With Big Data 3.0, Marketers Will Finally Strike Gold
Until recently, Big Data has been a huge deal in marketing circles mostly because of its potential, rather than its present benefits. Like prospectors who occasionally find a hefty gold nugget nestled in a panful of gravel, analysts and marketers understand the potential value of the insights Big Data contains; the challenge is to extract actionable insights. The first iteration of Big Data—let's call it Big Data 1.0—didn't quite live up to its promise, and neither did the second iteration, Big Data 2.0. But the next generation—Big Data 3.0—is poised to finally deliver the mother lode marketers have long sought.
To put the evolution of Big Data into its proper context, it's important to recall that Big Data 1.0 came about because technology finally allowed us to capture a ridiculous amount of transactional or event-based data. But analysts quickly found that the dataset was overwhelming, primarily for two reasons: First, the scale was mind-boggling—imagine an Excel spreadsheet with millions if not billions of cells populated to get a sense of the challenges involved in parsing the numbers to yield meaningful insights.
And secondly, the complexity of the data was also hard to comprehend. In the pre-Big Data world, we grew accustomed to thinking about datasets as tables with columns and rows, numbers that could be expressed as graphs with an X and Y-axis. Encountering a dataset with multi-corollary information was disconcerting, to say the least. Moreover, it soon became clear that multiple relationships exist between patterns in the data, and that the sets go beyond two or three dimensions; they are multidimensional.
We were still perfecting the art of multidimensional analysis and visualization when Big Data 2.0 came about, driven largely by advances in natural language processing and sentiment analysis. Big Data 2.0 promised to be an improvement over its predecessor by featuring technologies that could detect emotion and sentiment in text—interpreting the feelings contained within tweets, posts and other text-based social media.