Predictive Analytics has been billed as the next big thing for almost fifteen years, but hasn’t gained mass acceptance so far the way ERP and CRM solutions have. One of the main reason for this is the high upfront investment required in Software, Hardware and Talent for implementing a Predictive Analytics solution.
Predictive Analytics has been billed as the next big thing for almost fifteen years, but hasn’t gained mass acceptance so far the way ERP and CRM solutions have. One of the main reason for this is the high upfront investment required in Software, Hardware and Talent for implementing a Predictive Analytics solution.
As a result, only a handful of very large enterprises such as mega banks or top telecom companies have made the required investments and have benefited from power of Predictive Modeling and advanced Statistical techniques that are in existence for well over five decades.
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Most of the other companies have not been able to levarage power of business analytics as they cannot afford investing in specialized harware, database and BI/Analytics software applications being marketed by enterprise software vendors such as SAS and Teradata.
Well, this is about to change – thanks to technologies such as Apache Hadoop (which supports Big Data distributed applications under a free license), HBase (an open source, non-relational/distributed database) and the freely available R programming language (which is part of the GNU project).
Using R, HBase and Hadoop, it is possible to build cost-effective and scalable Big Data Analytics solutions that match or even exceed the functionality offered by costly proprietary solutions from leading BI/Analytics software vendors at a fraction of the cost. And since R programming language is a freely available Open Source Software, users can leverage work done by others for specific analytics functionality and don’t have to re-invent wheel by rewriting the code. This reduces cost of developing analytics solution significantly.
Established BI and Analytics software vendors have no option other than offering their solution under SaaS (Software as a Service) model so that it is cost effective for their customers to implement analytics solution without requiring large upfront investment. This is all the more important for Big Data as the field is evolving rapidly. And if any BI or Analytics software vendor fails to adapt to this changing technological environment, they risk losing their market share.
