Picture in your mind a sales rep, Alex, who's on the verge of closing a huge deal. The moment has come to reach out to the lead with a personalized proposal, but there's a snag.

Despite searching through multiple databases, he can’t find the right contact information.

This lost opportunity is a stark example of how "dirty data" can directly impact business success – especially in your CRM. Dirty data refers to inaccuracies, duplications and outdated information that clutter your databases, slow down your efficiency and hinder decision-making.

Luckily, there’s a way to prevent these issues and improve operational processes: a robust data hygiene strategy. In this guide, we will explore the principles of data hygiene and what you need to get it right.

What’s Data Hygiene and Why Should You Care?

Data hygiene is vital for ensuring the accuracy, cleanliness, reliability and proper organization of your data. After all, it’s where your revenue growth stems from.

The process involves verifying details like names and email addresses for correctness and consistency.

For example, you want to make sure "Michael" isn't mistakenly referred to as "Mike" in some records.  Or if a lead has multiple emails (e.g. work and personal), you want them to be correctly linked to avoid confusion.

The True Importance of Data Hygiene

But why exactly does data hygiene matter so much? For starters, it enables businesses to make informed decisions based on accurate data, leading to more effective strategies and outcomes.

However, it can have big consequences if you ignore data hygiene. For instance, IDC found that dirty data costs US businesses over $3 trillion annually – yes, that’s trillions. And research from Gartner backs this up, finding that bad data loses a business nearly $13 million per year.

Your team relies heavily on data to identify potential leads, forecast sales and develop strategic plans to reach revenue targets. However, over time, you might notice that your sales figures aren't aligning with your forecasts and your conversion rates seem lower than expected.

Upon closer examination, you might realize that the data your team has been using is outdated and inaccurate. Contacts have changed roles, companies have merged or rebranded and some leads are no longer viable. This lack of data hygiene has led to inefficiencies in your sales process, with your team wasting time and resources chasing leads that are no longer relevant.

Beyond that financial impact, it can also damage trust and customer satisfaction. Over 60% of customers say that a company will lose their loyalty for an un-personalized experience.

Just imagine the frustration of receiving poorly targeted communications due to outdated or incorrect information and it makes sense. In a digital world, personalization is everything. If you get a contact’s name wrong or you send an email to the wrong person, you lose trust and credibility.

At its core, good data hygiene helps streamline business operations, reduce burnout, increase opportunities and improve the overall experience for customers.

Common Data Hygiene Challenges (and How to Overcome Them)

The first step in proper data hygiene implementation is understanding what challenges will crop up and what you can do to address them. These are the most common ones:

Duplicate Data

Duplicate data refers to having two or more identical records in your database. This redundancy can clutter your database and lead to inefficiency in operations and analysis. Without a consistent process that your organization is committed to, the bad data never gets corrected and is wrong.

This may work today. But as new data is added to your CRM, it piles up and decays, the same as the old information. The best approach is to stop the issue from snowballing.

It often occurs when data is collected from multiple sources in, for example, lead enrichment processes without a centralized checking system or when manual data entry leads to errors.

Here are a few solutions that can combat duplications:

  • Implement alerts for duplicates
  • Regularly audit your database
  • Streamline and standardize the data entry process

Incorrect/Outdated Data

As you might guess, this type of bad data happens because it was either incorrect at the time of entry or has become outdated over time (decayed), such as wrong addresses or old phone numbers.

Changes in personal information are not uncommon. For example, people change jobs and get a new work email.

The problem comes when databases don’t get updated promptly to reflect these changes. And of course, human error during data entry can also lead to inaccuracies.

Try these tips to keep incorrect and outdated data at bay:

  • Establish a routine for regularly updating data
  • Validate data at the point of entry with confirmation emails or SMS
  • Use data verification tools like Findymail, which provides and re-checks for fresh B2B email address contact information

Incomplete Data

Incomplete data includes records in your database that are missing critical information, making them less useful for analysis or outreach. Maybe you only have the first name or just the company’s phone number.

Often, incomplete data entry is due to optional fields in data collection forms or oversight during manual data entry.

