How to define data quality and ensure accuracy

Lotame VP of data solutions Evgeny Popov
By Lotame VP of data solutions Evgeny Popov | 13 September 2018
Evegeny Popov

Global marketing investment is rising, according to a Dentsu Aegis survey of more than 1,000 CMOs worldwide. APAC ad spend has been particularly strong, with Australia, China and Japan spending more than ever on digital. Above other factors, one stands out as a critical engine for this growth - data.

Marketers rely heavily on data to deliver relevant content to audiences, improve consumer experiences, and make educated business decisions daily. This is why the same Dentsu Aegis study found that 80% of CMOs recognise audience data’s importance in effective marketing campaigns. It’s this trend toward “all things data” that has made data’s scale a key industry driver for quite some time.

However, as the data market has reached maturity, shortcuts that help achieve scale are now undermining the quality of data available. Here’s how.

Cookie stuffing

The need for scale has created a situation where data providers add attributes to as many cookies and mobile IDs as possible. This means users with online behavior only tangentially relevant to a brand are being used expand the types of data that “fit” into a certain segment. the practice helps sell more data, of course, but accuracy suffers. Cookie stuffing can sell audiences, but it doesn’t credibly support advertisers.

Mischaracterisation

Another issue is mischaracterization of data. This happens when audience segments are incorrectly identified or labeled. When a person reads an article about Beyoncé that mentions Honda, does that mean that they’re interested in buying a Honda? Does it mean they’re interested in automobiles as a category? Interpretations can vary. Mischaracterization is possible from any data provider, and it happens frequently. The problem is only growing as data has become more valuable.

Moving forward

As the importance of audience data grows, sophisticated marketers are becoming more interested in the quality of data to ensure they’re making the right business decisions while reducing wasted ad spend.

Still, while everyone is now starting to talk about data quality, there hasn’t been a whole lot of work done to protect or even define it. Definitions and standards of quality differ from company to company. Here’s what marketers need to know.

Defining data quality

Do marketers want quality data? Of course. Without hesitation, CMOs will affirm this, then quickly change the subject to the kind of audiences they want to reach and what they want to pay. But what they may not realize is that their ability to use data to reach those audiences hinges on quality. So, what does “quality” really mean in this context? It turns out to be very subjective, because we all use the word differently.

In the absence of a clear definition, marketers are beset with several questions:

  •       Labeling: What are the ingredients? What’s “in” this data set?
  •       Provenance: Where did the data come from?
  •       Efficacy: Does the data work?
  •       Accuracy: Who is it?

When they refer to data quality, CMOs are usually talking about its accuracy.  Marketers are asking if the information contained inside the data files is true and correct. If the cookies say males age 18-34, are you actually reaching that segment? Quality inputs return quality results, so it’s imperative that the data marketers use is accurate. And accuracy needs to be considered relative to both targeting and measurement.

Talk to data providers

To address data accuracy, advertisers need to have candid conversations with data providers. How precise are their audiences and how its validated? How curated are they? How trustworthy?

For buyers, the first step is to demand greater detail from partners about what they’re selling relative to source, offline data versus online, offline-to-online matching, qualifying segmentation and more. This should be the standard information available from any worthwhile provider.

But data buyers can dig deeper. Here are some additional questions advertisers should consider when discussing data accuracy with existing or potential partners:

  •       How human is your data? The best providers have identified and eliminated bots.
  •       How are you testing for cookie stuffing? Providers need to constantly evaluate whether an individual seller is adding too many behavioral attributes to profiles.
  •       How predictive is the data? If the data helps predict multiple different profile attributes, it’s higher quality.
  •       How on-target is the audience? Providers must explore how precise segments are.
  •       How are audiences curated? Data curation is key. How are providers doing it? By time/recency, user-confirmation, predictiveness? All of these things matter.
  •       What about performance? Buyers should get information on demographic results (on-target %) and more.

Delivering on data quality will always be an ongoing process. The more transparency advertisers command about the data they’re buying and the processes in place to ensure integrity, the more confident they’ll be in its accuracy.

Lotame VP of data solutions Evgeny Popov

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