Home On TV & Video Digital Video Can’t Thrive Until We Resolve Its Data Challenges

Digital Video Can’t Thrive Until We Resolve Its Data Challenges

SHARE:

benreidOn TV And Video” is a column exploring opportunities and challenges in programmatic TV and video.

Today’s column is written by Ben Reid, founder and CEO at elasticiti.

Nothing captures the imagination of the consumer – or aids in brand recall – better than that magic combination of sight, sound and motion.

While video is clearly a boon for the digital advertising industry as a whole, we urgently need to get serious about its glaring data challenges before it hobbles the channel.

The first challenge is the lack of a common language or vocabulary within the media industry for digital video data. That, in turn, leads to a lot of painful data munging to clean up or standardize data so that it’s useful. With every data project, the rule of thumb is that 80% of the effort is spent reconciling disparate data sets. But when you enter the video realm and work with data sets from multiple partners, this challenge becomes magnified. A lot.

Our appetite for data has driven us to collect vast amounts of data on each customer interaction with a video ad, delivering a rich data set. But without a standard nomenclature, it’s difficult to assess what really happened. As a result, understandings between parties are hampered.

For example, how should we refer to content that’s only accessible to consumers with the proper credentials? Is it “restricted” vs. “unrestricted”? “Free” vs. “paid”? “Subscriber” vs. “nonsubscriber”? It gets even more confusing when referring to content by service type, such as free content vs. authenticated consumer state. And how do we refer to content that’s distinguished by length, such as short form and long form or clip vs. full episode? What’s the threshold?

Beyond length, can we distinguish between video starts initiated by consumers rather than a video player? Pre-rolls are often included in the former, since they’re shown prior to content a consumer has opted to see. That’s very different from video that plays automatically the moment a consumer lands on a page. Clearly, advertisers will want to distinguish between the two, but to do that we need a standard nomenclature.

Lastly, how should the industry address the many forms of digital TV, such as digital simulcast of a TV feed linear start, digital simulcast of live events or a live stream start? These are often bundled together, but they’re not always the same thing.

End Apples And Oranges Comparisons

Challenge No. 2: Digital data vendors don’t always collect the dimensions and metrics of interest to clients, which means they’ll render data with fields that aren’t easy to interpret.

For instance, different vendors bucket content type differently. And many digital data vendors don’t record program starts with live or linear streams. Instead, the time recorded for a stream is often attributed to the program during which the stream started, even though several other programs might have subsequently been viewed on the stream.

When clients finally obtain the data from all of their partners, they often find they can’t process the dimensions or metrics in the same way because their definitions aren’t standardized. To be sure, this is partly due to the fact that Hulu, AppleTV, Verizon FiOS and other platforms operate differently, but platform differences don’t explain away all of the problems.

Reconciling all of these issues comes at a cost: the loss of valuable time that could be spent doing deeper analysis. For example, instead of writing filtering and aggregation rules multiple times for each data set, what if we could reduce it to one master rule set?

Time To Follow The IAB’s Lead

The IAB has started a data nomenclature project, spearheaded by David Smith at MediaSmith, aimed at solving some of these issues. It’s early innings so the scope isn’t defined. This strategy is the right way to do this since hoping for vendors to work it out among themselves is wishful thinking.

As I said earlier, video is a big deal. It’s really the one area where demand continues to outstrip supply and the performance justifies the demand. But friction anywhere in the value chain is bad for individual parties and the market itself.

With momentum growing around programmatic video, and the blurring of the lines between traditional broadcast approaches and digital paradigms, ironing this out can make the difference between lots of hype but minimal adoption and the hockey stick everyone hopes for.

Follow elasticiti (@elasticiti) and AdExchanger (@adexchanger) on Twitter.

Tagged in:

Must Read

AdExchanger Senior Editors Anthony Vargas and Alyssa Boyle.

POSSIBLE 2026: AdExchanger's Hot Takes

AdExchanger Senior Editors Alyssa Boyle and Anthony Vargas share their takeaways from three days chatting about agentic AI at POSSIBLE.

Reddit Reports A 75% Boost In Q1 Ad Revenue As It Reaches For 100 Million Daily US Users

Generative AI search has pushed traffic off a cliff across most of the internet, but not on social platforms. Reddit included.

POSSIBLE 2026: Can AI Help Agencies Finally Break Down Those Silos?

Domenic Venuto, indie agency Horizon Media’s chief product and data officer, sat down with AdExchanger during POSSIBLE at the Fontainebleau in Miami to unpack the role of AI in today’s media and advertising landscape.

Privacy! Commerce! Connected TV! Read all about it. Subscribe to AdExchanger Newsletters

Google Touts Its AI Ad Tech Adoption And New AI Max Features

Google announced new features and ad types for AI Max, its AI-based bidding product for search and shopping or sponsored product ads. The company also touted “hundreds of thousands” of advertisers using AI Max.

Hand pressing blue AI button on keyboard. Digital collage of artificial intelligence interface.

Meta’s Ad Machine Is Purring, So Why Did Its Stock Drop?

Meta’s Q1 call sounded like an AI and hardware pitch, but under the hood it was still about one thing: investing in AI to squeeze more money out of its ads business.

Alphabet Exceeds $100 Billion In Q1 And Its Profits Almost Doubled

Alphabet earned $109.9 billion in Q1 this year, up from $90.2 billion a year ago. And that’s not even the truly gobsmacking number.