Business data growth rates have been increasing exponentially in recent years and will continue to do so for the foreseeable future. "Big data," as it is termed, has become a big challenge as organizations look to revamp their infrastructures and processes to scale to accommodate steep growth rates without the equivalent cost curve. With that in mind, ESG Lab validated the real-world performance and functional capabilities of the EMC Greenplum Data Computing Appliance (DCA). Testing was designed to assess the ease of use, performance, scalability, and analytics-readiness of the DCA platform running an extremely large data set using a real-world retail data model.
The ROI of stockpiling vast amounts of data is directly correlated to an organization's ability to leverage it. In other words, data is only an asset if you can make it one. This is apparent in the evolution of new roles such as the "data scientist" — part hacker, part quantitative analyst — that help companies derive competitive advantages from rich data stores. The more traditional act of reporting is being supplemented with data analytics and data mining disciplines requiring strong mathematical and technological repertoires. The reason for the programming aspect of the role has to do in part with the diminishing use of the traditional data warehouse model as a solution to big data. Data is simply growing too fast and changing too rapidly for architects to organize in advance and aggregate it into a conformed model.