Description
The present economic crisis experienced by all the states of the world orients more and more the information technology industry towards efficiency.
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-1, Issue-6, January 2013
81
Abstract - The present economic crisis experienced by all the
states of the world orients more and more the information
technology industry towards efficiency. Organizations are striving
to become intelligent and achieve competition advantages through
the use of Business I ntelligence (BI ) solutions. One of the
instruments that can bring about the technology requirements of
evolving BI solutions is Cloud Computing. The present paper
identifies the key factors responsible for evolution of New
Business I ntelligence on the Cloud, the various models available
to port BI solution on Cloud, the primary drivers for Cloud BI , the
impact of implementing Cloud BI as well as issues around it.
I ndex Terms—Business I ntelligence, Cloud, Cloud BI , BI in
the Cloud.
I. INTRODUCTION
The past decade has seen rapid evolution of the business
landscape. Business environment has totally changed today.
The nature and the structure of the current dynamic world
cause that nowadays, in times of uncertainty, risks and
incomplete information, the crisis becomes a feature of
modern business, not the state of emergency. There is more
discontinuity observer today then continuity. In this changing
world business organizations are increasingly realizing the
need for a more scalable and flexible information technology
architecture. The ever mounting burden of regulation and
compliance is further amplifying business expectations from
IT while at the same time tightening the noose [1].
Each organization tends at becoming an intelligent
organization and at gaining competition advantage on the
market by the use of new and innovative Business Intelligence
(BI) solutions. Today, business intelligence (BI) has been
under mounting pressure to evolve as an all pervasive
information and analytics agent. But the reality is that the
failure rate of BI projects is very high and the organizations
most often are not able to realize the full potential of a BI
project. This failed BI implementation could stem from a
number of reasons; including unclear business requirements,
multiple and diverse source data systems, long time-to-market
or the proprietary technology standards that some
off-the-shelf BI solutions demand. The problem gets
compounded when we factor in investments that the
organizations would have already made on hardware,
software and manpower where the cost of failure is too high
[11].
Manuscript received on January, 2013.
Yuvraj Singh Gurjar, Department of Computer Science, Pacific
Univerisity, Udaipur -313024, India.
Prof. (Dr.) Vijay Singh Rathore, Director, Shree Karni College, Jaipur
and Professor-CSE, Rajasthan College of Engineering For Women, Jaipur
-302021, India.
On the other side of the coin, in the wake of the present
economic crisis and the pursuant business environment, IT
has concretized its strategic relationship with business with
the reintroduction of grid technology in the form of cloud
computing. The cloud model enabled by SOA provides
flexibility and scalability (infinite in certain cases) using
external computing and processing power in the form of real
time e-services. The primary benefits driven by this model are
business agility with lower costs, enabling organizations to
respond quickly and effectively to the ever changing business
environment [1].
Taking into view the prospects provided by Cloud
Computing, large investments in traditional BI solutions are
often unpractical and unattractive, while popular solutions
based on Cloud Computing, called Cloud BI or BI services on
demand are increasingly popular. Cloud BI solution has
special interest for organizations that desire to improve agility
while at the same time reducing IT costs and exploiting the
benefits of Cloud Computing.
II. DEFINITIONS
A. Cloud Computing
The National Institute of Standards and Technology (NIST)
defines cloud computing in a specific manner, by this we can
understand the cloud computing in a better way, that: "Cloud
computing is a model for enabling convenient, on-demand
network access to a shared pool of configurable computing
resources (e.g., networks, servers, storage, applications, and
services) that can be rapidly provisioned and released with
minimal management effort or service provider interaction."
[3]
Gartner defines, ?cloud computing is a style of computing
where massively scalable IT-enabled capabilities are
delivered ?as a service‘ to external customers using Internet
technologies.? [4]
According to Quoting Chan, the concept may be defined
starting from its name (cloud) as ?common,
location-independent, online, utility that is available on
demand?. This approach emphasizes the fact that any shared
resource is statistically multiplied on several applications and
clients. Thus, no matter where the client is geographically
located, he can access the information in the ?cloud?. The ?on
demand? feature means resources have to be dynamically
allocated [5].
Proposed Definition
?Clouds are a large pool of easily usable and accessible
virtualized resources (such as hardware, development
platforms and/or services). These resources can be
dynamically reconfigured to adjust to a variable load (scale),
Cloud Business Intelligence – Is What Business
Need Today
Yuvraj Singh Gurjar, Vijay Singh Rathore
Cloud Business Intelligence – Today’s Need
82
allowing also for an optimum resource utilization. This pool
of resources is typically exploited by a pay- per-use model in
which guarantees are offered by the Infrastructure Provider by
means of customized SLAs.?
B. Business I ntelligence
?Business intelligence (BI) is the ability of an organization to
collect, maintains, and organizes knowledge. This produces
large amounts of information that can help develop new
opportunities. Identifying these opportunities, and
implementing an effective strategy, can provide a competitive
market advantage and long-term stability? — [6]
?Business intelligence (BI) is an umbrella term that includes
the applications, infrastructure and tools, and best practices
that enable access to and analysis of information to improve
and optimize decisions and performance – [7].
Proposed Definition
?Business Intelligence is a set of methodologies, processes,
architectures, and technologies that transform raw data into
meaningful and useful information used to enable more
effective strategic, tactical, and operational insights and
decision-making." When using this definition, business
intelligence also includes technologies such as data
integration, data quality, data warehousing, master data
management, text and content analytics, and many others that
the market sometimes lumps into the Information
Management segment.
C. Cloud Business I ntelligence
Cloud BI is a revolutionary concept of delivering business
intelligence capabilities ?as service? using cloud based
architecture that comes at a lower cost yet faster deployment
& flexibility.
Software as a Service (SaaS) BI is also being used by many
small and medium sized enterprises who seek to speed up
their businesses with BI and analytics tools.
III. CLOUD, BI AND CLOUD-BI IN TOP
TECHNOLOGY PRIORITIES
A. Cloud and Business I ntelligence
Looking at trends shown in Gartner‘s list of CIOs
technology priority (Compiled on the basis of Annual surveys
by Gartner Inc.'s), we can get an idea where Cloud Solutions
and Business Intelligence stands in technology priorities for
current CIOs [8]. Fig -1: depicts movements of Cloud and
Business Intelligence in priorities since 2007 to 2012.
Business Intelligence topped the list from 2007 to 2009,
ranked 5th in 2010 and again took top rank in year 2011-12.
Cloud Computing hold 1st rank in 2010 and remain in top 5
during last 5 years.
