Big Data Analytics
The first steps toward achieving a lasting competitive edge with Big Data analytics.
Now that it's becoming more feasible for IT organizations to work with Big Data, the question that many people are starting to ask is what can be done with all the data. There's obviously a potential treasure trove of information in all that data, but finding it can be a little problematic because the task quickly becomes the digital equivalent of looking for a needle in a haystack.
In addition, some providers of analytics applications are starting to make a case for new tools that are specifically designed with Big Data in mind. For example, Alpine Data Labs is a new company that launched Alpine Miner, a set of predictive analytics applications that runs within the database, as opposed to requiring companies to set up a separate data warehouse.
According to Alpine Data Labs CEO Anderson Wong, this approach not only reduces that total cost of deploying analytics applications, it dramatically improves the performance of those applications.
Alpine Data Labs, which just picked up an additional $7.5 million in funding, was actually incubated within EMC's Greenplum business unit, which Wong says created the perfect environment for building analytics tools that are optimized for Big Data environments. In contrast, Wong says existing analytics tools were designed to work against relatively narrow sets of data, which makes many of them unsuitable for working with massive amounts of data.
Wong says that Alpine Miner is also designed to be accessible to the average business analyst versus requiring dedicated professionals who have mastered the intricacies of the mathematics used to create models within an arcane analytics application.
Clearly, something profound is starting to happen with the advent of Big Data. But now that it's here, the time to start thinking about what to do with it is upon us.