5 Factors for Assessing Data Quality

It won't be long now before the blockchain disrupts online advertising to the point that target segments can pay publishers to skip, fast-forward or mute ads. This and ad blockers have taken off in recent years due to the advertiser's inability to serve its messages at the right target market, and the blatant unwillingness to focus on targeting instead of spraying. The problem is anchored in a lack of awareness around assessing data quality.

Till the blockchain steps in at scale, we as the advertising and marketing community need to step up our game in the system by which data is purchased and sold. At the moment, few agencies bother to verify their partners, with most complacent with the width over targeting depth. The widespread weakness of the data itself, coupled with the unwillingness to ensure quality has in part contributed to the billions of ad dollars lost to fake and low-quality impressions.

It's time agencies extracted value from the data procured, so here are the top five factors and considerations we go through when assessing data quality.

Origins

Data can come from publishers, surveys, CRM data sets, graduate directories and even offline resources. Every data buyer must understand the origins of the data under procurement consideration. Doing so will lead them to question the contextual relevance of the data as it relates to their target segments. Seek to check the validity and the utility of the data for more than one avenue.

In Market Segment

We prefer data that is grounded in the actions of the user, both proactive and reactive. Some advertisers give weighage to data that is segmented by predictive analytics. Others gravitate towards categorical recency and frequency. Whatever the reasoning  while assessing data quality, say it out loud and check with performance marketers who are tasked with delivering on commercial goals.

Scalability

You want to get the most bang for your buck. Similar to segmentation above, the acquired data must be able to serve its purpose across categories. Check if it does and how you would scale it. assessing data quality

Accuracy

Set up systems that ensure your vendors are held to a higher standard. Similar to P&G's Marc Pritchard, inform current and future partners that they must attain certification from the Media Ratings Council or your regional equivalent. Ideally, data that passes a standards test will help planners with any type of campaign goals, and key performance indicators. Often reach is valued over results, so reassess your approach and train your clients to appreciate accuracy.

Contribute

Establish a strong relationship with a data vendor so as to fully realize their data capabilities and cull concerns in your desire for assessing data quality. Doing so unlocks new potential data signals that build a custom audience. Use tools like C2 to segment data that meets campaign requirements, while ushering in an open dialogue with vendors for more transparency and collaboration. Doing so can help your company curate categorically accurate segmentation right from the source.