We are already excited about DatSci Awards 2018! This year there will be 9 Award Categories which will be judged by some of the country’s finest Data Science Leaders! Julien Goretti , Global Executive Director of Intelligence – Risk & Reputation from Storyful is one of our 2018 Awards Judges.

We recently caught up with Julien to see what his thoughts  on what is a data scientist? So here is what Julien had to say:

We can find many answers online to a very simple question: What is a data scientist?

One is given by Technopedia and says, “A data scientist performs data analysis on data stored in data warehouses or data centers to solve a variety of business problems, optimize performance and gather business intelligence.” It further goes on to say, “Data scientists are equipped with statistical models and analyze past and current data from such data stores to derive recommendations and suggestions for optimal business decision making.”

Going off this profile, a data scientist seems critical to any company’s success and can drive its performance. But is that enough?

Companies have always paid attention to their reputation and how to manage it. While brands could still mitigate risks and impact how their message was distributed 20 years ago, social and digital media have changed everything. Today, any brand or organization on social media has an incredible opportunity: live and direct access to their fans and potential customers. Unfortunately, one misstep and this incredible opportunity turns into a nightmare. It has never been easier or faster to reach an audience—but that also means brands are constantly under threat of a potential crisis.

Deltai Airlines

Image retrieved from Wikipedia

Let’s take a concrete example. Following the school shooting in Parkland, Fla., Delta Airlines announced on social media its intent to end discounts for NRA members and remove the Delta logo from the NRA website. Delta knowingly entered a contentious political debate in response to a national tragedy and surely expected a backlash. What happened next showed that the backlash, fueled by suggestions that the airline still offered discounts for Planned Parenthood, eclipsed even Delta’s expectation. (Despite Planned Parenthood having no membership program and no evidence of an association between the two organizations, the tweet received 268 retweets and more than a thousand likes.)

One day later, Georgia’s Republican Lt. Governor Casey Cagle vowed to kill any legislation that would grant tax benefits to Delta in Georgia. Several prominent Georgia GOP politicians, including House Speaker David Ralston and Republican gubernatorial candidate Clay Tippins, said a bill containing a $50 million sales tax exemption on jet fuel, which would have primarily benefited Delta, should not go through. The bill was ultimately passed, with the exemption stripped.

Delta’s decision dragged them into a national struggle of political ideologies that materially affected their bottom line. While other NRA-affiliated businesses (Hertz, Avis, Nationwide) seemed to avoid the fray, Delta was thrust front and center. Delta’s original tweet now has more than 600,000 interactions. Tens of thousands of users have commented on Reddit threads and Facebook. National and international press are running stories. All of which traced back to a few local voices in Georgia.

In retrospect, Delta probably did not correctly assess the backlash they would face and did not deal with it with the right way. Granted, it’s easy to say this after the fact. The real question is “How could we mitigate this kind of crisis before it becomes one?” That question could be answered by data scientists. We have at our disposal thousands, if not millions, of data points around PR crises over the past five years and could analyze how each one developed across platforms.

If data scientists are about using past data for predictive models, why not try to predict PR crises before they go beyond a company’s control? Imagine a model that analyzed a live stream of social media data to identify the threshold volumes that predict a crisis. These thresholds should mix quantitative and qualitative data for accuracy and relevancy because, as we all know, it’s not only the amount of conversation that matters, it’s also the context. A nice challenge for a data scientist, wouldn’t you say?

If you feel you have what it takes for you / your team to be recognized for your outstanding Data Science achievements – be sure to submit your entry before May 25th 2018!