B. Cloud Business I ntelligence
According to a recent Market Study of 859 respondents [9],
there is a current strong investment in cloud based BI and
growing interest in tapping into the cloud‘s benefits; while
Gartner survey throws the fact that almost one-third of the BI
platform users surveyed (27 percent, to be exact) are using or
planning to use the cloud / SaaS model to expand their
business intelligence capabilities in the next 12 months [10].
These statistics strongly suggest that cloud based BI
implementation is on an upswing among organizations.
Fig 1: Cloud and BI in CIOs Technology Priorities
IV. REQUIREMENT ANALYSIS
A. Need of Mastering "Acquisition-to-Action" Cycle
In the present economy, organizational competitiveness is
defined by how quickly companies can synthesize the many
sources of information coming their way. To achieve this,
they need to be able to master what we call the
"acquisition-to-action" cycle. In other words, ?How fast can
data be captured, stored, queried, analyzed, shared and acted
upon?? Traditional BI solutions, massive data warehouses /
data marts and databases that were originally designed to
crank out pre-configured reports, like sales and financials
history, are simply not agile enough to handle today's urgent
analytic needs, especially in the persistently expanding
domain of machine-generated data [2]. More complex data
analysis in the form of ad-hoc analysis (to figure out what to
do now) and predictive analysis (to understand what to do
next) is the requirement of today‘s changing business
requirements. Also, organizations are under incredible
pressure to do all such analysis at ultra-high tech speed and
there is no space for any lag in any term.
B. Arrival of New Data source
When we talk about information analysis, it's important to
think about where the data actually resides. While the scope
of traditional BI is limited to structured data that can be
stuffed into columns and rows on a data warehouse, the fact is
that over 90% of today‘s data is unstructured in the form of
images, MP3 files, videos and social media snippets. Also,
much of the data that organizations need to look at is not
necessarily "owned" by them - it exists within various social
computing services like Twitter and Facebook, it's hidden
within Web logs, sensor output, and call detail
records. Forward-looking businesses are desperate to tame
and gain competitive advantage from unstructured business
data floating around within and beyond their enterprise.
Finding exactly what you need within such an enormous
stream can be like finding a needle in a haystack. But imagine
if you could define a set of queries and get the summary
information needed in a much smaller, more digestible form.
This requires technology that's able to use knowledge about
the data itself to intelligently isolate the relevant information
and make queries more efficient [2]. To analyze such a big
new source of data companies need resources with unlimited
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-1, Issue-6, January 2013
83
processing power, which is not possible for every
organization, thus limiting the scope to analyze new data
sources for many.
C. Evolution of New Business I ntelligence
In recent times, business intelligence (BI) has been under
mounting pressure to evolve as an all pervasive information
and analytics agent. Through business intelligence it is
possible to improve the decision making process in virtually
any department, organization or industry. More and more
businesses are turning to analytic applications to provide
critical business insights. Whether focused on achieving
higher ROI, better understanding of the competitive
landscape, improving product and service quality, BI is one of
the few technologies that can equip organizations to more
effectively prepare for tomorrow today [1]. Organizations
have seen how business intelligence has changed over this
time, how the tools have evolved offering more functionality
to the analysts, and at the same time, providing solutions for
more users. Today's IT professionals need to help their
organizations capture, track, analyze and share more
information than ever before. From mass quantities of
transactional data, Web data, and huge and growing volumes
of "machine-generated" information, such as sensor and log
data, volumes are expanding into the terabyte (and even the
petabyte) range. At the same time, the way end users consume
information is rapidly changing. Information requirements
have grown exponentially: while only a few gigabytes of data
were needed some years ago, now data warehouses are
populated with terabytes of data and rapidly moving to the
petabyte range.
With the advent of new technologies new channels are
arriving with lots of data to analyze. In today‘s environment,
lots of social computing technologies like Facebook, Twitter
and LinkedIn, are spreading like wildfire, and companies are
starting to look at how to harness social networks, blogs, wikis
and more to share business intelligence and collaborate more
effectively. As the data center strains under the need for more
storage and faster performance (all while keeping costs in
check,) cloud computing, open source technologies and other
emerging approaches are presenting compelling new ways to
manage data and consume IT services [2].
D. Need of Collaborative BI solutions
The value of business intelligence lies in its ability to shape
and enhance decision-making throughout the enterprise. Yet
all too often, there's not enough context associated with the
analyses generated by information management experts,
leaving business end users unable to make sense of it all, or at
least make sense of it quickly enough to take action. Today's
social computing technologies offer a great opportunity for
intelligence to be digested in a more collaborative
atmosphere. Imagine combining analytics (a quarterly sales
report tracking online purchases, for example) with
capabilities like search, bookmarking, tagging, commenting
and rating capabilities. Now imagine accessing the
intelligence via a Web-based portal where any number of
enterprise stakeholders can look at the report collaboratively
and engage in a conversation that adds context to the content.
This socially powered approach to BI can significantly speed
decision-making. More important, it enables a far richer
understanding of corporate data, which enables better
decision-making as well. Collaborative BI solutions that
integrate social technologies directly into the analytic
environment are increasingly available, and smart companies
would do well to take a look at how these can enrich their BI
efforts. As organizations continue to be bombarded by data,
old BI strategies are increasingly giving way to more
innovative approaches. The businesses that thrive will be
those that succeed at adopting the best new ideas and
technologies.
E. Moving BI in the Cloud
Considering the present trends in adopting Cloud
Solutions, data center is not going to disappear anytime soon
and cloud computing is certainly democratizing information
access. The strengths of the cloud model e.g., accelerated
speed-to-market, reduced TCO, scalability, etc., have led
many BI vendors to introduce cloud services as a clear and
distinctive extension to the on-premise and on-demand BI
applications [1]. Companies like Amazon and Google offer
unlimited processing power and storage thus allowing any
business to cater to its increasing information stack while
keeping the IT related costs under control.
For example, smaller companies that previously couldn't
afford to build huge server farms to process mass amounts of
data can turn to providers like Amazon and Google to support
large-scale analysis efforts. In addition, a number of
innovative SaaS and "cloud-friendly" BI and analytic
solutions are cropping up, which means that organizations can
take advantage of the cloud to not only store their data, but
also crunch it. There are, of course, some key considerations.
Security and data privacy get the most press, but uptime,
performance and openness/portability are also important.
Depending on the organization's specific requirements, there's
more than one flavor of cloud, ranging from public
(affordable and highly scalable), private (offering greater
security and control) and hybrid (combining aspects of both).
The best approach will ultimately depend on what's most
important to the target organization. And, when it comes to
BI, cloud solutions will only be as good as the performance
they can deliver.
V. BASIC ARCHITECTURE
The Basic Architecture - The basic architecture needed to
run business intelligence solution in the cloud is depicted in
Figure 2 [12].
