About once every five years or so, the technology industry blazes a new path of innovation. The PC, the Internet, smart mobility and social networking have emerged over the past 20 plus years, delivering new technologies and business ecosystems that have fundamentally changed the world. The latest catalyst is Big Data.

Nearly every major new computing era in the past has had a hot IPO provide a catalyst for more widespread adoption of the shift. The recent Splunk IPO evokes parallels with Netscape, the company that provided the catalyst in 1995 to a wave of Internet computing for both B2C and B2B marketplaces. It ushered in a wave of new innovation and a plethora of new .com businesses. Hundreds of billions of dollars in new value was subsequently created and business environments changed forever.

Big Data refers to the enormous volume, velocity, and variety of data that exists and has the potential to be turned into business value. The challenge of Big Data is taking inhuman amounts of data and distilling it into information that human brains can use. Most businesses accumulate astronomical amounts of data - and the volume is expanding at an alarming rate. According to IDC, the volume of digital content in the world will grow to 2.7 billion terabytes in 2012, up 48% from 2011, and will reach 8 billion terabytes by 2015. [1]

The data flood, of course, comes from both structured corporate databases and unstructured data from Web pages, blogs, social networking messages and other sources. For example, there are now countless digital sensors worldwide in industrial equipment, automobiles, electrical meters and shipping crates. They can measure and communicate location, movement, vibration, temperature, humidity, even chemical changes in the air. Companies wield data like a weapon. Retailers, like Wal-Mart and Kohl's, analyze sales, pricing and economic, demographic and weather data to tailor product selections at particular stores and determine the timing of price markdowns. Logistics companies like UPS mine data on truck delivery times and traffic patterns to fine-tune routing.

Today, a whole ecosystem of new businesses is springing up to engage with this new reality: companies that store data; companies that mine data for insight; and companies that aggregate data to make it manageable. But it's an ecosystem that's still emerging, and its exact shape has yet to make itself clear.

One of the biggest challenges of working with Big Data is assembling it and preparing it for analysis. Different systems store data in different formats, even within the same company. Assembling, standardizing, and cleaning data of irregularities - all without scrubbing it of the information that makes it valuable - is a central challenge of this space.

Of course, Hadoop, an open source software framework derived from Google's Map Reduce and Google File System (GFS) papers, is being leveraged by several technology vendors to do just that. Hadoop maps tasks across a cluster of machines, splitting them into smaller sub-tasks, before reducing the results into one master calculation. It's really an old grid computing technique given new life in the age of cloud computing.

Hadoop is converging with other technology advances such as high-speed data analysis made possible because of parallel computing, in-memory processing, and lower cost flash memory in the form of solid state drives. The prospect of being able to process troves of data very quickly, in memory, without time-consuming forays to retrieve information stored on disk drives, is a big advance that will enable companies to assemble, sort, and analyze data much more rapidly.

For example, T-Mobile is using SAP's HANA to mine data from stores, text messages and call centers on its 30 million U.S. customers to tailor personalized deals. What used to take a week can be done in three hours with the SAP system. Organizations that can leverage this capability to make faster and more informed business decisions will have a distinct advantage over competitors.

In a short period of time, Hadoop has transitioned from relative obscurity as a consumer Internet project into the mainstream consciousness of enterprise IT. Hadoop is designed to handle mountains of unstructured data. However, as it exists, the open source code is a long way from meeting enterprise requirements for security, management, and efficiency without some serious customization. Enterprise-scale Hadoop deployments require costly IT specialists who are capable of guiding a lot of somewhat disjointed processes. That currently limits adoption to organizations with substantial IT budgets.

It will take a refined platform to enable Hadoop and its derivatives to fit into the enterprise as a complement to existing data analytics and data warehousing tools from established business process vendors like Oracle, HP, and SAP. At Zettaset, for example, we are focused on making Hadoop much more accessible to enterprises of all sizes by creating a high availability platform that takes much of the complexity out of assembling and preparing huge amounts of data for analysis. We have aggregated multiple steps into a streamlined automated process, significantly enhanced security, and are now integrating our software into an appliance which can be racked in the data center and easily managed through a user-friendly GUI.

The true value of Big Data lies in the amount of useful data that can be derived from it. The future of Big Data is therefore to do for data and analytics what Moore's Law has done for computing hardware, and exponentially increase the speed and value of business intelligence. Whether it is linking geography and retail availability, using patient data to forecast public health trends, or analyzing global climate trends, we live in a world full of data. Effectively harnessing Big Data will give businesses a whole new lens through which to see it.

Reference

1. Source: "IDC Predictions 2012: Competing for 2020," December 2011