The Only Data Quality Checklist You Need for B2B Success

Low-quality data is a real pipeline killer.

Incorrect, incomplete or irrelevant data creates a whole host of business problems. When your company is struck by poor data quality, you’ll make decisions based on faulty assumptions. You’ll waste resources chasing up correct customer contact details. And ultimately, you’ll lose prospects (and revenue!).

If you’re committed to improving quality standards in your sales and marketing data, the first thing you’ll need is an effective data quality checklist.

In this article, we’ll explain what a comprehensive checklist looks like, why it’s beneficial, how to put one together, and what to include in yours.

Scroll 👇 for the only quality checklist template you need for B2B.

What constitutes quality data in a B2B context?

First, though, let’s take a step back and explain what quality B2B data actually looks like.

After all, if you’re going to run a checklist in your existing data set, you want to know what you’re aiming for.

We consider data quality across six distinct characteristics:

1. Accurate. The data in question is correct and free from errors, both big (like an incorrect business email or invalid phone number) and small (like a misspelt name).
2. Complete. All necessary data points are present and stored in the correct place.
3. Consistent. The data is uniform across different systems, departments, or software tools.
4. Timely. The data is up-to-date and relevant at the time of use.
5. Valid. The data follows the required formats, basic quality standards, and structures.
6. Reliable. You can trust the data to help you make critical business decisions, like territory planning.

If your sales data checks all six of these boxes, then you know it’s high-quality data.

What is a data quality checklist?

A data quality checklist is a structured set of criteria used to evaluate the quality of data in a system.

The main goal of a data quality checklist is to ensure that a given data set meets an organisation’s standards for accuracy, completeness, consistency, timeliness, validity, and reliability.

Using a checklist is an essential part of data quality management. It ensures that any data used in business operations is trustworthy and accurate.

You can divide data quality assessments into two categories:

1. Basic data checks: Verification of formatting, correct values, and random spot checks.
2. Advanced data checks: Validation of accuracy, completeness, and validity.

Advanced data checks should be the focus of your sales and marketing efforts. We don’t just want to know that our data is formatted correctly; we want to know that it’s accurate, complete, and useful.

What are the benefits for B2B teams in having a data quality checklist?

So, what exactly can a good checklist help with?

Efficiency

A robust data checklist helps B2B sales teams confirm data accuracy in an efficient way.

Without quality checking in place, your sales and marketing teams will be plagued by common data quality issues.

These issues usually pop up when it’s too late and you’re in the middle of something important, like firing off an email or making a cold call. Everyone in B2B knows there’s nothing worse than searching for an accurate phone number when you’re in the middle of a hot streak.

A quality checklist ensures your data is accurate before jumping into those focus sessions.

Better decision-making

Quality checks improve data accuracy and enable better business decisions.

Consider, for example, a GTM team that’s preparing to launch a new ABM campaign.

If they have an accurate view of the account’s buying committee, they’ll be in a stronger position to target the right stakeholders.

Improved customer relationships

Having accurate data in your marketing and sales processes helps you personalise communications and boost customer engagement.

Inversely, if a customer’s name is spelt wrong in your CRM, you’ll struggle to build rapport with your cold emails.

Cost savings

Improving data quality significantly impacts the bottom line, not only through improved sales but also through a sharp decrease in wasted resources.

Here’s an example:

Suppose you’re a B2B logistics company sending thousands of monthly invoices. Errors in client billing addresses or purchase orders lead to unnecessary payment delays, which can hurt cash flow and even lead to an overreliance on business credit.

Why and when do you need to use a data quality checklist?

The simple answer is: you need good customer data quality for all your sales and marketing campaigns.

That said, there are some situations in which quality checking is even more important, such as when you have to prepare accurate reports or make critical business decisions.

As far as when to use your checklist goes, here are three great places to implement the practice:

• Right after data is inputted or updated.
• When merging data from multiple sources.
• Periodically, such as every quarter.

How do you put a data quality checklist together?

Looking to put together your own quality assessment checklist?

Here are a few key steps to follow.

1. Define data quality goals

Start by identifying what exactly your team needs from its data.

Accuracy is probably a good start, but consider:

• How critical is completeness?
• Is it important that your data is consistent across all systems?
• What do validity and usefulness mean to you?

Your answers to these questions must align with your overall business objectives.

2. Identify critical data elements

Here, you must determine what types of data you’re going to check.

Examples include prospect phone numbers, email accounts, and reliable job title data.

3. Set up validation and review procedures

Now, establish a plan for checking and validating your data.

Start by reviewing and validating your existing customer data, then move on to prospect data, and finally cover previous client or supplier information.

Creating a phased plan like this is an excellent way to ensure that your data cleansing procedure is structured and manageable. It doesn’t have to be a daunting task!

4. Establish ongoing monitoring mechanisms

Finally, you’ll want to set up a system for continuous quality testing.

This can be a regular audit (quarterly, for instance), though a better move is to use a quality control and enrichment tool (like Cognism!) to run cleanup tasks in your CRM.

See how Cognism can enrich and refresh your CRM with high-quality data - take this interactive tour 👇

What should a data quality checklist contain?

Before you dig into preparing your checklist, take a quick note of these items. You must include all of them!

• Accuracy. Is the data free from errors? Does it correctly represent real-world information?
• Consistency. Is the data consistent across different systems? Is it all formatted in the same way? Are there any conflicting records or values?
• Completeness. Are all of the required fields filled in? Are there any gaps or missing values?
• Timeliness. Is the data up-to-date? Is it being collected and updated at the required frequency?
• Uniqueness. Are there any duplicate records or blank entries? Is there a process in place for identifying and removing duplicates?
• Relevance. Is the data relevant to the task or decision-making process for which you intend to use it? Are there any outdated or irrelevant data points that need removing?
• Compliance. Does the data meet any legal or regulatory requirements? Are there appropriate processes in place to ensure data privacy and protection?
• Accessibility. Is the data easily accessible to those with the authority to view it? Are there access restrictions in place to protect the data from non-authorised parties?
• Traceability. Can the source of the data be traced?
• Data governance. Is there a policy for data ownership and stewardship? Are there clearly defined and accessible rules and responsibilities for managing the data?

Including the above points in your data quality checklist will ensure that your company data is accurate, relevant, and compliant.

Test Cognism’s data

A data quality checklist is an effective tool for B2B teams. It ensures that the data fuelling your sales and marketing engine is accurate, relevant, and helpful.

It’s also a good way to check whether your data provider is worth their salt!

So, why not put our data to the test? Get a free sample of Cognism’s lead data and compare it to yours.

We guarantee:

• 2x more cell phone numbers than ZoomInfo.
• 180% more contacts than ZoomInfo in Europe.
• Unrestricted data views and page-level exporting - no paying for extra credits!

What are you waiting for? 👇