Fig 2: BI on the Cloud: Architecture
Cloud Business Intelligence – Today’s Need
84
The lower layers are formed by hardware and software
systems. These are the minimum elements that have to be
offered by the cloud computing provider. Hardware refers to
processing, storage, and networks, while software refers to the
operating systems and drivers required to handle the
hardware.
The Data integration box refers to the tools needed to
perform the ETL and data cleansing processes. The database
box refers to the relational or multidimensional database
systems that administer the information. It is important to note
that there are new devices called "data warehouse
appliances", which integrate hardware, software and
databases elements in just one box. However, they should be
considered as an integrated part of the architecture.
Data warehousing tools are the set of applications that
allow the creation and maintenance of the data warehouse. BI
tools are the set of front-end applications that enable the final
users to access and analyze the data.
Finally, since all the architecture is going to be accessed
through the Internet, there is no need for thick clients or
preinstalled applications, because all the content and
configuration can be reached through traditional internet
browsers.
VI. DEPLOYMENT MODELS
The following models are available while deploying BI
components on Cloud -
1) Public cloud-based IaaS for a BI system: This option
involves subscribing to an IaaS model for data storage
and processing power. Companies can then buy and
deploy their own ETL, DBMS and BI tools on top of this.
Vendors serving IaaS Cloud solution are Amazon,
Rackspace or GoGrid pro.
2) Public / Hybrid Cloud based PaaS for BI and DW: This
option involves deploying the BI/DW system on a public
or hybrid cloud to host own cloud-based BI system. This
option is best suited to implement BI systems for SMBs,
Custom Analytic applications, Enterprise BI systems,
Data Mining, Prototyping etc. Vendors in this area
are—AsterData MPP on Amazon EC2, IBM Cognos
Express on Amazon EC2, Teradata Express on Amazon
EC2 and RightScale/ Talend/Vertica/Jaspersoft on
Amazon EC2.
3) Analytics on the Cloud: These products are public cloud
based solutions. Vendors provide pre-build solutions for
analytics. Vendors include – SAP Business Objects,
Microsoft BI Tools, LucidEra, IBM Cognos, Cloud9
Analytics etc [14].
4) Public or Hybrid cloud based SaaS BI – This option
provides cloud based reporting system on operational
data either from SaaS based transaction processing
system or from internal transaction processing system.
Vendors available are – SAP Business Objects,
Microsoft Azure etc.
5) Private cloud-based BI system running internally - The
largest private cloud-based BI system is IBM‘s internal
Blue Insight which is based on IBM System Z and IBM
Cognos 8 BI. IBM has also launched the Smart Analytics
Cloud, a private cloud offering for large enterprises
based on the same technologies [14].
VII. DRIVERS FOR CLOUD BI
There are several operational and financial factors that
work in favor of Cloud Business Intelligence (BI), the key
being:
1) Time Saving with speed of implementation and
deployment: Immediate availability of environment
without any dependence on the long periods associated
with infrastructure procurement, application deployment,
etc. drastically reduces the BI implementation time
window [11][14].
2) Lower Total Cost of Ownership: Convert some part of
capital expenditure to operational expenditure,
cost-effective pricing models, pay per use model, etc. On
a long term, these solutions help organization in reducing
operational costs, IT support expenses and much more.
[14]
3) Elasticity: Leverage the massive computing power
available on the Web, scale up and scale down based on
changing requirements. [14]
4) On-demand Availability: Support mobile and remote
users, Browser-based access to control everything from
the cloud platform to database management, from the
data warehouse layer to the analytics platform. [14]
5) Expertise support: Organizations choosing third-party
Cloud BI solutions getting expertise backing in the form
of cloud and tech-savvy professionals who can help their
internal IT and business team to deal with managing BI
and analytic systems. [9]
6) Focus on Core Strength: Outsource running of BI apps to
professionals and focus on core capabilities.
VIII. IMPACT OF TRANSFORMING BI IN THE CLOUD
Cloud computing is transforming the economics of BI and
opens up the opportunity for enterprises to compete using the
insight that BI provides. Cloud-based solution will impact BI
by:
Fig 3: Benefits provided by Cloud BI Solutions
1) Easier evaluation of Technology: Cloud enables software
companies to make new technology available to
evaluators on a self-service basis, avoiding the need to
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ISSN: 2277-3878, Volume-1, Issue-6, January 2013
85
download and set up free software downloads or acquire
hardware‘s fitting to the technology. [13]
2) Increased short-term ad-hoc analysis: Where short term
needs (weeks or months) for BI is required, cloud
services are ideal. A data mart can be created in a few
hours or days, used for the necessary period, and then
cancel the cloud cluster, leaving behind no redundant
hardware or software licenses. The cloud makes short
term projects very economical. [13][15]
3) 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 [13][15].
4) 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 tons of terabytes of historical data to fine tune
market strategies. Cloud-based solution can enable such
businesses to warehouse and analyze terabytes of data in
spite of these resource constraints [13].
5) Drive the analytic SaaS market: Companies that collect
economic, market, advertising, scientific, and other data
and then offer customers the ability to analyze it online
will be able to bring their solutions to market with much
less risk and cost by utilizing cloud infrastructures during
the early stages of growth [13][16].
6) ?Scale-out? shared-nothing architecture: To handle
changing analytic workloads as elastically as the cloud.
Auto-scaling of Virtual Machine (VM) can be used to
provide necessary compute power required during heavy
workload and an efficient algorithm needs to be worked
out in order to auto-scale in VM‘s when not required.[13]
7) Aggressive data storage: Cloud provides an appropriate
infrastructure for storing large amount of data at low cost.
No further additional overheads are required to store data
on cloud thus helping achieve manpower savings for
operations like data backup and server maintenance. For
example in case of Windows Azure, Table Storage is
designed to be massively scalable and a typical Azure
Table can contain billions of records amassing to
Terabytes of data. Blob Storage provides a means to store
unstructured data much in the same way that would store
a bunch of images on the File System of a server. Blobs
can be mounted as XDrives on the Virtual Machine
instance where a particular service is running and
accessed exactly like a file system would. [1][13]
8) Automatic replication and failover: This will provide
high availability in the cloud. In case of Windows Azure,
data is stored on 3 nodes to enhance both access speeds
and reduce data redundancy.
According to recent research [17], Cloud based BI
solutions were viewed as beneficial with 78% saying they
would see value. Several participants took the time to point
out that the demand for data analysis in a BI solution is very
uneven, which makes it a particularly good fit for a cloud
solution. Figure-3 lists the various benefits by Cloud BI as
accepted by various participants of the research.
