Minority Report could one day be real, thanks to big data and predictive analytics [Q&A]
Everyday humans create 2.5 quintillion bytes of data, according to IBM. This data comes from virtually everywhere -- sensors used to gather environmental information, posts to social media sites, cell phone signals and more. In order to best determine how to put this data to use in meaningful ways, the science of analytics has changed (think: the amount of data that can be analyzed, the tools and methodologies that enable us to do so, etc.). Most importantly, companies can now be predictive based on data analytics, enabling them to better forecast demands and proactively prevent certain issues.
I spoke to Murali Nadarajah, Global Head of Big Data Analytics for Xchanging, a publicly listed multi-national business technology and services provider, about how organizations today are using predictive analytics, and how the ability to be predictive has -- and will continue -- to change the business landscape enabling the development of new approaches and products.
BN: Tell me a little bit about predictive analytics. How is it different from traditional analytics?
MN: Predictive analytics is like traditional analytics with a built-in lens into the future. The proliferation of things like social media and all-things-mobile have massively influenced the speed at which consumers make buying decisions and interact with brands. These forces have created an incredible amount of data, which best-in-class businesses are not only collecting and reporting on, but are identifying causal relationships between data points and thus, predicting future outcomes. And best yet is that there’s an incredible degree of accuracy in it.
BN: This all sounds very "Minority Report" -- the sci-fi movie based in the year 2054 where a specialized law enforcement team is able to arrest people for crimes they haven’t yet committed. Is that where we’re headed?
MN: We’re not there quite yet, but I can’t say it would be impossible in the future! That type of knowledge could possibly exist, and it would be provided by big data. Although being arrested for pre-determined crimes is still a ways off, there are a number of practical applications of predictive analytics that are in-play today.
BN: Can you tell us about some of those?
MN: Absolutely. Tonal analysis is an example I find particularly interesting: Historically, call centers have measured customer satisfaction based on metrics like average handling time, call abandon rate and customer surveys. With big data, we can actually listen to, and analyze, the tone of a customer’s voice over the course of a call. Based on this, we can gauge whether the customer called in angry and left the call happy, or if they started the call neutral and left unhappy, and so on. This is Tonal Analysis and, when it’s combined with existing customer care reports can provide a much higher level of preciseness that you wouldn’t have had with traditional reporting.
To take this one step further, Tonal Analysis becomes predictive when it’s used by industries like debt recovery. Debt collectors can call an individual, tell them how much they owe, for example, ask a few questions and then, based on the individual’s tone during the call, can predict the likelihood of recovering that debt. This allows the debt collector to focus their efforts on money they have a greater chance of seeing again.
BN: Interesting. Broadly speaking, how is the ability to predict changing the business landscape and how companies operate?
MN: There is an absolute plethora of data around us -- more so than ever before. For most companies, managing this data has become vital to building competitive advantage, sustaining and identifying new areas for growth, innovating and managing evolving customer demands. Businesses need to make quicker and more informed decisions to outpace competition, and those who can’t keep up will lose.
The ability to forecast future trends by drawing meaningful and engaging insights from past data has become a game changer for companies. Being able to more or less predict the future is changing how organizations strategize long term, manage customer care and find ways to stand out from the competition.
BN: If we’re already at the point of being predictive, how can analytics continue to evolve? Where does it go from here?
MN: Believe it or not, there is a level of analytics that goes beyond predictive, and that’s what is being called prescriptive analytics. This is the next stage of the analytics evolution and essentially automates a recommendation and steps to take to adjust or correct behavior. In other words, it goes a step further than predicting behavior, and tells you what to do about it.
This rung on the analytics latter -- along with predictive analytics -- is still somewhat nascent, and it’s worth pointing out with predictive that it’s illustrative of what might happen, not what will. So before "Minority Report" becomes reality, we can rest assured that we’re not yet living in a world where our decisions are being made by machines, but businesses have clearly gotten smarter about how they use data to be ahead of the curve, and what’s yet to come will most definitely be worth the wait.