We interview MediaScience’s Artie Bulgrin to get his thoughts on quality research data and how you can stay ahead of the curve.
What does the term “quality data” mean to you?
In simple terms I would say Quality Data means:
– Data that are clean, representative of a known frame or population and a known time period of collection.
– Projectable and stable over time with a minimal and known range of error and bias.
– Ultimately fit enough to make reliable, confident decisions.
Do you believe the current “big data” movement has compromised the integrity of today’s media research standards?
I strongly believe that the “big data” movement has taken attention away from media research rigor and advancement. While advanced data solutions for advertising have value, it was also expected that return path data and census analytics alone could be a substitute for cross-platform measurement. That has now proven not to be the case – we need hybrid solutions. In the meantime we have fallen further behind the consumer and the blind spots have grown.
You are known for developing innovative “out-of-home” methodologies for measuring viewing behavior across different screens and locales. From your viewpoint, how far away are the media and technology industries from accurately capturing the current state of fragmented consumption?
We are close, but we need persistence, competition and continued innovation. When TV and Radio measurement first began, the industry used diaries. Then competitive innovation produced passive meters and people meters. When ESPN first started cross-platform research we relied on survey’s. That insight created an appetite for more and better data. Today, innovation has produced better technology and hybrid solutions to measure most platforms. But it still isn’t easy or efficient. The challenge is that implementing measurement is now a team sport, requiring cooperation from publishers, distributors and platforms. We need more cooperation and innovation to produce simpler, passive solutions and universal standards. We are very close, but the industry has to be on the same course.
What are your concerns, if any, about the increased use of DIY research solutions by media brands?
Every media company knows more about their consumers device usage than anyone – because they have the analytics. They can and should use that data to move their business forward. But one concern is that having this limited proprietary data may have reduced incentive and urgency for broader industry measurement advancement and instead increased marketplace complexity with disparate data sources. The problem remains lack of standardized measurement and transparency: Understanding totality of usage, competitive positioning and people vs. just devices. This affects everyone in the media marketplace, but mostly advertisers in terms of their inability to properly plan and evaluate the performance of campaigns across media brands and platforms. The simple, but critical, metric of unduplicated reach is now nearly impossible to measure.
What are some of the best ways for up and coming data/analytics professionals to learn and stay ahead of the curve of audience measurement?
Get out, get involved, listen and learn. Hopefully the company you work for has access to organizations like The ARF, The Media Ratings Council, the IAB, CIMM and others. Attend their conferences and go to their websites to read papers and watch presentation videos. I would particularly urge young professionals to read the MRC’s minimum standards document. Also, some of these organizations, especially the ARF, have educational and networking programs just for young professionals.
You are also instrumental in pioneering the use of authentic lab-based experimental design in the world of media research. If you had an opportunity to design a “dream lab” with an unlimited budget, what would it include?
I have been working with MediaScience for the past ten years, first as a client and now as an employee. The methods, tools and technology used in our labs today have improved significantly over ten years and that innovation will continue. The dream would be to have more labs to add scale and representation of different regions of the country.