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BigDataWordleReal-time analytics, human-language query…, it’s the disruptive innovation that is missing from the Big Data story that’s most important. Stories of technology developments today focus on the proliferation of data, growth of information, and IT management solutions.

eMarketer Digital Intelligence on July 19th published the story: Online Data Collection Explodes Year Over Year in US. The database giant, Oracle, on July 17th shared a report: From Overload to Impact: An Industry Scorecard on Big Data Business Challenges. Neither of these stories provides insight into the future need for flexible, configurable and customizable analysis platforms.

I’m not talking about SAS, or SPSS or Cognos –like tools for analysts and statisticians. I am suggesting we need tools that can make Big Data available, useful and interactive to an entirely new consumer. A Big Data consumer that is less sophisticated and is a much larger group by orders of magnitude. I am suggesting truly disruptive innovation around Big Data.

Big Data Current Focus

The focus of the majority of today’s Big Data news stories and reports is on three fronts. First is the deluge of data that is being collected at an ever-alarming rate. Second is the IT perspective and  technology that is advancing to support the collection and data management.  And, third is the promise that this data has for us. It is this third area that is the weakest.

The Oracle report’s initial key finding is that 94% of C-level executives indicate their companies are collecting more data than two years ago. Much of that data the report says is customer information, and secondarily operations, sales and marketing. This sounds much like what was being managed 25 years ago.

The eMarketer article identifies (web) widgets, ad networks, supply-side platforms, exchanges, and demand-side platforms as the top US data collection volume growth channels. It points out that data is the focus of new ad platforms for interactive and transaction-based business. Their focus is on the collection of enough user/consumer data in order to statistically drive relevant results.

Disruptive Innovation

Oracle describes the technology advances for enterprise platforms as primarily focused on data collection, storage, retrieval, and further automation and performance improvement.  This is age-old incremental innovation, not disruptive. The focus is generally around moving existing enterprise Big Data pushing the capacity of its current containers to the cloud.

For marketers the platforms are proprietary tool sets that campaigners engage in to extend reach and measure the social conversation. This demand has grown as they collect customer relationship management (CRM) data. And, as they need to process leads through the pipeline from prospect development to closing sales and ongoing account management.

This activity is critical and important, but is not true disruptive innovation as Clayton Christensen defined. To be truly disruptive, Clayton tells us that: “innovation that is disruptive allows a whole new population of consumers access to a product or service that was historically only accessible to consumers with a lot of money or a lot of skill.”

Big Data Platform For A New Group

To unleash Big Data for this new group of users, or consumers in Clayton’s terms, we need new tools. We need tools that can process and serve information to mobile devices and other new access mechanisms. We need technologies that re-invent SQL query into human-language access. We need to engage with information in a new way. This new way has to be as radical as the way the iPhone and the iPad re-invented the human-machine interface.

The new Big Data consumers in disruptive innovation will want access to real-time answers. These answers will be processed in real-time, from Big Data and other syndicated information. Consumers will make family-economics decisions, entertainment choices, and queries in ways we haven’t begun t fathom. The new consumers will also be in corporate middle management, line workers and staff. They will need to intelligently act and perform optimally when upper management is not available.

The potential to have the rigor of decision making and statistics-based logic in the hands of consumers is hard to grasp. However, it is obvious that it will happen.  What we need is the operating system that can enable these applications to be built. Such an operating system or platform will make this primary focus shift happen faster.

To Ponder

We can all agree, the volume of Big Data is going to continue to increase as  it gets easier and easier to collect it. New digital and connected tools that consumers carry, and the proliferation of sensors on everything stationary or in transit (think your car, packages being shipped, and so on) are everywhere. Additionally, as emerging economies jump on Big Data, this geographic factor will drive need for access and for disruptive analysis platform tools even higher.

Could IBM (SPSS, Cognos) or SAS re-assess their market segmentation and develop a new strategy for this new group of Big Data consumer? Who else might have the statistics, analytics, real-time, and understanding of the less-sophisticated Big Data consumer’s persona in mind?

Share your thoughts on the need for an analysis platform. Do you believe that a new platform can re-invent the value proposition of Big Data? Can a platform that is not focused on sophisticated corporate users get traction? Will a new group of data consumers in the context of Clayton Christensen’s definition of disruptive innovation be ready?.

Please share your thoughts and comment.

4 Responses to Big Data Disruptive Innovation – Platform For Analytic Apps

  1. Jan-Kees says:

    Stein, great article, want to let you know we’re building it, at www dot synerscope dot com, just that for human-language you read human-visual-language also know as map making. In our case high-data-density interactieve maps that allow sliding and dicing of data with an absolute no-code method. The reason we choose for images and not language to describe patterns lies in the human brain: its parallel image processing capacity is much faster than our sequential language processing section of the brain. Your always invited to come and have a look.

  2. […] Real-time analytics, human-language query…, it’s the disruptive innovation that is missing from the Big Data story that’s most important.  […]

  3. […] Real-time analytics, human-language query…, it’s the disruptive innovation that is missing from the Big Data story that’s most important. Stories of technology developments today focus on the proli…  […]

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