Insights From the Next Wave of Collecting & Utilizing Big Data

Big data has been a hot topic for everyone lately — from content creators to the big agencies trying to evaluate their numbers. As the creation and consumption of content has been changing drastically, companies have been developing new ways to reach their target audience, and new methods for tracking that success.

Here at Skaled, we frequently serve as the connection between startup technologies and the big brands who benefit from their evolving capabilities. We see first hand, the many ways that advancing SaaS options can offer valuable insight to big companies. Startups that produce and interpret big data are popping up everywhere and brands are scrambling to stay on top of it all — trying to acquire more accurate and efficient data than their competitors.

When I look at what brands and agencies need from big data, there are three distinct phases:

First, they want to view the data in a digestible format.

Second, they want recommendations for how to interpret and utilize this data.

Third, they want to view results after utilizing that data and compare it to their baseline.

A product that will appeal to big companies must have all three. You can have a beautiful interface that’s easy to understand and simple to use, but if you don’t utilize a certain amount of data science to interpret this data and make recommendations for moving forward, then you’re simply repackaging audience trends that companies could obtain through other channels.

Big data must be actionable.

It’s the second phase — the recommendations for how to interpret and utilize — that most big data providers fall short on.

I’ve been diving into the world of DMPs (data management platforms) for the last few months and have been amazed by the growth in this space. But what’s been particularly interesting in discussing DMPs with publishers, brands, and agencies has been the question of whether using DMPs has actually made them more money.

The answer is typically an anecdotal “yes”, but in looking at their CPM values (their cost per 1,000 impressions), they’re continuing to dump dollars into RTB (real time bidding). Despite the availability of data, they still can’t turn those numbers into attractive, targeted opportunities, rather than dumping the leftover space into real time bidding where results are a crapshoot, at best.

So why are RTBs continuing to gain traction, rather than true publisher to brand implementation of big data, when the technologies exist and have proven to improve results exponentially?  Laziness is part of it. It takes more time to set up the tracking of your big data — not to mention learning to use these new technologies. But a lack of data science is also discouraging the use of big data as it often just sits there looking pretty with nothing to ignite it’s potential.

Data science is the missing ingredient. It’s what brands and agencies want more of, and it’s what many startup technologies are glazing over. Without the ability to make accurate predictions from big data, you have a huge ocean but not a drink of water can be found.

I predict that mathematicians will make up the next generations of programmers as we activate big data into huge returns for the companies that are willing and able to use it. Excelling in the second phase of  the cycle will be critical for every big data provider to effectively demonstrate their value on both sides.