IX. ISSUES WITH CLOUD BI
While business intelligence can benefit from cloud
computing, it is not a silver bullet, and there are several
potential challenges, such as:
1) Moving data to the cloud – Large data sets in silos sitting
on premises need to get to the cloud before they can be
completely used. Moving large data volumes over the
Internet can be a challenge for most companies. Upload
speeds are often slower than download speeds and
network pipes are often divided up into channels to
accommodate voice and data. Initial data loads often
require the sending of media and coordination with
service providers. This is often recommended by cloud
providers like Amazon. Although this introduces
latencies, it is often acceptable for most BI options. With
load windows shrinking in many environments, this can
pose a significant barrier. While it is true that we could
load data more frequently, many data integration
architectures aren‘t currently designed to support this
change. [15][18]
2) Data Security – For some organizations, the concerns
over security may be a barrier that is impossible to
overcome today. The majority of data is core and
proprietary to enterprises. Secure storage of this data is
essential and, in quite a few cases, it might be mandatory
to keep this data on premise. There are several options to
secure data in the cloud including during storage and at
rest, which include standard security measures like
encryption keys, SSL and certificates. Compliance and
regulatory reasons also require data to be stored securely.
However in many cases, the Cloud vendors provide a
more secure environment than what exists at customer
sites.[14][18]
3) Speed of data access: Since storage resources are
separate from server resources, there is likely to be a
significant latency in accessing large amounts of data.
Large scale data warehouses require high speed
backplanes and dedicated data storage nodes. Until
Cloud providers can provide this level of service or data
warehouses can exist entirely in memory, there will be
limits to the size and performance of data warehouses in
the Cloud. There will also be significant latency if BI
applications exist in the Cloud but the data exists at a
client site, especially when processing and returning
large amounts of data. [18]
4) BI components as a service – So far, only a limited set of
services are available from established BI vendors. This
includes some reporting capabilities and ability to do
visualizations. Most established vendors are yet to
introduce complete product features over the cloud. [15]
5) Integration with on premise data – It is challenging to
integrate on-premise data with cloud components, as it
continues to exist in silos and requires access to data
behind the firewall. [14][15]
6) Lack of control: Tough to get Service Level Agreements
(SLAs) from cloud providers. Data control and data
ownership, reliability of service challenges are some of
the main reasons for client concern. To mitigate this,
organizations should already have in place thorough IT
governance and service delivery standards and models.
[14]
7) Vendor Maturity: Too many cloud BI vendors, hosting
Cloud Business Intelligence – Today’s Need
86
providers with varying offerings, etc. makes it confusing
to choose the right vendor based on required needs and
vendor capabilities. [14]
8) Reliability of service: The services offered by different
vendors vary a lot and back support provided by vendors
is limited. Amazon Web Services doesn‘t have a
customer service phone number. All contact is via email
with a 48 hour service level agreement. This can be
especially frustrating during service disruptions. On
October 7, 2007, Amazon‘s EC2 went down and an
undisclosed number of customers lost their servers and
their data. On February 2, 2008, both Amazon‘s EC2 and
S3 were inaccessible for more than 3 hours. [18]
9) Limited ability to scale-up: Some BI software, such as
SMP databases, in-memory analytics and OLAP scale
better on a single server versus across servers. Currently,
the ability to provision a server beyond 4 CPU‘s (8 cores)
and 16GB‘s is limited. As more providers support 64-bit
platforms and virtualization becomes more advanced,
this limitation will begin to go away.[18]
10) Performance: Limits to the size and performance of data
warehouses in the Cloud, significant latency if BI
applications exist in the Cloud but the data exists at a
client site, especially when processing and returning
large amounts of data. [14]
11) Pricing: It is difficult to budget for computing resources
when those costs are variable in nature. It is especially
difficult when pricing is based on multiple components
such as network traffic, storage size and IP address
requests as well as different pricing tiers for each
component. Also, different vendors base their pricing on
different components. For example, GoGrid charges, in
part, based on memory (each Gigabyte (GB) of RAM
utilized is equivalent to 1 RAM hour) as well as network
traffic. [14][18]
X. CONCLUSION
Cloud is a big part of future Business Intelligence and
offers several advantages in terms of cost benefits, flexibility
of implementation, availability and speed of implementation.
BI on the Cloud offers huge possibilities for removing
barriers to decision making by integrating high volume and
mission critical business processes. Therefore, a Cloud BI
solution may be a feasible answer to the challenges of the
economic crisis. By such a solution, the economic
organization – small, middle-sized or large – may use market
opportunities that under normal conditions (other than
adopting BI or Cloud BI) would not be accessible.
Irrespective of the age of a BI landscape the cloud model can
drive increased BI adoption, improved end-user experience,
better access to analytics and reduced IT dependence.
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2012. Available:http://www.hostanalytics.com/sites/default/files/HostAnalytics_CIO-v
4.pdf
[18] “TDWI – BI in the Cloud”. Available:http://www.isaca.org/chapters1/phoenix/events/Documents/business_
intelligence_overview.ppt
Yuvraj Singh Gurjar, MCA, .MCITP – SQL Server,
MCTS – Business Intelligence, Oracle Certified
Associate. He received his MCA degree from University
of Rajasthan in the year of 2002. He started his career as
MIS Programmer with RUIDP (An Asian Development
Bank financed project in Rajasthan (India)). In 2003, he
joined renowned bank Punjab National Bank as
Officer-IT. In 2008 he shifted to Union Bank of India as
Sr. Manager (Software). Later on, in year 2011 he founded KURIOS - A firm
working in the area of Business Intelligence and Performance Management
Solutions. Currently he is working as Chief Technical Consultant with
KURIOS. He has more than 10 years of experience in IT field with
specialization in MIS and BI applications. He is also a student of Ph.D. in the
Department of Computer Science in the Faculty of Computer Science of
Pacific University, Udaipur, India.
Prof. (Dr.) Vijay Singh Rathore, MCA, M.Tech.
(C.S.) PhD (Computer Science), MBA, ICDA-USA,
ICAD-USA, Diploma French & German. Presently, He
is Director in Shree Karni College, & Shree Karni
Institute of Science, Management & Technology
(KISMAT), and Professor (CSE) in Rajasthan College
of Engineering For Women, Jaipur (India). He has 12
years of experience in Technical Education, having done the First PhD in
Computer Science after MCA from University of Rajasthan, Jaipur, written
12 Books on various subjects of Computer Science, published more than 50
research papers at national & international level, supervised 06 PhD
candidates, and currently supervising 08 PhD research scholars in Computer
Science. He is active member of various committees of UGC, RTU, UOR,
UOK, JNU, SGVU, VMOU, NIMSU, AMITYU, MJRPU, IISU, JRSU,
JRNRVU, PU etc. He is reviewer in some reputed International Journals. His
research area is networking & security with special emphasis on Cloud
Computing.
doc_184952556.pdf
The present economic crisis experienced by all the states of the world orients more and more the information technology industry towards efficiency.
