Description
What will Business intelligence be like in the future? Rechercher believe the Cloud is a big part of the future of business intelligence.
Abstract—What will Business intelligence be like in the
future? Rechercher believe the Cloud is a big part of the future
of business intelligence. Business intelligence (BI) in the cloud
can be like a big puzzle. Users can jump in and put together
small pieces of the puzzle but until the whole thing is complete
the user will lack an overall view of the big picture. In this paper
reading each section will fill in a piece of the puzzle. "BI in the
Cloud" architecture is only going to be feasible when most of
user source data lives in the cloud already, possibly in
something like SQL Server Data Services or Amazon Simple
DB or Google BigTable; or possibly in a hosted app like
Salesforce.com. Also, Cloud computing enable organizations to
analyze terabytes of data faster and more economically than
ever before
Index Terms—Cloud computing, business intelligence,
platform as a services.
I. INTRODUCTION
In the wake of the economic slowdown, organizations are
increasingly looking for ways to do more with the same
resources; articulate differently - to make every penny, input
and contribution count. In such situations, technologies like
Cloud Computing and Business Intelligence (BI) are
becoming increasingly important in gaining and maintaining
a competitive edge [1]. The future will be very bright for the
use of BI in the cloud, both because of the advantages that
underpin this new computing paradigm as well as the
explosion of digital data that grows each day [2]. Cloud BI is
the new way to do Business Intelligence: instead of
implementing expensive and complex software on-site, the
BI software runs in the Cloud. It is accessible via any web
browser in a so-called software-as-a-service model. There is
no need to install software, or to buy any hardware. And
when you’re computing needs grow, the system will
automatically assign more resources. This elastic scale is
what makes Cloud BI so powerful – user pay for what he use
as opposed to always paying to provision for peak load. With
business intelligence software running in the cloud, it is still
possible to make comprehensive integration with back-end
systems – both within User Company and in the cloud [3].
II. BUSINESS INTELLIGENCE
Business intelligence (BI) has been referred to as the
process of making better decisions through the use of people,
processes, data and related tools and methodologies. The
Manuscript received on July 28, 2011; revised October 16, 2011.
Authors are with Faculty of Computers and Information Helwan
University, Egypt (e-mail: [email protected]; [email protected]).
roots of business intelligence are found in relational
databases, data warehouses and data marts that help
organizing historical information in the hands of business
analysts to generate reporting that informs executives and
senior departmental managers of strategic and tactical trends
and opportunities [4].
In recent years, business intelligence has also come to rely
on near real-time operational data found in systems including
enterprise resource planning (ERP), customer relationship
management (CRM), supply chain, marketing and other
databases. “Operational” BI is meant to provision many more
functions in the organization with role-specific dashboards
and scorecards and is increasingly tied to the topics of
performance management and business process management.
Inherent to any form of BI is the notion of data quality,
consistent and dependable data and the processes involved in
its creation and maintenance [3]. Business Intelligence
involves intelligent reporting on top of existing data which
helps in prompt and actionable decision making. These
decision making might involve "geography based investment
decision for a multinational company" or even a "buy
decision for a product by the consumer". BI has evolved over
time but the key components still continue to hold true. It is
still necessary to be able to aggregate the factual data from
various data sources and doing involved transformations.
This data then either needs to be stored in a data mart or
warehouse to enable reporting and analysis on top or it could
then be further aggregated into metrics which are then
reported. Nevertheless the ability to perform BI involves key
aspects related to data management and computationally
expensive analytics or reporting [1]. Cloud computing is
transforming the economics of BI and opens up the
opportunity for smaller enterprises to compete using the
insights 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-services basis, avoiding the need to download and set up
free software downloads [2].
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 create in a few hours
or days, used for the necessary period, and then the cloud
cluster cancelled, leaving behind no redundant hardware or
software licenses. The cloud makes short term projects very
economical [5].
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
The Cloud Computing: The Future of BI in the Cloud
Shimaa Ouf and Mona Nasr
International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011
750
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.
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 [2].
III. CLOUD BUSINESS INTELLIGENCE'S PERFECTLY STORMY
FUTURE
Cloud BI represents a way for reporting and analysis
solutions to be developed, installed, and consumed more
easily due to its lower cost and easier deployment. Ideally, a
cloud-based business intelligence platform makes use of
infrastructure-as-a-service (IaaS), complements and extends
today's platform-as-a-service (PAAS), utilizes an on-demand,
virtualized, elastic software and hardware environment, and
delivers application-level functionality as a service
(commonly referred to as software-as-a-service) [5].
