With Big Data 3.0, Marketers Will Finally Strike Gold
However, Big Data 2.0 has largely fallen short of its ambition. Detecting human emotion in text sounds great in theory, but it isn't easily done. That's because people tend to express themselves in complex and subtle ways. If you're having a bad day and someone asks how you're doing, it's unlikely that you'll respond with, "My sentiment today is negative and I am feeling frustrated."
However, keywords like "negative" and "frustrated" are what machines look for when identifying sentiment. To properly interpret emotion, technology has to take into account the delivery, including factors like tone of voice, cadence and intonation. Machines haven't figured out how to identify and match the appropriate "delivery" style to raw text.
Because of these shortfalls, both Big Data 1.0 and 2.0 have left a huge gap in the market—a chasm that marketers, ecommerce leaders and business leaders increasingly notice. Big Data technologies today are immensely powerful at capturing quantitative data—they excel in tallying clicks and similar metrics. However, bounce rates, site dwell time, click-through rates and similar indicators are fundamentally just numbers that reflect an activity. They yield metrics but are short on insights.
We predict that Big Data 3.0 will blend the two objectives of its predecessor versions—gleaning insights from both metrics and sentiment. But instead of driving more analytical value out of text, we predict that the market will turn to driving analytical value out of media that is currently ineffectively captured or explored: video, audio, surveys, blogs and shorter forms of text like tweets and Facebook posts.
How do we consolidate all of the market's quantitative and qualitative data into one dataset and run multidimensional analytics across it? Creating a platform that allows you to do that actually provides the context and insight you need to address the biggest problem in Big Data 1.0—understanding the "why" behind all of those numbers—and solves the riddle that has thus far eluded Big Data 2.0 by providing the emotional and behavioral context needed to understand the real sentiment behind what people are saying.