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Big data has made a huge impact on how data can be used efficiently to bring a change in the business processes and decision making for improved profitability. Data driven business decisions bring competitive advantages and improved results in terms of customer acquisition and satisfaction levels. Organizations want their IT teams to develop competencies to be able to unlock value from their operational databases and derive useful insights for the board.

Even though this need for data analysis is quite evident but IT teams are not always fully equipped technically and even fall short on budget while trying to implement analytics infrastructure for the business. This comes at a time when there is a strong requirement for real-time, machine to machine analytics with self-serving abilities to fulfill the demand for updated BI.

Also with the changing nature of data sources (structured, unstructured, real time) and business demands, internal IT teams find them incapable to fulfill advanced reporting and prediction needs for the business.

To tackle this problem, an entirely new approach to analytics and insight provision is to be adopted. Analytics as a Service has emerged as a viable option for firms that have data management and analysis requirements without the technical skills and the capital expenditure required to keep them on-premise.

Analytics as a Service (AaaS) is cloud based delivery of analytics which is highly scalable with no upfront capital expenditure and available on demand. For some cases, AaaS is the best solution especially for firms with huge big data workloads. Many public cloud providers offer templates for big data platforms like Hadoop which can be used to set up the required infrastructure.

Even for firms with limited workloads, going on to the cloud could be quite useful since they always have the pay-per-use model. Many firms with traditional business intelligence on-premise setups are thinking of moving to the cloud model given the cost effectiveness and the size, throughput and performance of the cloud version.

The only concern that has come as a resistance from most firms is the security aspect. Transferring Terabytes of customer or business data on to the public cloud sounds like a terrifying idea to most. However with improved encryption and security features this resistance might ultimately fade away.

Have you given a thought to revamp your analytics infrastructure? Do share your thoughts in the comments below or shoot us an email at .