in any process. Despite your best efforts, data entry mistakes can happen, leading to duplicate records, incorrect data, and inconsistencies across your system.

Developing a data quality plan to minimise these errors and ensure that your information remains reliable is essential.

Data decay

Data decay is the gradual decline in the accuracy of your data over time.

As contacts change jobs and businesses evolve, the information in your database may become outdated. Incomplete records and unengaged contacts can also contribute to data decay and weaken your customer relationship management systems (CRM).

Multiple systems

A common issue I have experienced in my career is the challenge of integrating multiple CRMs. If each department uses a different system, you rely on APIs or (shudder) manual data transfer to maintain accuracy.

If one system has outdated or missing information, you are unlikely to achieve a single source of truth.

Managing multiple systems can be achievable with a robust data strategy and pipeline to transfer data. However, if you are a smaller business low on resources, I recommend using a single consolidated CRM like HubSpot.

‍(Heads up - we are a HubSpot affiliate. If you upgrade and make a purchase through this link, we will receive a commission).

What are the impacts of bad data management?

Inefficient data management can be costly, especially for B2B companies.

• One critical issue is duplicate data. Duplicate contact records not only waste time and valuable resources but can also harm your reputation.
• Poor data security can expose your customers' personal information, leading to legal and financial consequences.
• Another concern is missing information. Incomplete or inconsistent data can affect your ability to make informed decisions, leading to lost opportunities and reduced revenue.

Best practices to keep your database clean

Maintaining proper database hygiene practices is crucial. Keeping your data accurate, clean, and up-to-date ensures effective lead tracking, campaign performance, and personalisation.

Here are some practices to help you improve database hygiene:

• Regular audits: Conduct data audits to identify inconsistencies, inaccuracies, and errors.
• Data cleansing: Implement techniques such as deduplication, correcting missing or invalid entries, aligning data formats and standardising naming conventions.
• Standard operating procedures (SOPs): Develop SOPs and a data governance policy. Staff should know how to manage data at the point of entry and during ongoing maintenance to ensure consistency.

Several constraints might impede your database hygiene efforts. You can address these by:

• Hiring data analysts: Bringing in-house data specialists on board ensures accuracy and effectiveness in managing your data.
• Using data hygiene tools: Invest in tools and software that improve data hygiene. These tools can streamline your processes and save time.

To improve targeting and organisation-wide data handling, consider the following:

• Establish a data owner: Assign someone in your organisation responsible for managing and maintaining your data. They can ensure data accuracy and oversee the implementation of data hygiene practices.

Remember, good database hygiene goes a long way in ensuring your campaigns' success and improving your organisation's overall performance. By incorporating these best practices, you'll have a solid foundation for improved database hygiene in your organisation.

Summing up - how do you maintain database hygiene?

To maintain data hygiene:

1. Standardise data at the point of entry.
2. Cleanse your data regularly to remove duplicates and inaccuracies.
3. Validate contact information to ensure it is accurate and up-to-date.
4. Implement SOPs and a governance policy to ensure data quality.