Additionally, a business intelligence tool should easily
deploy and even migrate from on-premise to the cloud (and
back), providing a new kind of Web-based flexibility that
accompanies the most modern platform architecture.
Typically, a cloud-based BI platform is used to solve one of
three primary customer needs [6]:
1) As a horizontal BI tool to deliver standalone, internally
facing reporting and analysis applications—probably
using a traditional relational database (or data mart) as
the primary source data system.
2) As an application framework or pre-built reporting and
analysis template for systems integrators to use for
assembling customer-specific solutions more quickly.
These solutions are probably function- or
domain-specific and contain reusable components and
application logic (but are assembled uniquely for each
customer).
3) As a development platform that enables embeddable,
externally-facing applications that solve a
function-specific data analysis problem (for example,
CRM analytics, financial analytics, or supply chain
analytics). In this case, an ISV (or an enterprise IT team
with appropriate skills) would probably use the BI
platform to deliver reporting and analytics as a
well-defined and well-featured layer within its larger
application. The result is an analytic application that
solves a customer problem with minimal customization
and that is ideally delivered using a
software-as-a-service architecture on top of a cloud
infrastructure.
The Cloud Impact on Data Centers:
A mix of private and public clouds will become the norm.
Many organizations and lines of business will bypass IT to
secure cloud-based infrastructure and SaaS applications.
- Virtualization and cloud-based infrastructure will become
the norm.
- Power and cooling efficiency and green data centers will
become critical and the norm.
- A new breed of cloud computing skills will become
common in data center operations.
- Private cloud technology, such as cloud storage, will find
its way to the IT organizations.
- Service oriented architectures (SOAs) will drive the IT
infrastructure and application architecture.
The Cloud Impact on IT Organizations:
- We will see a transformation from programming to
service integration and customization.
- With the cloud and SaaS usage-based pricing, IT
budgets will transform from CAPEX to more OPEX,
opening the door for immediate IT investments.
The Cloud Impact on Cloud Computing Vendors:
- Significant market growth and momentum will fuel hyper
growth.
- Cloud infrastructure utilization and efficiency will become
critical to success.
- Power and cooling costs will become enormously
important factors to profitability.
- Cloud infrastructure and SaaS vendors will become the
new giants of the industry where the IT operations shop
for infrastructure and SaaS applications.
- Merger and acquisition frenzy will become the norm for
hyper growth.
The Cloud Impact on SaaS Vendors:
- There will be hyper growth in the number of SaaS
applications and vendors.
- Venture spending will grow significantly.
- New requirements and standards for APIs, reporting,
security and service-level agreements (SLAs) will
emerge.
- SaaS vendors will become the main source of applications
[7].
The Cloud Impact on Infrastructure Vendors:
- Server, storage and networking customer influence will
decrease.
- Server, storage and networking vendors will be selling to
cloud vendors.
- Infrastructure vendors will be fighting for mind-share with
both cloud and SaaS vendors.
- Infrastructure vendors will lose contact with many
enterprise customers as they flock to cloud infrastructure
and SaaS.
- Merger and acquisition frenzy will become the norm for
survival.
- Infrastructure vendors will experience a dramatic change
of business model.
The Cloud Impact on Application Software Vendors:
- Application software vendors will have to adopt the SaaS
model to survive.
- They will lose business to SaaS companies.
- Software licensing will dramatically change.
- Merger and acquisition frenzy will become the norm for
survival [4], [8].
International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011
751
IV. THE BENEFITS OF CLOUD COMPUTING FOR BUSINESS
INTELLIGENC
Utilizing SaaS solutions are an effective way to minimize
costs and maximize performance. But, there are many
noteworthy benefits of Clouding BI and using a BI reporting
and analytics tool as a SaaS application:
1) Fast, easy and inexpensive deployment: Lack of
infrastructure set up means a faster Return On
Investment (ROI).
2) No hardware and setup expenditure: Reduced
implementation costs equate to a low Total Cost of
Ownership (TCO).
3) Reliability: Cloud Computing that uses multiple
redundant sites can provide reliable and secure locations
for data storage and are ideal for disaster recovery and
business continuity
4) No capital expenditure (lowers entry barriers): No
capital expenditure normally associated with setting-up
traditional IT environments means the benefits of BI can
be rolled out faster to more people within your
organization [2].
5) Multi-tenancy environment (do more with less): The
multi-tenancy nature of Cloud Computing means that
cost and resources can be spread across a large number
of users
6) Free automated software upgrades and maintenance: The
service provider owns and hosts the software, and so
users can benefit from ongoing upgrades and
maintenance without the associated costs, time
constraints and drain on IT resources
7) Flexibility and scalability associated with low ongoing
total software costs: Freedom from upgrade and
maintenance expenses mean that it’s easy to keep fiscal
control over IT projects and have the flexibility to scale
up or down usage as needs change
8) Only pay for what you use: SaaS ensures that users only
pay for what they use, eliminating wastage, resulting in
low ongoing software costs.
