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Cloud Computing and BI


The three main constraints to BI adoption and a new era of analytic data management for business intelligence are:

  1. Effort required to consolidate and cleanse enterprise data
  2. Cost of BI technology
  3. Performance impact on existing infrastructure / inadequate IT infrastructure

Cloud computing is potentially an answer to two of these problems.

Cloud computing enables organizations to analyze terabytes of data faster and more economically than ever before. And the difference from previous models is that it is delivered in an on-demand basis.

OnDemand IT is not a new concept - and most BI vendors have BI onDemand Solutions often called Software as a Service [SaaS] BI.

Cloud computing is often confused with software as a service (SaaS) models, however it is fundamentally different in infrastructure.

Cloud customers 'rent' dedicated servers and the people needed to house, secure, and manage them. This is considered somewhat more secure than multi-tenant SaaS models in which data from one customer may co-exist with data from another customer within the same application.

Cloud customers have full control over server and firewall settings to ensure security.

 

Benefits of Cloud Computing for BI

Cloud computing environments mean organizations no longer need to expend capital upfront for hardware and software purchases. Nor do they need to suffer through prolonged in-house implementations. In these two areas, cloud computing and SaaS models both share similar benefits.

Cloud customers instead, link into a computing cloud, such as Amazon's Elastic Compute Cloud [Amazon EC2], to have a dedicated, high-performance analytic database cluster provisioned and hosted for them.

The services is provided on a pay-per-use basis, usually for a monthly fee.

 

Transforming BI

Cloud computing is transforming the economics of BI and opens up the opportunity for smaller enterprises to compete using the insight that BI provides. Cloud-based analytics will impact BI by:


Accelerating BI technology adoption - the cloud becoming the default platform for evaluating new software.

Easier evaluation - the cloud enables software companies to make new technology available to evaluators on a self-service basis, avoiding the need to download and set up free software downloads.

Increased short-term ad-hoc analysis - avoiding data marts spawned as a result of new business conditions or events. Where short term needs [weeks or months] for BI is required, cloud services are ideal. A data mart can created in a few hours or days, used for the necessary period, then the cloud cluster cancelled, leaving behind no redundant hardware or software licenses. The cloud makes short term projects very economical.

Increased flexibility - due to the avoidance of long term financial commitments, individual business units will have the flexibility to fund more data mart projects. This is ideal for proof of concept, and ad-hoc analytic data projects on-demand. This agility enables isolated business units to respond to BI needs faster than their competitors and increase the quality of their strategy setting and execution.

Drive data warehousing in MB markets - medium-size businesses often have very large volumes of data for analysis, yet only a few IT resource at their disposal to analyze tens of terabytes of historical data to fine tune market strategies. Cloud-based analytic databases enable such businesses to warehouse and analyze terabytes of data in spite of these resource constraints

Drive the analytic SaaS market - companies that collect economic, market, advertising, scientific, and other data and then offer customers the ability to analyze it on line will be able to bring their solutions to market with much less risk and cost by utlising cloud infrastructures during the early stages of growth. This will save significant dollars at the formative, cash-flow constrained period of inception. This frees up capital to invest in customer acquisition, product development, and other market development activities. Once the viability of the business model is proven, analytic data can be migrated to internal databases from the cloud if needed.

 

Growth Considerations

As data volumes grow, for analytic cloud projects to succeed they will require a database architecture that is designed to function efficiently in elastic, hosted computing environments like the cloud. At a minimum, such databases must include the following architectural features:

  • "Scale-out" shared-nothing architecture - to handle changing analytic workloads as elastically as the cloud
  • Aggressive data compression - to keep storage costs low
  • Automatic grid replication and failover - to provide high availability in the cloud

Next: Cloud Computing BI Vendors

 

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