Technology innovation and its adoption are two critical successful factors for any Business / organization. Cloud computing is a recent technology paradigm that enables organizations or individuals to share various services in a seamless and cost-effective manner. Business Intelligence for organizations, on the other hand, is becoming a growing need to understand their business insights and trends. Currently organizations are trying to leverage BI to maximum extent; however, there is a big gap in turning BI outcome to aid their ROI expectations and further growth. This necessitates porting current data analytic applications on to the cloud due to its ability to process large datasets as well as extensive support for scalability at low cost. This article brings out the technology challenges and opportunities to enable analytics in cloud environment, which makes BI affordable for all organizations.

1. By
AJITHKUMAR RAVI

2. • WHAT IS A CLOUD?
• ADVANTAGES OF CLOUD.
• SERVICES PROVIDED BY A CLOUD.
• WHAT IS CLOUD ANALYTICS ?
• ANALYTICS FOR BUSINESS INTELLIGENCE.
• ADVANTAGES OF CLOUD ANALYTICS.
• CHALLENGES OF CLOUD ANALYTICS.
• PUBLIC VS PRIVATE CLOUD.
• CONCLUSION.

4. • A major advantage with cloud is the
recoverability in case of disaster.
• Cloud uses the concept of Utility Computing.
• Cloud provides analytical services to the
clients.

5. IaaS
• Infrastructure as a service
PaaS
• Platform as a service
SaaS
• Software as a service

6. Cloud Analytics is term for a set of technological and
analytical tools and techniques specifically designed to help
clients extract information from massive data

7. AaaS
MaaS

8. • Analytics as a Service provides clients with analytics on demand.
• They pay for the usage of the analytic solutions as a service by CSP.
• The idea here is that, CSP lists analytic solutions as a service .
• The customers pick required solutions and leverage it for their specific
purposes.
• This helps in drafting product roadmaps based on market analysis.

9. • Models as a Service provide clients with building
blocks to develop their analytical solutions by
subscribing to the models available over a cloud

11. • Since the CSP provides analytics for the clients before they place their
product on the cloud, product becomes more successful in the market.
• Obviously the organizations get more profit as their product becomes
successful.
• The product becomes more reliable.

12. • Tuning Knowledge models
• Ensuring Privacy
• Supporting Column Based Indexing
• Ensuring Data Availability
• Ensuring Data Quality
• Ensuring Data Currency

13. • Tuning existing models for a particular application
being served over the cloud will enable the clients to
leverage existing skill sets to customize the model to
their particular requirement.

14. • The data or knowledge Analytic services may be
processed outside the client’s premises.
• So, one needs to ensure the privacy
of the data as well as knowledge derived as a service.

15. • Most of the current relational databases being used by the clients are row-
based.
• Existing models have to be augmented to support column-oriented
databases.

16. • Data analytics are data intensive applications and hence required new
mechanisms when one or more nodes of a cloud fail.
• Cloud should have the capability to recover and progress in the event of
multiple node failures.
• Specialized file systems are needed for cloud units to handle such
failures.

17. • It is a reported fact that, in some systems, the output of a query is most of
the times incomplete due to poor quality of data.
• Adopting similar situations over the cloud, the data residing on multiple
and highly distributed processing units creates poor data quality that may
not be suitable for analytics.

18. • In Data Analytic applications, by the time models are generated from the
data that is available, there might be more recent data on which the model
built might stay invalid.
• Cloud can tackle this problem by reducing the cycle time between data
availability and model generation.
• Specialized algorithms which enable fast incremental learning model
generation over cloud need to be explored.

20. Cloud computing is a recent technology paradigm that most of the
infrastructure and services industries are focusing to
capture potential opportunities. Analytic over cloud enables organizations
to realize their analytical needs in a more affordable
manner.