9) Fast and easy scalability: Cloud solutions can support
large numbers of simultaneous users, meaning that
customers can swiftly increase their software usage
without the cost or delay of having to deploy and install
additional hardware.
10) Flexibility: Cloud BI solutions have the flexibility to be
altered quickly to give technical users access to new data
analysis and reporting features
11) Improved data sharing capabilities: Cloud applications
enable easy cross-location data sharing and remote data
access as they are deployed via the internet and outside a
company’s firewall. [8]
12) Low risk and high reward: Low TCO and overall
resource investment means that SaaS represents a low
risk venture that retains high reward potential.
V. THE PROPOSED MODEL
The future will be very bright for the use of BI in the cloud,
both because of the advantages that underpin this new
computing paradigm as well as the explosion of digital data
that grows each day. "BI in the Cloud" architecture is only
going to be feasible when most of user’s source data lives in
the cloud already, possibly in something like SQL Server
Data Services or Amazon Simple DB or Google BigTable; or
possibly in a hosted app. like Salesforce.com. Cloud BI is the
new way to do Business Intelligence instead of implementing
expensive and complex software on-site, the BI software runs
in the Cloud. It is accessible via any web browser in a
so-called software-as-a-service model. There is no need to
install software, or to buy any hardware. And when users are
computing needs grow, the system will automatically assign
more resources. This elastic scale is what makes Cloud BI so
powerful users pay for what they use as opposed to always
paying to provision for peak load.
Starting at the back, the first objection raised to a purely
'BI in the cloud' architecture is that user has got to upload his
data to it somehow. Users can use the tools and applications
they're familiar with to work from anywhere. With the choice
and flexibility of cloud computing, users’ businesses can
deploy services on-premises, in the cloud, or a blend of both.
And, our solutions are all built on a unified productivity
platform that's not only cost-effective, but gives user the
ability to respond as business needs evolve.
Fig. 1. Business intelligence proposed model
International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011
752
Component of the proposed model (Business Intelligence
in the Cloud The proposed model represents a new
environment atmosphere for the business intelligence to
make the ability to shorten BI implementation windows,
reduced cost for BI programs, Ability to add environments
for testing, proof-of-concepts and upgrades. This
environment represented as follows:
A. Cloud Computing
The cloud provides a virtually unlimited pool of
computing power, storage and memory. However, these
resources are delivered in discrete modules. Each node
consists of “standard” units of processing power, storage
space and memory. While the amounts may vary (by service
provider, price point, etc.) and they may increase over time,
the Cloud’s pool of resources is a large grid of
interchangeable, industry-standard computing resources.
Achieving true scalability requires a database architecture
that can fully maximize this pool of resources.
A shared nothing, massively parallel database architecture
is particularly designed to take advantage of multiple units of
computing resources. In the proposed model we use
Microsoft windows azure and we describe how cloud
computing can be affected by the business intelligence?
B. AppFabric
Helps connect applications and services in the cloud or
on-premise, for example applications running on Windows
Azure, Windows Server and a number of other platforms
including Java, Ruby, PHP and others.
C. Platform as Services
Platform as services is a cloud services operating system
that serves as the development, service hosting and service
management environment. It is a flexible cloud–computing
offering that lets user focus on solving business problems and
addressing customer needs. No need to invest upfront on
expensive infrastructure. Pay only for what user use, scale up
when he need capacity and pull it back when he don’t. Users
handle all the patches and maintenance all in a secure
environment. Cloud computing as a platform supports
multiple languages and integrates with existing on-premises
environment. In addition, it supports popular standards,
protocols and languages.
1) Database Platform
Business intelligence can be moved to cloud using
Platform as services. It is a cloud-based relational database
service built on database technologies. It provides a highly
available, scalable, multi-tenant database service hosted in
the cloud. Platform as a services helps to ease provisioning
and deployment of multiple databases. Users do not have to
install setup and patch or manage any software. High
availability and fault tolerance is built-in and no physical
administration is Database Platform of their existing
on-premises databases. Database Platform delivers scale to
meet the needs of the entire organization and provides IT
with flexibility to respond quickly to the evolving needs of
the business. Organizations can bring great new experiences
and empowerment to their end users on a familiar IT
infrastructure that's more manageable and cost effective.
