Unearthing previously unimaginable insights from massive data sets is the premise of all the big data hype. Over the past few years as more and more stories come out about how companies are finding competitive advantages in their data, big data has moved beyond the buzz. Enterprises are deploying big data projects at a faster rate every year, and even more plan to do so within the next 2 years.
The extent to which a company can take advantage of big data analysis is determined by the amount of resources and infrastructure it has available. The good news is that now the barriers to entry have been lowered, making it possible for more organizations to meet their goals to transform operations with insights gained from big data. Here are three approaches that companies of any size can take based on their particular situation.
One thing to note is that these are underlying infrastructure approaches, and that you’ll still need an analytic engine like arcplan on top in order to interact with, visualize and distribute your insights.
Lots of resources and lots of infrastructure
Before big data was “big data,” Teradata was the only game in town. They’ve been at it for so long and their functionality is so robust – some of their capabilities are second to none. Now other vendors like SAP (with HANA) and Kognitio have their own massively parallel analytic databases. They offer robust processing and querying power on multiple machines simultaneously, enable near real-time MDX (Multidimensional Expressions, for OLAP querying) and SQL (Structured Query Language, the standard way to ask a database a question) queries, and in the case of SAP HANA and Kognitio, are fully in-memory. Not surprisingly, Teradata and SAP HANA come at a high price, but for that high price, the insights you achieve can be very near the speed of thought.