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-1, Issue-6, January 2013
81
Abstract - The present economic crisis experienced by all the
states of the world orients more and more the information
technology industry towards efficiency. Organizations are striving
to become intelligent and achieve competition advantages through
the use of Business I ntelligence (BI ) solutions. One of the
instruments that can bring about the technology requirements of
evolving BI solutions is Cloud Computing. The present paper
identifies the key factors responsible for evolution of New
Business I ntelligence on the Cloud, the various models available
to port BI solution on Cloud, the primary drivers for Cloud BI , the
impact of implementing Cloud BI as well as issues around it.
I ndex Terms—Business I ntelligence, Cloud, Cloud BI , BI in
the Cloud.
I. INTRODUCTION
The past decade has seen rapid evolution of the business
landscape. Business environment has totally changed today.
The nature and the structure of the current dynamic world
cause that nowadays, in times of uncertainty, risks and
incomplete information, the crisis becomes a feature of
modern business, not the state of emergency. There is more
discontinuity observer today then continuity. In this changing
world business organizations are increasingly realizing the
need for a more scalable and flexible information technology
architecture. The ever mounting burden of regulation and
compliance is further amplifying business expectations from
IT while at the same time tightening the noose [1].
Each organization tends at becoming an intelligent
organization and at gaining competition advantage on the
market by the use of new and innovative Business Intelligence
(BI) solutions. Today, business intelligence (BI) has been
under mounting pressure to evolve as an all pervasive
information and analytics agent. But the reality is that the
failure rate of BI projects is very high and the organizations
most often are not able to realize the full potential of a BI
project. This failed BI implementation could stem from a
number of reasons; including unclear business requirements,
multiple and diverse source data systems, long time-to-market
or the proprietary technology standards that some
off-the-shelf BI solutions demand. The problem gets
compounded when we factor in investments that the
organizations would have already made on hardware,
software and manpower where the cost of failure is too high
[11].
Manuscript received on January, 2013.
Yuvraj Singh Gurjar, Department of Computer Science, Pacific
Univerisity, Udaipur -313024, India.
Prof. (Dr.) Vijay Singh Rathore, Director, Shree Karni College, Jaipur
and Professor-CSE, Rajasthan College of Engineering For Women, Jaipur
-302021, India.
On the other side of the coin, in the wake of the present
economic crisis and the pursuant business environment, IT
has concretized its strategic relationship with business with
the reintroduction of grid technology in the form of cloud
computing. The cloud model enabled by SOA provides
flexibility and scalability (infinite in certain cases) using
external computing and processing power in the form of real
time e-services. The primary benefits driven by this model are
business agility with lower costs, enabling organizations to
respond quickly and effectively to the ever changing business
environment [1].
Taking into view the prospects provided by Cloud
Computing, large investments in traditional BI solutions are
often unpractical and unattractive, while popular solutions
based on Cloud Computing, called Cloud BI or BI services on
demand are increasingly popular. Cloud BI solution has
special interest for organizations that desire to improve agility
while at the same time reducing IT costs and exploiting the
benefits of Cloud Computing.
II. DEFINITIONS
A. Cloud Computing
The National Institute of Standards and Technology (NIST)
defines cloud computing in a specific manner, by this we can
understand the cloud computing in a better way, that: "Cloud
computing is a model for enabling convenient, on-demand
network access to a shared pool of configurable computing
resources (e.g., networks, servers, storage, applications, and
services) that can be rapidly provisioned and released with
minimal management effort or service provider interaction."
[3]
Gartner defines, ?cloud computing is a style of computing
where massively scalable IT-enabled capabilities are
delivered ?as a service‘ to external customers using Internet
technologies.? [4]
According to Quoting Chan, the concept may be defined
starting from its name (cloud) as ?common,
location-independent, online, utility that is available on
demand?. This approach emphasizes the fact that any shared
resource is statistically multiplied on several applications and
clients. Thus, no matter where the client is geographically
located, he can access the information in the ?cloud?. The ?on
demand? feature means resources have to be dynamically
allocated [5].
Proposed Definition
?Clouds are a large pool of easily usable and accessible
virtualized resources (such as hardware, development
platforms and/or services). These resources can be
dynamically reconfigured to adjust to a variable load (scale),
Cloud Business Intelligence – Is What Business
Need Today
Yuvraj Singh Gurjar, Vijay Singh Rathore
Cloud Business Intelligence – Today’s Need
82
allowing also for an optimum resource utilization. This pool
of resources is typically exploited by a pay- per-use model in
which guarantees are offered by the Infrastructure Provider by
means of customized SLAs.?
B. Business I ntelligence
?Business intelligence (BI) is the ability of an organization to
collect, maintains, and organizes knowledge. This produces
large amounts of information that can help develop new
opportunities. Identifying these opportunities, and
implementing an effective strategy, can provide a competitive
market advantage and long-term stability? — [6]
?Business intelligence (BI) is an umbrella term that includes
the applications, infrastructure and tools, and best practices
that enable access to and analysis of information to improve
and optimize decisions and performance – [7].
Proposed Definition
?Business Intelligence is a set of methodologies, processes,
architectures, and technologies that transform raw data into
meaningful and useful information used to enable more
effective strategic, tactical, and operational insights and
decision-making." When using this definition, business
intelligence also includes technologies such as data
integration, data quality, data warehousing, master data
management, text and content analytics, and many others that
the market sometimes lumps into the Information
Management segment.
C. Cloud Business I ntelligence
Cloud BI is a revolutionary concept of delivering business
intelligence capabilities ?as service? using cloud based
architecture that comes at a lower cost yet faster deployment
& flexibility.
Software as a Service (SaaS) BI is also being used by many
small and medium sized enterprises who seek to speed up
their businesses with BI and analytics tools.
III. CLOUD, BI AND CLOUD-BI IN TOP
TECHNOLOGY PRIORITIES
A. Cloud and Business I ntelligence
Looking at trends shown in Gartner‘s list of CIOs
technology priority (Compiled on the basis of Annual surveys
by Gartner Inc.'s), we can get an idea where Cloud Solutions
and Business Intelligence stands in technology priorities for
current CIOs [8]. Fig -1: depicts movements of Cloud and
Business Intelligence in priorities since 2007 to 2012.
Business Intelligence topped the list from 2007 to 2009,
ranked 5th in 2010 and again took top rank in year 2011-12.
Cloud Computing hold 1st rank in 2010 and remain in top 5
during last 5 years.