Database Platform includes features to help user manage
critical data assets company-wide and across diverse systems,
helping to ensure integrity of information. Mostly master data
is maintained into the permanent staging database and
synchronized with delta from extracts and this piece of data is
not that huge too. With a proper design and dissecting
permanent staging area into two parts, by moving
master tables to the cloud and delta records to a temporary
staging area, the intermediate need of a staging server can be
eliminated. Database Platform is a fit for this, as we just need
to store the staging data and queries are not that complex.
And just for storing of this staging data, we do not need an
enterprise level database and access to this data is not that
frequent too.
2) BI Infrastructure
The Data Layer: The data layer is responsible for storing
structured and unstructured data for management support.
Regarding structured data, the central component is the data
warehouse (DWH) [3]. A DWH is commonly defined as a
“subject-oriented, integrated, time-variant, and non volatile
collection of data in support of management’s
decision-making process”. Many current realizations of
DWHs are based on so called core DWHs .Core DWHs are
usually not used as a direct source for analysis systems, but
rather distribute data to individual Data Marts. Data Marts
keep excerpts of application specific data. More recently;
there has been a shift towards DWH infrastructures that are
integrated with operational systems. This is usually achieved
by the introduction of an Operational Data Store (ODS) that
is designed to keep real time data on a transactional level for
time critical tasks. ODS/DWH architectures allow to build
Closed-loop and Active Data Warehousing solutions. To
feed the various data storages, ETL (Extract-Transform-Load)
tools are needed. An ETL tool supports the extraction and
transformation of data from heterogeneous source systems.
The transformation includes filtering out syntactical and
semantic errors, harmonizing data from different sources, as
well as aggregating and enriching it. For the storage and
administration of unstructured data, Content Management
Systems (CMS) and Document Management Systems (DMS)
are inserted into the data layer [8].
The Logic Layer: The Logic Layer provides functionality
to analyze structured data or unstructured content and
supports the distribution of relevant knowledge among
different users. The most salient tools in BI environments are
reporting, data mining, and OLAP tools: Reporting tools
present quantitative data in a report-oriented format that
might include numbers, charts, or business graphics. OLAP
denotes a concept for interactive and multidimensional
analysis of aggregated quantitative business facts. Data
mining tools support the identification of hidden patterns in
large volumes of structured data based on statistical methods
like association analysis, classification, or clustering. Data
mining and similar model based tools are also referred to by
the term Advanced Analytics [4].
The Access Layer: The Access Layer allows the user to
conveniently use all relevant functions of the Logic Layer in
an integrated fashion within the confines of defined user roles
and user rights.
International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011
753
VI. CONCLOSION AND FUTURE WORK
The development of business Intelligence field cannot
ignore the cloud computing trends. There are many benefits
from using the cloud computing for business intelligence. It
influences the way business intelligence software projects are
managed which it provide a virtually unlimited pool of
computing power, storage space and memory for the business
intelligence infrastructure, so our proposed model represents
a new environment atmosphere for the business intelligence
that help in shortening BI implementation windows,
reduction of cost for BI programs, enabling to add
environments for testing, proof-of-concepts and upgrades.
Business Intelligence in the cloud has been developed in
order to enhance the efficiency and productivity of business
intelligence and increase the performance of BI software. In
the future we are aiming to develop business intelligence by
using web 3.0 technologies. In the future we are aiming to
develop business intelligence by using web 3.0 technologies.
REFERENCES
[1] Dialogic Corporation, “Introduction to cloud computing,” white paper.
[2] R. Buyya, C. S. Yeo, and S. Venugopal, “Market-Oriented Cloud
Computing: Vision, Hype and Reality for Delivering IT Services as
Computing Utilities.” Future Generation Computer Systems, vol. 25,
pp. 599-616, 2009.
[3] W. H. Inmon, “Building the Data Warehouse,” John Wiley Sons, Inc.,
New York (NY, USA), 2005.
[4] J. Dibbern, T. Goles, R. Hirschheim, and B. Jayatilaka, “Information
Systems Outsourcing. A Survey and Analysis of the Literature,” 2004.
[5] P. Relan and Sibbingz, “Business intelligence for the cloud software
stack helps gaming sites quickly adapt to evolving customer needs,”
2009.
[6] Baars, Henning and Kemper, et al, (2010) Business Intelligence in
[7] P. Pocatilu, F. Alecu, et al. ( 2010, January) "Measuring the Efficiency
of Cloud Computing for E-learning Systems," Romania .
[8] Omnipress, Madison “Service-Based Approach as a Prerequisite for BI
Governance,” Proceedings of the 14th Americas Conference on
Information Systems (AMCIS), Toronto.
[9] Wiley “Withee, Microsoft Business Intelligence for Dummies,”
Publishing, Inc.
