Science Project

Data ScienceBy now, everyone in the advertising universe is familiar with “Big Data.” If used wisely, it fuels device-driven acquisition campaigns to produce high returns with minimal media spend, and optimizes consumer engagement. But any marketer can scan a dashboard or spreadsheet and glean meaningful audience intelligence with a snap of the finger, right?

Mmm, not so much. Big Data is still in its infancy, and creating head-scratching problems for many. Especially when utilized for cross-screen purposes. So who are the individuals that are vetting and deciphering this data? Well, the Data Scientist is one key player.

Data Scientists are pushing the envelope of today’s advertising technology through deep analysis and interpretation of device signals, taking raw data and producing “actionable knowledge” and insights for the rest of us. We sat down with BlueCava’s Chief Data Scientist, Andrés Corrada-Emmanuel, to help better understand what Data Scientists are doing for Adtech, and what they’re bringing to their employers’ tables.

Q: What are the problems that every Adtech provider is facing with regards to cross-screen data?

Andrés Corrada: Computers and devices have exploded the ability to collect raw signals. There are huge amounts of them. The issue that we have… how do we interpret them? How do we detect consumers and what time of the day they purchase online products, and still respect their privacy? For example, when do consumers typically purchase travel insurance? We discovered the answer to this question is a Tuesday night!

Data is information, not knowledge, that’s the issue. It’s usually an organic thing – think of a primitive sign. But almost always we are interested in the underlying phenomenon that produces that raw signal. Example: a doctor takes a patient’s temperature (the data signal) but what he/she is really after is the cause of illness (the phenomenon).

Q: What are Data Scientists doing to remedy these problems?

AC: Data scientists in the Adtech industry want to create actionable intelligence from past (and real-time) data. Even though Big Data is a buzzword right now, it does point to an important trend in the industry. Big Data pioneers like Google have developed data pipelines that scale to all RTB requests seen in the ad exchanges, impression streams of clients, and other signals. “Data trumps algorithms,” as famously put by Peter Norvig at Google. And innovations like the Cascalog framework by Nathan Marz, formerly from Twitter, have allowed us to quickly focus on data insights rather than data plumbing. Each has played a role in the advancing Adtech industry.

Here at BlueCava, we are focusing on two things with respect to Data Science: privacy and cross-screen. BlueCava is a leader in developing methodologies that respect consumer privacy while measuring cross-screen technology’s accuracy and errors. We are taking the raw data signals (impressions, RTB requests, etc.) and identifying households, the separate consumers within, as well as their multiple devices, and then measuring our accuracy for these associations against the whole marketplace. And the magic here is that we can now do all of this without the use of PII (Personally Identifiable Information).

Q: Name two things that the average marketing executive doesn’t know about data science.

AC: 1. You can measure how well you are doing (your accuracy and errors for a given campaign or analysis) without the use of PII.
2. You can use data science to outrun the competition.

When it comes down to it, data is meaningless if we do not have the tools (and technicians) to understand it. And as device proliferation (combined with cookie inflation) fragments the advertising space, this data continues to become an increasingly difficult asset to understand and measure while respecting consumer privacy. The Data Scientist is combatting this, “measuring the immeasurable” and playing a key role in the evolution of advertising technology and privacy-centric consumer insights.