Big Data Analytics and InnovationData has been at the forefront as far back as one can remember — dominating every piece of human existence. Organizations have tapped into the vast sources of knowledge, readily transforming the face of digital revolution. Big data Analytics has had its share of controversies but the so called buzzword has now taken the center stage in most business circles— more so with the growth of information. Big data is growing at a rate of 42% CAGR and is expected to touch 40 Zettabytes (40 trillion gigabytes) of information by 2020. Further, Big Data is a frontrunner when it comes to adding business value and almost 75 percent of organizations are keen on including it into the scheme of things.

In the current scenarios plagued by volatility, uncertainty, complexity and ambiguity, ability of an organization to make sense of the data will be a key differentiator for them. This is more so because the product life cycles are diminishing rapidly which calls for getting the new product development process not only right the first time but also crashing the time to market. Research shows that 90% of all new products fail due to lack of understanding of customer needs.Although lots of data is being collected, there is limited effort by organizations to engage employees to make sense of the data and build new products and solutions that can overcome some of the emotions and frustrations that a customer might be repeatedly communicating with them. How often are you as a customer irritated with the airline for not delivering your priority checked-in baggage or for that matter your preferred choice of pillow in a hotel room?

Big Data Analytics and InnovationSome solutions attempt to take surveys that are largely quantitative in nature because there is apparently a comfort established with data. Some of these tools are classified in the adjoining graphic where on one end you have Fast data tools to Big Analytics and finally the ones that offer Deep Insights. Quality of these tools in terms of moving beyond the obvious improves as we move from left to right.

Fast data techniques like Hadoop, Teradata, etc. provide the ability to see most of what you know in a short enough time in order to quickly take actions. These techniques have grown exponentially faster barely keeping up with Big Data growth volume. Big Analytics tools like SPSS, SAS on the other hand help to turn information into knowledge using a combination of existing and new approaches. Finally, Deep Insights take us a step ahead where all the information at hand is considered be it qualitative or quantitative, analytics processes are applied to it and new knowledge and insights get generated for the business specific situation.

Currently, organizations are collecting lots of qualitative data but are unable to make sense of the intangibles that will help them finally uncover some insights to be successful at innovation. Hence the question arises how can one effectively make sense of and use the information provided by the customers on their products and services? With all the noise around big data, how does one use it to extract information to drive innovation? The larger challenge will be to segregate the signals from the noise so that a better understanding of the customers future needs clearly emerge that will further enable organizations to direct their overall strategy and the resulting innovation efforts in a much more effective manner.

Investments made by a firm do bring in hopes, in the form of Return on Investment. Big Data Analytics will help weed out obsolete techniques and bring in programs that actually work. Real time analytics can therefore be gathered for a given region— focusing readily on profit and usability in the long run. Be it the hidden patterns or unknown correlations— Big Data Analytics can help identity each for digging deep into the market trends.  American Express, for example, started looking for indicators that could really predict loyalty and developed sophisticated predictive models to analyze historical transactions and 115 variables to forecast potential churn. The company believes it can now identify 24% of accounts that will close within the next four months. Walmart relies on text analysis, machine learning, and even synonym mining to produce relevant search results. They say that adding semantic search has improved online shoppers completing a purchase by 10% to 15% that converts to billions of dollars. This and several other examples are making a strong case for big data analytics even on the ROI front.

Big Data Analytics and InnovationSome of the leading companies in the world like the Tata Group, Dr. Reddy’s, Reckitt Benckiser, Unilever, Philips are trying to marry data analytics with innovation by providing solutions that give deep insights into what the customers actually mean. PanSensic® is one of these rare multi-dimensional sentiment analysis tool that uses smart lenses to sift through big data and help innovators pick up weak signals of changing consumer emotion. Using PanSensic, one can read between the lines of narrative for a brand new level of understanding.

The collection and analysis of big Data will serve as a critical input into helping organizations during their strategy creation process. Increasingly it will become a necessary competency for people within organizations as they identify their growth drivers and build new products and services. Just as computer literacy has become a necessary skill in the last century, making sense of big data will become a necessary skill set in the current century. And those companies and individuals that build this capability early will have the first mover advantage…