B. Cloud Business I ntelligence
According to a recent Market Study of 859 respondents [9],
there is a current strong investment in cloud based BI and
growing interest in tapping into the cloud‘s benefits; while
Gartner survey throws the fact that almost one-third of the BI
platform users surveyed (27 percent, to be exact) are using or
planning to use the cloud / SaaS model to expand their
business intelligence capabilities in the next 12 months [10].
These statistics strongly suggest that cloud based BI
implementation is on an upswing among organizations.
Fig 1: Cloud and BI in CIOs Technology Priorities
IV. REQUIREMENT ANALYSIS
A. Need of Mastering "Acquisition-to-Action" Cycle
In the present economy, organizational competitiveness is
defined by how quickly companies can synthesize the many
sources of information coming their way. To achieve this,
they need to be able to master what we call the
"acquisition-to-action" cycle. In other words, ?How fast can
data be captured, stored, queried, analyzed, shared and acted
upon?? Traditional BI solutions, massive data warehouses /
data marts and databases that were originally designed to
crank out pre-configured reports, like sales and financials
history, are simply not agile enough to handle today's urgent
analytic needs, especially in the persistently expanding
domain of machine-generated data [2]. More complex data
analysis in the form of ad-hoc analysis (to figure out what to
do now) and predictive analysis (to understand what to do
next) is the requirement of today‘s changing business
requirements. Also, organizations are under incredible
pressure to do all such analysis at ultra-high tech speed and
there is no space for any lag in any term.
B. Arrival of New Data source
When we talk about information analysis, it's important to
think about where the data actually resides. While the scope
of traditional BI is limited to structured data that can be
stuffed into columns and rows on a data warehouse, the fact is
that over 90% of today‘s data is unstructured in the form of
images, MP3 files, videos and social media snippets. Also,
much of the data that organizations need to look at is not
necessarily "owned" by them - it exists within various social
computing services like Twitter and Facebook, it's hidden
within Web logs, sensor output, and call detail
records. Forward-looking businesses are desperate to tame
and gain competitive advantage from unstructured business
data floating around within and beyond their enterprise.
Finding exactly what you need within such an enormous
stream can be like finding a needle in a haystack. But imagine
if you could define a set of queries and get the summary
information needed in a much smaller, more digestible form.
This requires technology that's able to use knowledge about
the data itself to intelligently isolate the relevant information
and make queries more efficient [2]. To analyze such a big
new source of data companies need resources with unlimited
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-1, Issue-6, January 2013
83
processing power, which is not possible for every
organization, thus limiting the scope to analyze new data
sources for many.
C. Evolution of New Business I ntelligence
In recent times, business intelligence (BI) has been under
mounting pressure to evolve as an all pervasive information
and analytics agent. Through business intelligence it is
possible to improve the decision making process in virtually
any department, organization or industry. More and more
businesses are turning to analytic applications to provide
critical business insights. Whether focused on achieving
higher ROI, better understanding of the competitive
landscape, improving product and service quality, BI is one of
the few technologies that can equip organizations to more
effectively prepare for tomorrow today [1]. Organizations
have seen how business intelligence has changed over this
time, how the tools have evolved offering more functionality
to the analysts, and at the same time, providing solutions for
more users. Today's IT professionals need to help their
organizations capture, track, analyze and share more
information than ever before. From mass quantities of
transactional data, Web data, and huge and growing volumes
of "machine-generated" information, such as sensor and log
data, volumes are expanding into the terabyte (and even the
petabyte) range. At the same time, the way end users consume
information is rapidly changing. Information requirements
have grown exponentially: while only a few gigabytes of data
were needed some years ago, now data warehouses are
populated with terabytes of data and rapidly moving to the
petabyte range.
With the advent of new technologies new channels are
arriving with lots of data to analyze. In today‘s environment,
lots of social computing technologies like Facebook, Twitter
and LinkedIn, are spreading like wildfire, and companies are
starting to look at how to harness social networks, blogs, wikis
and more to share business intelligence and collaborate more
effectively. As the data center strains under the need for more
storage and faster performance (all while keeping costs in
check,) cloud computing, open source technologies and other
emerging approaches are presenting compelling new ways to
manage data and consume IT services [2].
D. Need of Collaborative BI solutions
The value of business intelligence lies in its ability to shape
and enhance decision-making throughout the enterprise. Yet
all too often, there's not enough context associated with the
analyses generated by information management experts,
leaving business end users unable to make sense of it all, or at
least make sense of it quickly enough to take action. Today's
social computing technologies offer a great opportunity for
intelligence to be digested in a more collaborative
atmosphere. Imagine combining analytics (a quarterly sales
report tracking online purchases, for example) with
capabilities like search, bookmarking, tagging, commenting
and rating capabilities. Now imagine accessing the
intelligence via a Web-based portal where any number of
enterprise stakeholders can look at the report collaboratively
and engage in a conversation that adds context to the content.
This socially powered approach to BI can significantly speed
decision-making. More important, it enables a far richer
understanding of corporate data, which enables better
decision-making as well. Collaborative BI solutions that
integrate social technologies directly into the analytic
environment are increasingly available, and smart companies
would do well to take a look at how these can enrich their BI
efforts. As organizations continue to be bombarded by data,
old BI strategies are increasingly giving way to more
innovative approaches. The businesses that thrive will be
those that succeed at adopting the best new ideas and
technologies.
E. Moving BI in the Cloud
Considering the present trends in adopting Cloud
Solutions, data center is not going to disappear anytime soon
and cloud computing is certainly democratizing information
access. The strengths of the cloud model e.g., accelerated
speed-to-market, reduced TCO, scalability, etc., have led
many BI vendors to introduce cloud services as a clear and
distinctive extension to the on-premise and on-demand BI
applications [1]. Companies like Amazon and Google offer
unlimited processing power and storage thus allowing any
business to cater to its increasing information stack while
keeping the IT related costs under control.
For example, smaller companies that previously couldn't
afford to build huge server farms to process mass amounts of
data can turn to providers like Amazon and Google to support
large-scale analysis efforts. In addition, a number of
innovative SaaS and "cloud-friendly" BI and analytic
solutions are cropping up, which means that organizations can
take advantage of the cloud to not only store their data, but
also crunch it. There are, of course, some key considerations.
Security and data privacy get the most press, but uptime,
performance and openness/portability are also important.
Depending on the organization's specific requirements, there's
more than one flavor of cloud, ranging from public
(affordable and highly scalable), private (offering greater
security and control) and hybrid (combining aspects of both).
The best approach will ultimately depend on what's most
important to the target organization. And, when it comes to
BI, cloud solutions will only be as good as the performance
they can deliver.