You can prevent incomplete data if you:

Inconsistent Data

Another issue is inconsistent data that occurs when the same type of info is stored in different formats across the database (or multiple), which complicates data integration and analysis.

Often, it’s a lack of standardization in data entry or merging data from diverse sources without proper integration that leads to inconsistencies.

Naturally, the solution will involve:

  • Standardizing data entry formats for all data types
  • Applying data normalization techniques
  • Using software tools designed to identify and resolve inconsistencies

Missing Values

Missing values represent the absence of data where it’s expected, leading to gaps in information that can throw off your team and produce bad decisions. They usually result from errors in data collection, transfer or processing stages.

Here’s what you can do to fight against missing values in your dataset:

  • Implement a system to highlight when data is missing using tools
  • Regularly review and cleanse your data

Data Contradictions

When two or more records provide conflicting information about the same entity, that’s a data contradiction. It can lead to frustrations in your team and missed opportunities.

They typically arise from errors in data entry, merging records without proper deduplication or not updating all relevant records after changes happen.

You can overcome this challenge if you:

  • Use data-matching algorithms to identify potential contradictions
  • Establish a single source of truth for each data entity
  • Regularly review data relationships and dependencies

What Does a Good Data Hygiene Process Look Like? (Data Hygiene Checklist)

While each organization’s data hygiene process will look slightly different based on their needs and goals, the general process will follow these steps:

Step 1. Identify the Most Important Data

Decide which pieces of data are crucial for your business’s success. This means deciding which customer details, sales information or product data you absolutely need to keep an eye on.

For example, if you’re in B2B SaaS, understanding your customers' organizations is crucial. This includes information such as company size, industry vertical, geographical location, decision-making hierarchy and past interactions with your company.

However, you’d also monitor metrics such as sales pipeline, conversion rates, average deal size and sales velocity.

Step 2. Collect and Store Data Safely

Once you know what data you need, the next step is gathering that information and putting it somewhere safe. Typically, we recommend choosing reliable data providers, such as Findymail.

The goal isn’t just to get the contact information and have it be relevant today. Your team is going to create campaigns three, six and thirty-six months down the line. Their data has to be fresh.

In Findymail’s case, it doesn’t just integrate with your preferred CRM once. Instead, it integrates, supplying the initial data and then re-verifies the email address information, so you can rest assured the data is clean, accurate and reliable.

Think of this step as collecting all your important documents and storing them in a secure cabinet where you can easily find them. For most companies, this means the CRM – your revenue headquarters.

Make sure the integrations with your CRM work as expected and implement regular data audits.

Step 3. Clean out the Clutter

Over time, you’ll find duplicates or information you no longer need. This step is about going through your data and removing anything that’s not necessary.

The typical data cleansing process consists of:

  • Identifying duplicate, inaccurate or incomplete entries
  • Reviewing and updating information
  • Removing irrelevant data
  • Implementing data governance processes

It is a process, but one with a shining light at the end of the tunnel – more effective campaigns and higher conversion rates.

Step 4. Fill in the Gaps

Sometimes, you might notice some information is missing. Find the missing pieces and put them in place, so your data set is complete and ready to be used.

For example, you might notice that older records don’t contain intent data that was integrated after the fact. Ensure the records and contacts are still relevant and add the missing information.

Step 5. Uniformity Is Key

Imagine you're organizing a bookshelf, but all the books are of different sizes. It’s chaotic and the same goes for your company’s data. It needs to be standardized, so everything is in a format that’s consistent and easy to read.

First, implement data entry standards and enforce them in the collection fields so no record can bypass the check.

For example, define standard formats for phone numbers (country code - local code - number) and add the rules to your CRM so everyone (and everything) has to adhere to them in order to update a record.

At the same time, provide training to your team, so they’re clear on why the CRM information should be inserted according to the standards.

By following these steps and sticking to a routine, you can keep your data in top shape. Keeping your data clean and organized is an ongoing process but it’s well worth it – your campaigns will be more effective and your forecasts more accurate.

The 5 Best Tools for Data Hygiene

As mentioned, there are several tools you can use to make the data hygiene process easier and streamlined – ultimately improving the quality of your CRM. Here are some of the top tools we’ve found:

1. Findymail

Use Findymail to ensure your email address information is accurate and trustworthy.