International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011
754
doc_121371231.pdf
What will Business intelligence be like in the future? Rechercher believe the Cloud is a big part of the future of business intelligence.
Abstract—What will Business intelligence be like in the
future? Rechercher believe the Cloud is a big part of the future
of business intelligence. Business intelligence (BI) in the cloud
can be like a big puzzle. Users can jump in and put together
small pieces of the puzzle but until the whole thing is complete
the user will lack an overall view of the big picture. In this paper
reading each section will fill in a piece of the puzzle. "BI in the
Cloud" architecture is only going to be feasible when most of
user source data lives in the cloud already, possibly in
something like SQL Server Data Services or Amazon Simple
DB or Google BigTable; or possibly in a hosted app like
Salesforce.com. Also, Cloud computing enable organizations to
analyze terabytes of data faster and more economically than
ever before
Index Terms—Cloud computing, business intelligence,
platform as a services.
I. INTRODUCTION
In the wake of the economic slowdown, organizations are
increasingly looking for ways to do more with the same
resources; articulate differently - to make every penny, input
and contribution count. In such situations, technologies like
Cloud Computing and Business Intelligence (BI) are
becoming increasingly important in gaining and maintaining
a competitive edge [1]. The future will be very bright for the
use of BI in the cloud, both because of the advantages that
underpin this new computing paradigm as well as the
explosion of digital data that grows each day [2]. Cloud BI is
the new way to do Business Intelligence: instead of
implementing expensive and complex software on-site, the
BI software runs in the Cloud. It is accessible via any web
browser in a so-called software-as-a-service model. There is
no need to install software, or to buy any hardware. And
when you’re computing needs grow, the system will
automatically assign more resources. This elastic scale is
what makes Cloud BI so powerful – user pay for what he use
as opposed to always paying to provision for peak load. With
business intelligence software running in the cloud, it is still
possible to make comprehensive integration with back-end
systems – both within User Company and in the cloud [3].
II. BUSINESS INTELLIGENCE
Business intelligence (BI) has been referred to as the
process of making better decisions through the use of people,
processes, data and related tools and methodologies. The
Manuscript received on July 28, 2011; revised October 16, 2011.
Authors are with Faculty of Computers and Information Helwan
University, Egypt (e-mail: [email protected]; [email protected]).
roots of business intelligence are found in relational
databases, data warehouses and data marts that help
organizing historical information in the hands of business
analysts to generate reporting that informs executives and
senior departmental managers of strategic and tactical trends
and opportunities [4].
In recent years, business intelligence has also come to rely
on near real-time operational data found in systems including
enterprise resource planning (ERP), customer relationship
management (CRM), supply chain, marketing and other
databases. “Operational” BI is meant to provision many more
functions in the organization with role-specific dashboards
and scorecards and is increasingly tied to the topics of
performance management and business process management.
Inherent to any form of BI is the notion of data quality,
consistent and dependable data and the processes involved in
its creation and maintenance [3]. Business Intelligence
involves intelligent reporting on top of existing data which
helps in prompt and actionable decision making. These
decision making might involve "geography based investment
decision for a multinational company" or even a "buy
decision for a product by the consumer". BI has evolved over
time but the key components still continue to hold true. It is
still necessary to be able to aggregate the factual data from
various data sources and doing involved transformations.
This data then either needs to be stored in a data mart or
warehouse to enable reporting and analysis on top or it could
then be further aggregated into metrics which are then
reported. Nevertheless the ability to perform BI involves key
aspects related to data management and computationally
expensive analytics or reporting [1]. Cloud computing is
transforming the economics of BI and opens up the
opportunity for smaller enterprises to compete using the
insights 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-services basis, avoiding the need to download and set up
free software downloads [2].
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 create in a few hours
or days, used for the necessary period, and then the cloud
cluster cancelled, leaving behind no redundant hardware or
software licenses. The cloud makes short term projects very
economical [5].
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
The Cloud Computing: The Future of BI in the Cloud
Shimaa Ouf and Mona Nasr
International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011
750
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.
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 [2].
III. CLOUD BUSINESS INTELLIGENCE'S PERFECTLY STORMY
FUTURE
Cloud BI represents a way for reporting and analysis
solutions to be developed, installed, and consumed more
easily due to its lower cost and easier deployment. Ideally, a
cloud-based business intelligence platform makes use of
infrastructure-as-a-service (IaaS), complements and extends
today's platform-as-a-service (PAAS), utilizes an on-demand,
virtualized, elastic software and hardware environment, and
delivers application-level functionality as a service
(commonly referred to as software-as-a-service) [5].