V. BASIC ARCHITECTURE
The Basic Architecture - The basic architecture needed to
run business intelligence solution in the cloud is depicted in
Figure 2 [12].
Fig 2: BI on the Cloud: Architecture
Cloud Business Intelligence – Today’s Need
84
The lower layers are formed by hardware and software
systems. These are the minimum elements that have to be
offered by the cloud computing provider. Hardware refers to
processing, storage, and networks, while software refers to the
operating systems and drivers required to handle the
hardware.
The Data integration box refers to the tools needed to
perform the ETL and data cleansing processes. The database
box refers to the relational or multidimensional database
systems that administer the information. It is important to note
that there are new devices called "data warehouse
appliances", which integrate hardware, software and
databases elements in just one box. However, they should be
considered as an integrated part of the architecture.
Data warehousing tools are the set of applications that
allow the creation and maintenance of the data warehouse. BI
tools are the set of front-end applications that enable the final
users to access and analyze the data.
Finally, since all the architecture is going to be accessed
through the Internet, there is no need for thick clients or
preinstalled applications, because all the content and
configuration can be reached through traditional internet
browsers.
VI. DEPLOYMENT MODELS
The following models are available while deploying BI
components on Cloud -
1) Public cloud-based IaaS for a BI system: This option
involves subscribing to an IaaS model for data storage
and processing power. Companies can then buy and
deploy their own ETL, DBMS and BI tools on top of this.
Vendors serving IaaS Cloud solution are Amazon,
Rackspace or GoGrid pro.
2) Public / Hybrid Cloud based PaaS for BI and DW: This
option involves deploying the BI/DW system on a public
or hybrid cloud to host own cloud-based BI system. This
option is best suited to implement BI systems for SMBs,
Custom Analytic applications, Enterprise BI systems,
Data Mining, Prototyping etc. Vendors in this area
are—AsterData MPP on Amazon EC2, IBM Cognos
Express on Amazon EC2, Teradata Express on Amazon
EC2 and RightScale/ Talend/Vertica/Jaspersoft on
Amazon EC2.
3) Analytics on the Cloud: These products are public cloud
based solutions. Vendors provide pre-build solutions for
analytics. Vendors include – SAP Business Objects,
Microsoft BI Tools, LucidEra, IBM Cognos, Cloud9
Analytics etc [14].
4) Public or Hybrid cloud based SaaS BI – This option
provides cloud based reporting system on operational
data either from SaaS based transaction processing
system or from internal transaction processing system.
Vendors available are – SAP Business Objects,
Microsoft Azure etc.
5) Private cloud-based BI system running internally - The
largest private cloud-based BI system is IBM‘s internal
Blue Insight which is based on IBM System Z and IBM
Cognos 8 BI. IBM has also launched the Smart Analytics
Cloud, a private cloud offering for large enterprises
based on the same technologies [14].
VII. DRIVERS FOR CLOUD BI
There are several operational and financial factors that
work in favor of Cloud Business Intelligence (BI), the key
being:
1) Time Saving with speed of implementation and
deployment: Immediate availability of environment
without any dependence on the long periods associated
with infrastructure procurement, application deployment,
etc. drastically reduces the BI implementation time
window [11][14].
2) Lower Total Cost of Ownership: Convert some part of
capital expenditure to operational expenditure,
cost-effective pricing models, pay per use model, etc. On
a long term, these solutions help organization in reducing
operational costs, IT support expenses and much more.
[14]
3) Elasticity: Leverage the massive computing power
available on the Web, scale up and scale down based on
changing requirements. [14]
4) On-demand Availability: Support mobile and remote
users, Browser-based access to control everything from
the cloud platform to database management, from the
data warehouse layer to the analytics platform. [14]
5) Expertise support: Organizations choosing third-party
Cloud BI solutions getting expertise backing in the form
of cloud and tech-savvy professionals who can help their
internal IT and business team to deal with managing BI
and analytic systems. [9]
6) Focus on Core Strength: Outsource running of BI apps to
professionals and focus on core capabilities.
VIII. IMPACT OF TRANSFORMING BI IN THE CLOUD
Cloud computing is transforming the economics of BI and
opens up the opportunity for enterprises to compete using the
insight that BI provides. Cloud-based solution will impact BI
by:
Fig 3: Benefits provided by Cloud BI Solutions
1) Easier evaluation of Technology: Cloud enables software
companies to make new technology available to
evaluators on a self-service basis, avoiding the need to
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-1, Issue-6, January 2013
85
download and set up free software downloads or acquire
hardware‘s fitting to the technology. [13]
2) Increased short-term ad-hoc analysis: Where short term
needs (weeks or months) for BI is required, cloud
services are ideal. A data mart can be created in a few
hours or days, used for the necessary period, and then
cancel the cloud cluster, leaving behind no redundant
hardware or software licenses. The cloud makes short
term projects very economical. [13][15]
3) 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 [13][15].
4) 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 tons of terabytes of historical data to fine tune
market strategies. Cloud-based solution can enable such
businesses to warehouse and analyze terabytes of data in
spite of these resource constraints [13].
5) Drive the analytic SaaS market: Companies that collect
economic, market, advertising, scientific, and other data
and then offer customers the ability to analyze it online
will be able to bring their solutions to market with much
less risk and cost by utilizing cloud infrastructures during
the early stages of growth [13][16].
6) ?Scale-out? shared-nothing architecture: To handle
changing analytic workloads as elastically as the cloud.
Auto-scaling of Virtual Machine (VM) can be used to
provide necessary compute power required during heavy
workload and an efficient algorithm needs to be worked
out in order to auto-scale in VM‘s when not required.[13]
7) Aggressive data storage: Cloud provides an appropriate
infrastructure for storing large amount of data at low cost.
No further additional overheads are required to store data
on cloud thus helping achieve manpower savings for
operations like data backup and server maintenance. For
example in case of Windows Azure, Table Storage is
designed to be massively scalable and a typical Azure
Table can contain billions of records amassing to
Terabytes of data. Blob Storage provides a means to store
unstructured data much in the same way that would store
a bunch of images on the File System of a server. Blobs
can be mounted as XDrives on the Virtual Machine
instance where a particular service is running and
accessed exactly like a file system would. [1][13]
8) Automatic replication and failover: This will provide
high availability in the cloud. In case of Windows Azure,
data is stored on 3 nodes to enhance both access speeds
and reduce data redundancy.
According to recent research [17], Cloud based BI
solutions were viewed as beneficial with 78% saying they
would see value. Several participants took the time to point
out that the demand for data analysis in a BI solution is very
uneven, which makes it a particularly good fit for a cloud
solution. Figure-3 lists the various benefits by Cloud BI as
accepted by various participants of the research.
