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The Problem with Periodic (Not real-time) Data Updates

Many data vendors update their database periodically, often only refreshing a small portion (about 10%) every 90 days. This means that a large majority of contact and company records could be 12 months old or more. The issues with this approach primarily revolve around:

1. Outdated Contact Details: Contacts may no longer be associated with the listed company. The longer the time lapse since the last update, the higher the chance that the contact information is outdated.


2. Irrelevant Emails: Emails that were verified a long time ago may no longer be valid. Again, the lag in updates increases the chance of having irrelevant or non-functional email addresses.

To make matters worse, some data providers may use deceptive tactics to exaggerate their coverage or accuracy, such as:

1. Phone Number Misrepresentation: Some providers list the company phone number as the contact's direct phone number to inflate their coverage statistics.


2. Employee Count Inflation: Providers may exaggerate the number of employees for a given company, even when a significant number of those listed have moved on.

Overcoming Data Verification Challenges

The challenge for businesses lies in verifying the accuracy of the data. It's often only possible to fully verify the information when you actually send an email, reach out on LinkedIn, or call the prospect. Comparing each person's job information with their current LinkedIn profile for verification is time-consuming and impractical.

So, how can you maximize your understanding of data quality and ensure you're working with the most accurate data? Here are some strategies:

1. Ask About Update Frequency: During your initial evaluation, ask data vendors how frequently they update their data and what percentage of their database is refreshed during each update cycle.


2. Request Accuracy Metrics: Ask the vendor for metrics or measures they use to quantify data accuracy. This could include bounce rates, response rates, and more.


3. Spot Check the Data: Do a random spot-check on a sample of the data provided. Cross-verify a subset of contacts and company information with LinkedIn or other publicly available sources.


4. Monitor Response Rates: Once you start using the data, closely monitor response rates. A high bounce rate on emails or a large number of returned mails can be an indication of outdated data.


5. Implement Real-Time Data Verification: Consider investing in real-time data verification tools or services. These can help validate email addresses and other contact information at the point of entry, ensuring your data remains accurate over time.


6. Leverage Automation: Use automated tools that can cross-check and update your database regularly against public sources like LinkedIn. This will help ensure that your data remains up-to-date without significant manual effort.

By asking the right questions and taking proactive steps, you can better understand the quality of data you're purchasing. Remember, the goal is not just to acquire data, but to acquire data that is accurate, relevant, and actionable.