Additionally, a business intelligence tool should easily
deploy and even migrate from on-premise to the cloud (and
back), providing a new kind of Web-based flexibility that
accompanies the most modern platform architecture.
Typically, a cloud-based BI platform is used to solve one of
three primary customer needs [6]:
1) As a horizontal BI tool to deliver standalone, internally
facing reporting and analysis applications—probably
using a traditional relational database (or data mart) as
the primary source data system.
2) As an application framework or pre-built reporting and
analysis template for systems integrators to use for
assembling customer-specific solutions more quickly.
These solutions are probably function- or
domain-specific and contain reusable components and
application logic (but are assembled uniquely for each
customer).
3) As a development platform that enables embeddable,
externally-facing applications that solve a
function-specific data analysis problem (for example,
CRM analytics, financial analytics, or supply chain
analytics). In this case, an ISV (or an enterprise IT team
with appropriate skills) would probably use the BI
platform to deliver reporting and analytics as a
well-defined and well-featured layer within its larger
application. The result is an analytic application that
solves a customer problem with minimal customization
and that is ideally delivered using a
software-as-a-service architecture on top of a cloud
infrastructure.
The Cloud Impact on Data Centers:
A mix of private and public clouds will become the norm.
Many organizations and lines of business will bypass IT to
secure cloud-based infrastructure and SaaS applications.
- Virtualization and cloud-based infrastructure will become
the norm.
- Power and cooling efficiency and green data centers will
become critical and the norm.
- A new breed of cloud computing skills will become
common in data center operations.
- Private cloud technology, such as cloud storage, will find
its way to the IT organizations.
- Service oriented architectures (SOAs) will drive the IT
infrastructure and application architecture.
The Cloud Impact on IT Organizations:
- We will see a transformation from programming to
service integration and customization.
- With the cloud and SaaS usage-based pricing, IT
budgets will transform from CAPEX to more OPEX,
opening the door for immediate IT investments.
The Cloud Impact on Cloud Computing Vendors:
- Significant market growth and momentum will fuel hyper
growth.
- Cloud infrastructure utilization and efficiency will become
critical to success.
- Power and cooling costs will become enormously
important factors to profitability.
- Cloud infrastructure and SaaS vendors will become the
new giants of the industry where the IT operations shop
for infrastructure and SaaS applications.
- Merger and acquisition frenzy will become the norm for
hyper growth.
The Cloud Impact on SaaS Vendors:
- There will be hyper growth in the number of SaaS
applications and vendors.
- Venture spending will grow significantly.
- New requirements and standards for APIs, reporting,
security and service-level agreements (SLAs) will
emerge.
- SaaS vendors will become the main source of applications
[7].
The Cloud Impact on Infrastructure Vendors:
- Server, storage and networking customer influence will
decrease.
- Server, storage and networking vendors will be selling to
cloud vendors.
- Infrastructure vendors will be fighting for mind-share with
both cloud and SaaS vendors.
- Infrastructure vendors will lose contact with many
enterprise customers as they flock to cloud infrastructure
and SaaS.
- Merger and acquisition frenzy will become the norm for
survival.
- Infrastructure vendors will experience a dramatic change
of business model.
The Cloud Impact on Application Software Vendors:
- Application software vendors will have to adopt the SaaS
model to survive.
- They will lose business to SaaS companies.
- Software licensing will dramatically change.
- Merger and acquisition frenzy will become the norm for
survival [4], [8].
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751
IV. THE BENEFITS OF CLOUD COMPUTING FOR BUSINESS
INTELLIGENC
Utilizing SaaS solutions are an effective way to minimize
costs and maximize performance. But, there are many
noteworthy benefits of Clouding BI and using a BI reporting
and analytics tool as a SaaS application:
1) Fast, easy and inexpensive deployment: Lack of
infrastructure set up means a faster Return On
Investment (ROI).
2) No hardware and setup expenditure: Reduced
implementation costs equate to a low Total Cost of
Ownership (TCO).
3) Reliability: Cloud Computing that uses multiple
redundant sites can provide reliable and secure locations
for data storage and are ideal for disaster recovery and
business continuity
4) No capital expenditure (lowers entry barriers): No
capital expenditure normally associated with setting-up
traditional IT environments means the benefits of BI can
be rolled out faster to more people within your
organization [2].
5) Multi-tenancy environment (do more with less): The
multi-tenancy nature of Cloud Computing means that
cost and resources can be spread across a large number
of users
6) Free automated software upgrades and maintenance: The
service provider owns and hosts the software, and so
users can benefit from ongoing upgrades and
maintenance without the associated costs, time
constraints and drain on IT resources
7) Flexibility and scalability associated with low ongoing
total software costs: Freedom from upgrade and
maintenance expenses mean that it’s easy to keep fiscal
control over IT projects and have the flexibility to scale
up or down usage as needs change
8) Only pay for what you use: SaaS ensures that users only
pay for what they use, eliminating wastage, resulting in
low ongoing software costs.
