BTR: Data Quality Management Emerges as New Priority for Thriving in Today's Digital Advertising Environment

By: BizTechReports
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TiVo Xtend
TiVo Xtend
SILVER SPRING, Md. - July 27, 2022 - PRLog -- Spending on digital advertisements is poised to cross the $300 billion mark as we approach the middle of the decade. As a result, key players in the industry are in a race to secure strong market positions in this constantly evolving environment. After years of focusing on quantity — in which the industry has captured as much data as possible from as many sources as possible — a new key to prosperity is emerging, says Fariba Zamaniyan, vice president of data and advertising at TiVo.

In a recent podcast interview, she described how leaders across the ecosystems are coming to grips with the fact that capturing, rationalizing and analyzing data to rapidly execute effective advertising campaigns across an increasingly complex digital landscape is a competency that will have to be mastered to enjoy sustained success. In other words, quality is trumping quantity when it comes to securing competitive advantage in advertising.

Quality Over Quantity

Defining "quality" in the context of digital advertising can be confusing because the players across the industry have presented so many interpretations. Increasingly, however, the industry is coalescing around one core concept: identity resolution.

"Quality in digital advertising boils down to how data can be leveraged to isolate and identify specific audiences in an accurate and accountable manner," says Zamaniyan. "Once audiences have been identified in a way that meets this standard, advertising campaigns can be activated so that the right messages and products are presented to the people who are most likely to align with the offerings."

This is not an easy feat to accomplish. The process of accessing large amounts of data to then leverage insights requires a comprehensive effort.

"Information must be collected from a vast array of sources in many different formats and then be aggregated for correlation analysis. Furthermore, not all data is equal. There is often bias in the data modeling and collection process which is problematic. This is why limiting bias is key for effective data analysis and decision-making," explains Zamaniyan.

To read the full interview with TiVo's Fariba Zamaniyan visit:

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