Cloud Computing and BI
The three main constraints to BI adoption and a new era of analytic
data management for business intelligence are:
- Effort required to consolidate and cleanse enterprise data
- Cost of BI technology
- Performance impact on existing infrastructure / inadequate IT
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
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
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.
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
- "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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