9) Fast and easy scalability: Cloud solutions can support
large numbers of simultaneous users, meaning that
customers can swiftly increase their software usage
without the cost or delay of having to deploy and install
additional hardware.
10) Flexibility: Cloud BI solutions have the flexibility to be
altered quickly to give technical users access to new data
analysis and reporting features
11) Improved data sharing capabilities: Cloud applications
enable easy cross-location data sharing and remote data
access as they are deployed via the internet and outside a
company’s firewall. [8]
12) Low risk and high reward: Low TCO and overall
resource investment means that SaaS represents a low
risk venture that retains high reward potential.
V. THE PROPOSED MODEL
The future will be very bright for the use of BI in the cloud,
both because of the advantages that underpin this new
computing paradigm as well as the explosion of digital data
that grows each day. "BI in the Cloud" architecture is only
going to be feasible when most of user’s source data lives in
the cloud already, possibly in something like SQL Server
Data Services or Amazon Simple DB or Google BigTable; or
possibly in a hosted app. like Salesforce.com. Cloud BI is the
new way to do Business Intelligence instead of implementing
expensive and complex software on-site, the BI software runs
in the Cloud. It is accessible via any web browser in a
so-called software-as-a-service model. There is no need to
install software, or to buy any hardware. And when users are
computing needs grow, the system will automatically assign
more resources. This elastic scale is what makes Cloud BI so
powerful users pay for what they use as opposed to always
paying to provision for peak load.
Starting at the back, the first objection raised to a purely
'BI in the cloud' architecture is that user has got to upload his
data to it somehow. Users can use the tools and applications
they're familiar with to work from anywhere. With the choice
and flexibility of cloud computing, users’ businesses can
deploy services on-premises, in the cloud, or a blend of both.
And, our solutions are all built on a unified productivity
platform that's not only cost-effective, but gives user the
ability to respond as business needs evolve.
Fig. 1. Business intelligence proposed model
International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011
752
Component of the proposed model (Business Intelligence
in the Cloud The proposed model represents a new
environment atmosphere for the business intelligence to
make the ability to shorten BI implementation windows,
reduced cost for BI programs, Ability to add environments
for testing, proof-of-concepts and upgrades. This
environment represented as follows:
A. Cloud Computing
The cloud provides a virtually unlimited pool of
computing power, storage and memory. However, these
resources are delivered in discrete modules. Each node
consists of “standard” units of processing power, storage
space and memory. While the amounts may vary (by service
provider, price point, etc.) and they may increase over time,
the Cloud’s pool of resources is a large grid of
interchangeable, industry-standard computing resources.
Achieving true scalability requires a database architecture
that can fully maximize this pool of resources.
A shared nothing, massively parallel database architecture
is particularly designed to take advantage of multiple units of
computing resources. In the proposed model we use
Microsoft windows azure and we describe how cloud
computing can be affected by the business intelligence?
B. AppFabric
Helps connect applications and services in the cloud or
on-premise, for example applications running on Windows
Azure, Windows Server and a number of other platforms
including Java, Ruby, PHP and others.
C. Platform as Services
Platform as services is a cloud services operating system
that serves as the development, service hosting and service
management environment. It is a flexible cloud–computing
offering that lets user focus on solving business problems and
addressing customer needs. No need to invest upfront on
expensive infrastructure. Pay only for what user use, scale up
when he need capacity and pull it back when he don’t. Users
handle all the patches and maintenance all in a secure
environment. Cloud computing as a platform supports
multiple languages and integrates with existing on-premises
environment. In addition, it supports popular standards,
protocols and languages.
1) Database Platform
Business intelligence can be moved to cloud using
Platform as services. It is a cloud-based relational database
service built on database technologies. It provides a highly
available, scalable, multi-tenant database service hosted in
the cloud. Platform as a services helps to ease provisioning
and deployment of multiple databases. Users do not have to
install setup and patch or manage any software. High
availability and fault tolerance is built-in and no physical
administration is Database Platform of their existing
on-premises databases. Database Platform delivers scale to
meet the needs of the entire organization and provides IT
with flexibility to respond quickly to the evolving needs of
the business. Organizations can bring great new experiences
and empowerment to their end users on a familiar IT
infrastructure that's more manageable and cost effective.
