Bad Data is Everywhere.
When it comes to data, it’s quality — not quantity — that matters.
How can companies fix their bad data?
There is a lot of bad data out there. Here at NetWise, we perform a lot of comparative analyses of customers’ data sets. In some cases, they may have 100 million records in their data set, but their match rate is only 20%. If only 20% of their records match, that means they only have 20,00,000 accurate records. The other 80% is bad data.
If you take bad data and try to use it without cleansing it or otherwise try to improve the quality, you will end up with less-than-optimal results.
What is Bad Data?
Let me give you an example. A B2B data provider might have an accurate email for John the CTO at IBM. It knows that firstname.lastname@example.org is a good email, but it also associates 500 other Johns who work at IBM with the same email. So while it claims to have 500 good emails at IBM for people named John, in reality it’s only one email address. That’s highly inaccurate, bad data.
On the customer side, so many companies have messy CRMs. They have data that is years old, with all sorts of gaps or mistakes from improperly entered information. That’s bad data.
Or maybe the data is accurate but not at all useful. For example: a company wants to drive more traffic to its website. It may allocate too much advertising budget to Facebook and attract visitors with use cases that are much different than what its products are meant to solve. Using the data generated by those visitors will be useless for retargeting on other channels if they are simply not part of an appropriate target audience.
A Path Forward.
Bad data could be preventing you from making smart business decisions and maximizing your sales and marketing budgets. Here’s how you can take control:
Ask a lot of questions: When B2B data providers say that their data is 99% accurate, what does that mean? Are they 99% sure that this person is alive or that they have this title and work at this particular company? Or will this email be 99% deliverable? There are so many caveats to these kinds of claims, which is why we try to be as transparent as possible (and our customers love our honesty).
Answer a simple question: Why? Companies can hire other companies to cleanse their data and validate their records, but before they start cleansing their data, they must answer a simple question: Why? Why do they care if their data is a mess, and what problems is that causing? That’s key to identifying the solutions that can address a company’s issues related to bad data.
Lean on your partners: Once companies can say why they want to clean up their data, partners can help them prioritize solutions, based on budget, channels, existing data and other factors so they get the best bang for their marketing spend.
At NetWise, we aim for our data sets to be 92% accurate, and we don’t believe that you will find higher degrees of accuracy in the market at our scale. We can help you clean up your own data — which may be old, inaccurate or incomplete — by giving you a complete view of your CRM files that are still up to date. We can filter out the bad data so it doesn’t make it to the next step.
Companies can also start with our data set to select the audiences they want to target in a specific industry. For example, our platform may compile a list of 10,000 people you can target on LinkedIn. You can monitor whether any target clicked on an ad or visited your website, creating a data flow to your CRM and other systems. This data feedback loop of amplification and quality will only strengthen your sales and marketing efforts.
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Want to Dig Deeper?
You might like this Data-Driven Marketer podcast episode!