IX. ISSUES WITH CLOUD BI
While business intelligence can benefit from cloud
computing, it is not a silver bullet, and there are several
potential challenges, such as:
1) Moving data to the cloud – Large data sets in silos sitting
on premises need to get to the cloud before they can be
completely used. Moving large data volumes over the
Internet can be a challenge for most companies. Upload
speeds are often slower than download speeds and
network pipes are often divided up into channels to
accommodate voice and data. Initial data loads often
require the sending of media and coordination with
service providers. This is often recommended by cloud
providers like Amazon. Although this introduces
latencies, it is often acceptable for most BI options. With
load windows shrinking in many environments, this can
pose a significant barrier. While it is true that we could
load data more frequently, many data integration
architectures aren‘t currently designed to support this
change. [15][18]
2) Data Security – For some organizations, the concerns
over security may be a barrier that is impossible to
overcome today. The majority of data is core and
proprietary to enterprises. Secure storage of this data is
essential and, in quite a few cases, it might be mandatory
to keep this data on premise. There are several options to
secure data in the cloud including during storage and at
rest, which include standard security measures like
encryption keys, SSL and certificates. Compliance and
regulatory reasons also require data to be stored securely.
However in many cases, the Cloud vendors provide a
more secure environment than what exists at customer
sites.[14][18]
3) Speed of data access: Since storage resources are
separate from server resources, there is likely to be a
significant latency in accessing large amounts of data.
Large scale data warehouses require high speed
backplanes and dedicated data storage nodes. Until
Cloud providers can provide this level of service or data
warehouses can exist entirely in memory, there will be
limits to the size and performance of data warehouses in
the Cloud. There will also be significant latency if BI
applications exist in the Cloud but the data exists at a
client site, especially when processing and returning
large amounts of data. [18]
4) BI components as a service – So far, only a limited set of
services are available from established BI vendors. This
includes some reporting capabilities and ability to do
visualizations. Most established vendors are yet to
introduce complete product features over the cloud. [15]
5) Integration with on premise data – It is challenging to
integrate on-premise data with cloud components, as it
continues to exist in silos and requires access to data
behind the firewall. [14][15]
6) Lack of control: Tough to get Service Level Agreements
(SLAs) from cloud providers. Data control and data
ownership, reliability of service challenges are some of
the main reasons for client concern. To mitigate this,
organizations should already have in place thorough IT
governance and service delivery standards and models.
[14]
7) Vendor Maturity: Too many cloud BI vendors, hosting
Cloud Business Intelligence – Today’s Need
86
providers with varying offerings, etc. makes it confusing
to choose the right vendor based on required needs and
vendor capabilities. [14]
8) Reliability of service: The services offered by different
vendors vary a lot and back support provided by vendors
is limited. Amazon Web Services doesn‘t have a
customer service phone number. All contact is via email
with a 48 hour service level agreement. This can be
especially frustrating during service disruptions. On
October 7, 2007, Amazon‘s EC2 went down and an
undisclosed number of customers lost their servers and
their data. On February 2, 2008, both Amazon‘s EC2 and
S3 were inaccessible for more than 3 hours. [18]
9) Limited ability to scale-up: Some BI software, such as
SMP databases, in-memory analytics and OLAP scale
better on a single server versus across servers. Currently,
the ability to provision a server beyond 4 CPU‘s (8 cores)
and 16GB‘s is limited. As more providers support 64-bit
platforms and virtualization becomes more advanced,
this limitation will begin to go away.[18]
10) Performance: Limits to the size and performance of data
warehouses in the Cloud, significant latency if BI
applications exist in the Cloud but the data exists at a
client site, especially when processing and returning
large amounts of data. [14]
11) Pricing: It is difficult to budget for computing resources
when those costs are variable in nature. It is especially
difficult when pricing is based on multiple components
such as network traffic, storage size and IP address
requests as well as different pricing tiers for each
component. Also, different vendors base their pricing on
different components. For example, GoGrid charges, in
part, based on memory (each Gigabyte (GB) of RAM
utilized is equivalent to 1 RAM hour) as well as network
traffic. [14][18]
X. CONCLUSION
Cloud is a big part of future Business Intelligence and
offers several advantages in terms of cost benefits, flexibility
of implementation, availability and speed of implementation.
BI on the Cloud offers huge possibilities for removing
barriers to decision making by integrating high volume and
mission critical business processes. Therefore, a Cloud BI
solution may be a feasible answer to the challenges of the
economic crisis. By such a solution, the economic
organization – small, middle-sized or large – may use market
opportunities that under normal conditions (other than
adopting BI or Cloud BI) would not be accessible.
Irrespective of the age of a BI landscape the cloud model can
drive increased BI adoption, improved end-user experience,
better access to analytics and reduced IT dependence.
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Yuvraj Singh Gurjar, MCA, .MCITP – SQL Server,
MCTS – Business Intelligence, Oracle Certified
Associate. He received his MCA degree from University
of Rajasthan in the year of 2002. He started his career as
MIS Programmer with RUIDP (An Asian Development
Bank financed project in Rajasthan (India)). In 2003, he
joined renowned bank Punjab National Bank as
Officer-IT. In 2008 he shifted to Union Bank of India as
Sr. Manager (Software). Later on, in year 2011 he founded KURIOS - A firm
working in the area of Business Intelligence and Performance Management
Solutions. Currently he is working as Chief Technical Consultant with
KURIOS. He has more than 10 years of experience in IT field with
specialization in MIS and BI applications. He is also a student of Ph.D. in the
Department of Computer Science in the Faculty of Computer Science of
Pacific University, Udaipur, India.
Prof. (Dr.) Vijay Singh Rathore, MCA, M.Tech.
(C.S.) PhD (Computer Science), MBA, ICDA-USA,
ICAD-USA, Diploma French & German. Presently, He
is Director in Shree Karni College, & Shree Karni
Institute of Science, Management & Technology
(KISMAT), and Professor (CSE) in Rajasthan College
of Engineering For Women, Jaipur (India). He has 12
years of experience in Technical Education, having done the First PhD in
Computer Science after MCA from University of Rajasthan, Jaipur, written
12 Books on various subjects of Computer Science, published more than 50
research papers at national & international level, supervised 06 PhD
candidates, and currently supervising 08 PhD research scholars in Computer
Science. He is active member of various committees of UGC, RTU, UOR,
UOK, JNU, SGVU, VMOU, NIMSU, AMITYU, MJRPU, IISU, JRSU,
JRNRVU, PU etc. He is reviewer in some reputed International Journals. His
research area is networking & security with special emphasis on Cloud
Computing.
doc_184952556.pdf