Database Platform includes features to help user manage
critical data assets company-wide and across diverse systems,
helping to ensure integrity of information. Mostly master data
is maintained into the permanent staging database and
synchronized with delta from extracts and this piece of data is
not that huge too. With a proper design and dissecting
permanent staging area into two parts, by moving
master tables to the cloud and delta records to a temporary
staging area, the intermediate need of a staging server can be
eliminated. Database Platform is a fit for this, as we just need
to store the staging data and queries are not that complex.
And just for storing of this staging data, we do not need an
enterprise level database and access to this data is not that
frequent too.
2) BI Infrastructure
The Data Layer: The data layer is responsible for storing
structured and unstructured data for management support.
Regarding structured data, the central component is the data
warehouse (DWH) [3]. A DWH is commonly defined as a
“subject-oriented, integrated, time-variant, and non volatile
collection of data in support of management’s
decision-making process”. Many current realizations of
DWHs are based on so called core DWHs .Core DWHs are
usually not used as a direct source for analysis systems, but
rather distribute data to individual Data Marts. Data Marts
keep excerpts of application specific data. More recently;
there has been a shift towards DWH infrastructures that are
integrated with operational systems. This is usually achieved
by the introduction of an Operational Data Store (ODS) that
is designed to keep real time data on a transactional level for
time critical tasks. ODS/DWH architectures allow to build
Closed-loop and Active Data Warehousing solutions. To
feed the various data storages, ETL (Extract-Transform-Load)
tools are needed. An ETL tool supports the extraction and
transformation of data from heterogeneous source systems.
The transformation includes filtering out syntactical and
semantic errors, harmonizing data from different sources, as
well as aggregating and enriching it. For the storage and
administration of unstructured data, Content Management
Systems (CMS) and Document Management Systems (DMS)
are inserted into the data layer [8].
The Logic Layer: The Logic Layer provides functionality
to analyze structured data or unstructured content and
supports the distribution of relevant knowledge among
different users. The most salient tools in BI environments are
reporting, data mining, and OLAP tools: Reporting tools
present quantitative data in a report-oriented format that
might include numbers, charts, or business graphics. OLAP
denotes a concept for interactive and multidimensional
analysis of aggregated quantitative business facts. Data
mining tools support the identification of hidden patterns in
large volumes of structured data based on statistical methods
like association analysis, classification, or clustering. Data
mining and similar model based tools are also referred to by
the term Advanced Analytics [4].
The Access Layer: The Access Layer allows the user to
conveniently use all relevant functions of the Logic Layer in
an integrated fashion within the confines of defined user roles
and user rights.
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753
VI. CONCLOSION AND FUTURE WORK
The development of business Intelligence field cannot
ignore the cloud computing trends. There are many benefits
from using the cloud computing for business intelligence. It
influences the way business intelligence software projects are
managed which it provide a virtually unlimited pool of
computing power, storage space and memory for the business
intelligence infrastructure, so our proposed model represents
a new environment atmosphere for the business intelligence
that help in shortening BI implementation windows,
reduction of cost for BI programs, enabling to add
environments for testing, proof-of-concepts and upgrades.
Business Intelligence in the cloud has been developed in
order to enhance the efficiency and productivity of business
intelligence and increase the performance of BI software. In
the future we are aiming to develop business intelligence by
using web 3.0 technologies. In the future we are aiming to
develop business intelligence by using web 3.0 technologies.
REFERENCES
[1] Dialogic Corporation, “Introduction to cloud computing,” white paper.
[2] R. Buyya, C. S. Yeo, and S. Venugopal, “Market-Oriented Cloud
Computing: Vision, Hype and Reality for Delivering IT Services as
Computing Utilities.” Future Generation Computer Systems, vol. 25,
pp. 599-616, 2009.
[3] W. H. Inmon, “Building the Data Warehouse,” John Wiley Sons, Inc.,
New York (NY, USA), 2005.
[4] J. Dibbern, T. Goles, R. Hirschheim, and B. Jayatilaka, “Information
Systems Outsourcing. A Survey and Analysis of the Literature,” 2004.
[5] P. Relan and Sibbingz, “Business intelligence for the cloud software
stack helps gaming sites quickly adapt to evolving customer needs,”
2009.
[6] Baars, Henning and Kemper, et al, (2010) Business Intelligence in
[7] P. Pocatilu, F. Alecu, et al. ( 2010, January) "Measuring the Efficiency
of Cloud Computing for E-learning Systems," Romania .
[8] Omnipress, Madison “Service-Based Approach as a Prerequisite for BI
Governance,” Proceedings of the 14th Americas Conference on
Information Systems (AMCIS), Toronto.
[9] Wiley “Withee, Microsoft Business Intelligence for Dummies,”
Publishing, Inc.
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