Ever pulled your hair in frustration because of incomplete names, inaccurate email addresses, and fake phone numbers?

You’re dealing with bad data and it’s hurting your marketing efforts.

In this quick piece, I’ll help you understand what bad data is, how you can significantly reduce it, and what you can do to prevent it from affecting your marketing efforts.

Here goes!

Understanding Bad Data

Bad data is any data that suffers from inaccurate, invalid, outdated, incomplete information. Unfortunately, most data that is gathered for the purpose of marketing will have these problems as an inherent feature.

For example, the web form you’re using to collect data may be filled by the same person multiple times, using multiple email IDs. The person may also be typing in their names with a spelling mistake. Some even give fake credentials!

If you have just a few hundred rows of data, you can apply simple Excel filters to sort it out, but when you have hundreds of thousands of records and it comes from multiple sources?

Another significant problem with bad data is that of duplication. Citing the same example above, a lead’s record is duplicated three times over each time they use a different email address! This causes significant data duplication that derails the quality of your data. You might end up thinking you’ve got five different leads, but they are really just one person. Multiply this instance by 10 times and you’ve got hundreds of duplicated information that you’ll need to sort.

Traditionally, these problems were always sorted through the use of unique identifiers – like assigning a specific number to an entity or using a main token such as the [Phone] or [Email] token to sort out information, but the problem with this approach is it neglects the possibility of errors and duplication with the main unique identifiers and tokens themselves!

So while you may think that a unique identifier as an automated serial number, such as, ‘webform001’ may be used to identify or remove duplicates, chances are one user has, ‘webform001-005.’ And if for every form, they provide a different address, or a different detail, or even miss a critical information piece like last names or middle names, it’s going to be more complicated for you to resolve.

The pain is real. Marketers must deal with bad data every single day. And the impact is so severe, it could cost people jobs!

How Do You Reduce the Chances of Bad Data?

Raw data is inherently bad data. But that does not mean you cannot curb it. Notice how I’m focusing on curbing instead of completely preventing it – that’s because you cannot stop bad data from happening. Moreover, you cannot ensure 100% clean data. The best data record count is 97/100 – meaning out of every 100 records, three records are considered faulty by default. So, the goal is not to completely prevent bad data, but to ensure that the data we need to use for various purposes is good enough.

Several things you can do to reduce bad data include:

  • Setting up strong front-end data collection processes (like the web form example above)
  • Use a data cleansing software to ensure that your data is cleaned in real-time.
  • Create a focused data collection strategy. If a data field does not serve your purpose, you don’t need to collect it.
  • Train employees on the basics of data collection, data entering and data rechecking. Most data errors are caused by poor data entry practices.
  • If you can afford it, hire a data specialist to help you set up a data collection and data governance policy.

Bad data is not only harmful for your business, but it can also lead to hefty legal fines if you don’t manage it well. There are dozens of real-time cases of companies paying heavy fines because they did not do their due diligence with data.

It’s imperative to remember that bad data does only mean inaccurate or corrupt data, it also means data security and data compliance. If you don’t have access to quality data, you cannot implement a robust data governance and compliance strategy.

To make your marketing efforts count, start by ensuring you have high-quality data to work with.