The Internet boom has put traditional data analytic solutions under severe pressure. The business intelligence (BI) approach, which was born in the mid-’70s, no longer scales in the 21st century when organizations are routinely dealing with terabytes of data per day. With volumes of data from user action, pages, ads, tweets, rich media interactions, and other sources mushrooming faster than the efficacy of available hardware or traditional business intelligence approaches, enterprises are hard-pressed to keep up with the flood of data. Trying to address the data analytics scalability problem by throwing hardware at it becomes a losing proposition due to spiraling costs in terms of servers, people, power, cooling, and space.

Truviso is taking a different approach. Offering what it calls ‘continuous analytics’ of incoming data, a technique that enables massive scalability and extreme flexibility in data analysis. Co-founded in 2006 by Sailesh Krishnamurthy Ph.D. with UC Berkeley computer science Professor Michael J. Franklin, the U.S. based, California-headquartered Truviso solves the challenge of continuous data analysis for ‘always-on’, data intensive business environments. The company's software solution processes huge volumes of incoming data to enable continuous analysis, visibility, and action across heterogeneous business and IT systems. Truviso, with its next generation business intelligence solutions, changes the way companies’ data is processed, analyzed, and acted upon.

Next-gen BI Solution

Virtually every organization is facing rapid growth of data volume, with typical growth of data volumes being between 50 and 200 percent annually. More significantly, in network-centric areas such as media companies, social networks, content delivery, security, and others data can sometimes grow in excess of 1,000 percent a year.

In conjunction with this unprecedented data volume growth, net-centric organizations that provide digital services across the Internet are under unrelenting pressure to continue making smarter, faster, and more efficient decisions. These companies, in an effort to stay ahead of their competition, are applying increasingly sophisticated analyses over detailed and complex information. All of this has created a ‘perfect storm’ in which traditional data analytics’ approaches to scalability and performance have become increasingly untenable.