How Social Driven-Design Innovation Can Help Us Uncover Insights Beyond Black and White

Seeing Beyond Black and White

Consumers are taking to various online platforms to discuss, compare, champion and complain about any and all types of products. They do so in real time. Wrangling this information and analyzing the behavior behind the consumer’s commentary, companies are able to discover their next great product innovation. The process by which this happens is what we call Social Driven-Design Innovation. In a nutshell, it is a process that unearths a consumer’s unarticulated needs and identifies their compensating behaviors.

Companies that accrue massive data know that it is no simple task to analyze their own data. The difficulty most companies have in accessing insights through big data is their inability to sift through the massive amounts of chatter, whittle down the big data to discover the hidden patterns that matter, which must be done to access the marketable data. Now imagine the complexity involved in analyzing a major news event that dominates national and international media outlets.

In previous blog postings, we have discussed how Social Driven-Design Innovation can help solve problems as they apply to product innovation. However, In this posting, we are going to step back from product innovation, and think about how the same scientific approach that could help create tomorrow’s next great product, could also be applied to draw insight from two incredibly complex social events that dominated the U.S. news in the early Summer of 2015.

The first event was the rioting in Baltimore. The second incident was the biker gang shootout in Waco, Texas. Both underscore why typical sentiment analysis is not enough to discover true insights.

Some similarities between Baltimore and Waco were obvious. Both events were marked by wanton violence. In Baltimore, stores were burnt to the ground, rocks were thrown at police. In Waco, nine motorcycle gang members were killed, many by police, and at least 18 others were injured. But what caused the problems in Baltimore and Waco? Were those causes similar or dissimilar?

The riots in Baltimore were predominantly labeled a black affair, while the biker gang melee was labeled a white one. Both events drummed up a fair share of social commentators, who had all sorts of theories to justify why these events happened. These theories included the breakdown of morality in modern society, a lack of father figures at home, high unemployment, kids watching too much TV, and more. These were opinions of course – not necessarily backed by data.

Moreover, much of the social sentiment was divided along racial lines. “When are we going to start asking how many of the (people) in the Waco slaughter grew up in single-parent homes? Oh, that’s right,” one person posted cynically on social media.

We have to ask ourselves this: do we have data to make a truly comparative model of both events from which we could draw meaningful insights? Could we go beyond a black and white sentiment analysis?

The problem is that the data sets that might describe the sociological events, such as those that took place in Baltimore and Waco, are simply too big for the unaided mind to analyze without computational help. You need a way to scientifically make true sense of the massive amount of data surrounding both events.

Moreover, at some point, you have to scientifically filter that data like a pot of coffee. After all, if you were going to truly compare the events in Baltimore and Waco, you have to remove race from the equation, a bitter grind indeed. Big Data, it should be noted, is a colorblind coffee maker of insights – and we prefer our insights bold flavored.

So how do you cluster the social sentiment behind both events? How do you filter out the noise? Where might there be true insight into the dynamics of the national events, which could truly serve to support a comparative analysis?

When you consider both events from that point of view, a comparative analysis of these events, is a big data task – and also, a clear case of why we need to go beyond typical sentiment analysis. Knowing who is angry does not tell us much. Knowing why they are angry, tells us much more.

A big data scientist might ask if we even have the data to make an informed analysis? They might ask another question too: can that data be curated from both events? What data sources would we have to model both events to generate a scientifically comparative analysis? Could we simply analyze all the twitter postings about both events? Could we draw insight as the events were unfolding in real time? Could we cluster the data? Could we detect patterns in the data? Could we identify a topology that describes the social networks surrounding both events?

Moreover, could we leverage social scientists to make learned conclusions based on computational models rooted in tried and true science? What are the unarticulated needs of the masses that are left hidden in the chatter? What are the compensating behaviors of the under-served, the under-privileged? Is rioting a compensating behavior? If so, for what unarticulated need?

The sheer amount of the data and the speed at which it was generated would have been a huge obstacle to traditional data scientists. However, with the advent of big data – The Age of Big Data – which we now live in – we have the computing power, and the storage capacity to analyze and preserve the massive amounts of data that might enable us to to draw some deep, perhaps truly comparative, meaningful insight from the tragic events that took place in both Baltimore and Waco.

Using big data algorithms to analyze both events on a statistically equivalent playing field, we could derive meaning with a degree of color blindness that would previously have been impossible to filter out from any one person. This is the power that big data has the potential to deliver.

Scientifically analyzing the behavior behind the events that took place in Baltimore and Waco and captured the attention of a nation, is a big data task. The big data differentiator for us, when it comes to product innovation, is our approach called Social Driven-Design Innovation. Social Driven-Design is a multi-faceted approached that leverages scientific expertise, big data tools, and distributed analytical resources. A similar approach could be used to draw meaningful insights from the tragic events that unfolded in Baltimore and Waco. These insights could help inform, if not innovate, social policy, and they would go far beyond a simplistic black and white sentiment analysis.

Where To Learn More About Social Design-Driven Innovation