If you have an existing database, Findymail will help you fill the gaps and verify (or find) the leads’ new email addresses.

If your team is undertaking lead generation efforts for the first time, Findymail pairs with your CRM or their processes (Google Sheets, using Sales Navigator) to find email addresses.

If you’re building a B2B email list, then Findymail needs to be one of the top tools to include in your tech stack for contact information data hygiene.

2. DemandTools

DemandTools was built and designed to help clean up your CRM data. It has features like deduplication and record merging that make your database more reliable and easier to use.

It’s especially effective for large databases that need a thorough cleanup.

3. WinPure Clean & Match

As the name implies, this tool works to ensure your data matches up and is duplicate-free.

WinPure Clean & Match helps improve the quality of your database by ensuring that records are not only clean but also correctly aligned and relevant.

4. Melissa Clean Suite

Melissa Clean Suite offers a wide range of tools to verify, correct and standardize addresses and other personal data.

It’s particularly useful for businesses that need to maintain high-quality customer data, ensuring your messages reach the right people.

5. TIBCO Clarity

TIBCO Clarity is designed to tackle complex data hygiene tasks, offering features like data profiling, cleansing and standardization. It’s especially helpful for businesses looking to dive deep into their data and clean it from the ground up, ensuring it’s in the best shape for analysis and insights.

If your CRM has been running for a while and everyone’s had a chance to add some more data to it, TIBCO can provide clarity.

Data Hygiene Best Practices and Principles

1. Audit and Review Your Data Regularly

Regularly auditing your data helps identify and correct errors before they become big problems. This process includes looking for outdated information, inaccuracies or duplicates that might have crept in.

2. Implement Data Validation

Data validation ensures only the right information gets into your system. By setting rules and using the right tools for data entry, you can prevent incorrect data from being added in the first place. This might include format checks in your CRM, completeness checks or even cross-referencing with existing data.

3. Eliminate Data Silos

Data silos happen when information is kept separate and not shared within an organization. Think about if each department in your company used a different language – it would be really difficult to work and collaborate.

Breaking down these silos and ensuring data flows freely across all departments enhances your collaboration and decision-making.

4. Implement Data Governance

Setting up data governance is like creating traffic rules for your data. It involves defining who can access what data, how data is used and ensuring compliance with laws and regulations.

Your data will flow to the right people and systems, but there will be safeguards in place to prevent any incidents, manage risks and protect sensitive information.

5. Train and Educate Your Team

Finally, your team needs to understand the importance of good data hygiene and how to achieve it. Regular training sessions can keep everyone up-to-date on best practices and tools.

By following these best practices and fostering a culture of good data hygiene, you're setting up a system that keeps it clean, making sure your organization's decisions are based on solid, reliable information.

Frequently Asked Questions

Q: Does data hygiene help with customer success? How?

A: Yes, data hygiene ensures customer information is current and correct, so you can provide personalized assistance and use sales tactics tailored to the way your existing customers are interacting with your business.

Q: Are data hygiene and data management the same?

A: No, they're related but different. Data hygiene focuses on cleaning and maintaining data quality, while data management encompasses a broader scope, including the collection, storage and usage of data.

Q: What does good data hygiene look like?

A: Good data hygiene means having a database that's regularly cleaned and updated, free from duplicates and errors and structured in a way that's accessible and useful for your organization's needs.

The easiest way to spot it is through your communication success rates: do your emails bounce? Do your campaigns reach the right people? Do your sales calls end in deals won?

Your team has worked hard to fill the funnel. Don’t let high-quality prospects leak through just because of poor data hygiene.

Unlock Success with Data Hygiene

Data hygiene paves the way for making reliable decisions and building high-performance sales processes. Unfortunately, data tends to accumulate and, over time, this leads to dirty data your team can’t just clean on the go.

When you turn data hygiene into one of your goals, you're setting up your team and your business for success.

(Psssst.... Findymail's new CRM Datacare can put your entire data cleansing workflow on autopilot. No more manual updates! Learn more and sign up for the waitlist.)