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The days of spray-and-pray advertising are dead. They have been for a long time.

These days, audience targeting is a critical part of any marketing campaign. Most marketers use Big Data—the decade's sexiest buzzword—to target prospects.

The bad news is that Big Data is worthless unless it's of the highest quality.

Unfortunately, most ad campaigns are fueled by irrelevant, untrustworthy data. Bad-quality data costs companies on average nearly $13 million per year, according to a Gartner survey.

If you don't have high-quality data, you're in a lose-lose situation: You lose out on potential customers because of mistargeted ads, and you put yourself at risk financially, legally, and reputationally.

To address that issue, before launching campaigns you should ensure the accuracy and quality of any data that you are using.

Easier said than done, right? But there are ways to verify data sets and ensure that the data you amass in the future is trustworthy and accurate.

Conduct a data audit of your current assets

You might not be sure of the quality of your data right now, but it's possible to introduce processes that will help you find out exactly what you're dealing with. Possible, and necessary: data verification and hygiene should be a routine practice.

To start, conduct a thorough audit of all internally and externally sourced data sets. That includes data that you are directly collecting as well as data that you license from other partners.

Nobody likes the word "audit," but it's a necessary part of your data verification process. Simply put, you need to confirm that your data is legally OK.

Use internal teams or work with a partner that specializes in data process and policy verification to confirm that...

  • The data has been collected with the appropriate consent.
  • It abides by privacy regulation requirements.
  • The sources and methods of data collection are transparent.
  • The process for producing the resulting data follows industry best-practices.

Some of your data might not be up to snuff, but it's better to find that out now than during a lawsuit later.

When you have a better understanding of the processes and policies used to produce your data assets, you can conduct a risk assessment of your data sets. That will then help you decide which data sets to continue using and which to stop using because of potential compliance issues or quality risks.

Look for problems

After your audit, it's time to review and assess the data itself. When reviewing and cleaning your data, there are a few key problems to look for.

1. Identify and eliminate duplicates

Duplicate data can come from a variety of places: Data migration, manual data entry, third-party connectors, data exchanges, and batch imports can all cause duplicate entries.

Having multiple records for the same data point means you're paying extra for the storage, and you risk misinterpreting your results. You're also sending redundant messages to your prospects.

2. Check for out-of-date data

Data is not like wine or cheese. It doesn't age well at all. In fact, the older the data, the less useful it is.

For instance, you may be targeting a prospect who clicked on your ad a year ago, but who no longer works at the company you have in your records. Or you may be relying on intent data from a partner that was based on shopping behaviors the person exhibited six months ago while your products have a much shorter purchase cycle.

3. Look for incomplete records

Incomplete records are especially likely if you have any kind of manual data input in your process.

Imagine you convert 30% of your prospects who come to you through Google Ads, but for half of those conversions you don't have the marketing channel recorded as a consequence of a technical error. When it's time to pull together a marketing report, you might mistakenly believe Google Ads aren't very effective. You could lose the opportunity to double-down on a winning strategy, and you might instead mistakenly invest in a less effective channel.

When you have a handle on the scope of the data cleansing problem, you can decide whether it's small enough to fix manually or whether it's worth investing in technology to help.

If you're just a small company, it may be worth your time to spend a few days hunting through your CRM to ensure your records are complete, and maybe spend some time on LinkedIn to update your data. If you have too much data to reasonably comb through manually, you can look to automated technology or services that could do it for you instead.

Prevent bad data in the first place

Ideally, you won't find yourself in the position of having to go back and verify data sets after you have been using them. As they say, an ounce of prevention is worth a pound of cure.

Here are three tactics that can help you avoid the issue of low-quality data altogether.

1. Standardize your data auditing process

Establish the criteria that you will rely on to select data sources up front, and establish a process—internally or with an audit partner—for verifying compliance with your criteria. Such verification must be conducted before working with new data partners or introducing new data collection methods yourself. It needs to become a standard operating procedure to ensure everyone knows your best-practices and is evaluating data sets consistently.

2. Set up ongoing data monitoring

Despite your best-practices, bad data can and will creep in. Even if your data collection and sourcing technique is flawless, age will render your records less valuable. Data partners can modify their collecting techniques or methodologies, which can then have an impact on your performance from using that data.

Instead of waiting for data quality to become an issue, set up regular monitoring of your data, either automated or manual. Also, conduct regular re-audits of your data partners to verify what has or has not changed in their processes.

3. Rinse and repeat

If you repeat those two steps, you'll have a clean, robust, low-risk data set you can rely on for your marketing campaigns.

Don't let bad data ruin your ads

In a perfect world, ideal prospects would see the most persuasive ad at exactly the right time, and they'd convert.

That perfect world is not so far-fetched: Data can help you accomplish that with ease—if it's good-quality data.

Consider this situation: You source data from a partner that did not provide a clear opt-in during the original data collection. That partner also wasn't transparent with the user about how the collected data would be used. You try to contact the prospect, but you're met with annoyance and frustration because, of course, he or she never consented to being contacted by you in the first place. That prospect does not turn into a lead, which obviously is a waste of your time and money. Worse, you could face potential compliance issues as a result of your using data that was collected noncompliantly.

Now imagine this scenario: A Google ad reaches your ideal prospect, who sees it and, upon being presented with a clear opt-in, signs up for your mailing list. Your sales team speaks with that person and knows exactly what the prospect's needs are thanks to the clear consent and clean data about that prospect. The prospect converts, and your marketing team knows that Google Ads are a great way forward thanks to clean data collection and clear consent from the prospect to be contacted by you. Big Data for the win!

By rigorously validating data sources, cleansing current data, and setting up procedures to avoid poor data collection risks in the future, your ads will increase in both effectiveness and ROI.

More Resources on Data Quality

The Key Ingredient of Marketing Campaign Success: Quality Data

How Bad Data Hurts B2B Companies [Infographic]

ABM, Martech Trends, and the Importance of Data Hygiene: DiscoverOrg's Katie Bullard on Marketing Smarts [Podcast]

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Bad Data Is Ruining Your Ad Campaigns—Here's How to Fix It

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image of Timur Yarnall

Timur Yarnall is the founder and CEO of Neutronian, a SaaS company that provides data quality and compliance verification services, including NQI Certification, a comprehensive data quality, compliance, and transparency certification,.

LinkedIn: Timur Cuceloglu Yarnall