Jn2516891694

S.NASIRA TABASSUM, SK.ALTHAF AHAMMED / International Journal of Engineering
Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1689-1694
Data Warehousing and Business Analytics Implementation
S.NASIRA TABASSUM SK.ALTHAF AHAMMED

Abstract
Data warehousing (DW) has emerged as doing data mining analysis, as well as unstructured
one of the most powerful technology innovations data (thus the need for content management
in recent years to support organization-wide systems) thus providing managerial decision support
decision making and has become a key for complex business questions. DW is also an
component in the information technology (IT) enabling technology for data mining, customer-
infrastructure Data warehousing methodologies relationship management, and other business-
share a common set of tasks, including business intelligence applications. Although data warehouses
requirements analysis, data design, architectural have been around for quite some time, they have
design, implementation and deployment. This been plagued by high failure rates and limited
paper provides an overview of Analytics. It spread or use. Drawing upon past research on the
explains how data from a web server's log can be adoption and diffusion of innovations and on the
harvested to generate useful and actionable implementation of information systems (IS), we
business intelligence, particularly when the data examine the key organizational and innovation
is combined with existing customer and sales data factors that influence the infusion (diffusion) of DW
in a Data Warehouse. Business Intelligence is within organizations and also examine if more
often confused and sometimes used intermittently extensive infusion leads to improved organizational
with data warehouse concepts. We discuss the outcomes. Business intelligence is closely related to
similarities and differences between these two data warehousing. This section discusses business
concepts in this paper. intelligence, as well as the relationship between
business intelligence and data warehousing. As the
Keywords: data warehouse, business intelligence old Chinese saying goes, "To accomplish a goal,
make sure the proper tools are selected." This is
1. Introduction especially true when the goal is to achieve business
Business Intelligence refers to a set of intelligence. Given the complexity of the data
methods and techniques that are used by warehousing system and the cross-departmental
organizations for tactical and strategic decision implications of the project, it is easy to see why the
making. It leverages technologies that focus on proper selection of business intelligence software
counts, statistics and business objectives to improve and personnel is very important. After the tools and
business performance. A Data Warehouse (DW) is team personnel selections are made, the data
simply a consolidation of data from a variety of warehouse design can begin phase.
sources that is designed to support strategic and
tactical decision making. Its main purpose is to 2. Business Intelligence and Data Warehouse
provide a coherent picture of the business at a point Business intelligence (BI) is defined as the
in time. Using various Data Warehousing toolsets, ability for an organization to take all its capabilities
users are able to run online queries and 'mine" their and convert them into knowledge. This produces
data. Many successful companies have been large amounts of information that can lead to the
investing large sums of money in business development of new opportunities. Identifying these
intelligence and data warehousing tools and opportunities, and implementing an effective
technologies. They believe that up-to-date, accurate strategy, can provide a competitive market
and integrated information about their supply chain, advantage and long-term stability within the
products and customers are critical for their very organization's industry. BI technologies provide
survival. This website introduces some key Data historical, current and predictive views of business
Warehousing concepts and terminology. It explains operations. Common functions of business
Data Warehousing from a historical context and the intelligence technologies are reporting, online
underlying business and technology drivers that are analytical processing , analytics, data
making Data Warehouses a hot commodity. mining, process mining, complex event
A data warehousing (or data mart) system is the processing, business performance management
backend, or the infrastructural, component for , benchmarking , text mining , predictive
achieving business intelligence. Business analytics and prescriptive analytics. Business
intelligence also includes the insight gained from intelligence aims to support better business decision-

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Vol. 2, Issue 5, September- October 2012, pp.
making. Thus a BI system can be called a decision 4. Business Intelligence Services
support system(DSS). Though the term business 3. Related Work
intelligence is sometimes used as a synonym Dell helps create a successful business
for competitive intelligence (because they both intelligence solution.
support decision making), BI uses technologies, Business needs and current state of information
processes, and applications to analyze mostly assets are analyzed to define a BI strategy that may
internal, structured data and business processes include new or revised dashboards, redesigned data
while competitive intelligence gathers, analyzes and models and integration of source systems.
disseminates information with a topical focus on Business Intelligence Advisory Services
company competitors. If understood broadly, The Dell Business Intelligence Advisory Services
business intelligence can include the subset of include:
competitive intelligence. Often BI applications use  Framework for establishing a business
data gathered from a data warehouse or a data mart. intelligence competency center (BICC)
However, not all data warehouses are used for  Plans for building out a successful business
business intelligence, nor do all business process management (BPM) program, including
intelligence applications require a data warehouse. a collaborative approach to workshops and
In order to distinguish between concepts of business process flows
intelligence and data warehouses, Forrester  Business solutions defined and aligned to
Research often defines business intelligence in one corporate strategy
of two ways: Business Intelligence Advisory Services helps to
Using a broad definition: "Business develop a comprehensive business intelligence (BI)
Intelligence is a set of methodologies, processes, strategy and roadmap that aligns with strategic
architectures, and technologies that transform raw enterprise goals and get the information needed to
data into meaningful and useful information used to make fast, fact-based decisions.
enable more effective strategic, tactical, and 
operational insights and decision-making."[6] When Business Intelligence Design and Plan
using this definition, business intelligence also After BI analysis, the next step is to design
includes technologies such as data integration, data and plan the business intelligence data that is
quality, data warehousing, master data management, needed by the organization. The BI design and plan
text and content analytics, and many others that the helps to create a detailed business and technology
market sometimes lumps into the information design that will scale to the business needs, improve
management segment. Therefore, Forrester refers data quality and turn information into actionable
to data preparation and data usage as two separate, intelligence.
but closely linked segments of the business
intelligence architectural stack. Business Intelligence Implementation
Forrester defines the latter, narrower business The designed BI is then implemented using
intelligence market as "referring to just the top the right tools that help to speed up deployment and
layers of the BI architectural stack such as reporting, ensure a successful adoption of business intelligence
analytics and dashboards."[7] Thomas and data warehouse (BIDW) solution.
Davenport has argued that business intelligence
should be divided into querying ,reporting, OLAP, BI Management and Support
an "alerts" tool, and business analytics. In this The BI needs frequent maintenance and
definition, business analytics is the subset of BI support after it is implemented. This maximizes the
based on statistics, prediction, and optimization‘[8]. value of business intelligence and data warehouse
Before implementing a BI solution, it is worth (BIDW) investment with 24x7 production support,
taking different factors into consideration before maintenance and administration services.
proceeding. The Business Intelligence Analyst should be able to
According to Kimball et al., these are the three think strategically about business issues and
critical areas that you need to assess within your understand product & service portfolios as well as
organization before getting ready to do a BI emerging technologies and their benefits to
project:[12] customers.
1. The level of commitment and sponsorship of the In addition to being a professional in the
project from senior management Intelligence, the Business Intelligence Analyst
2. The level of business need for creating a BI partners closely with the Business Analysts and
implementation Product Managers to support the release of new
3. The amount and quality of business data products and proof-of-concept pilots.
available. 4. Strategy of Implementation
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A. Background: This includes identifying the integrated. Although there is no absolute dollar
background of the business which includes the value assessed for the BI effort, it is clear that
needs of the business clients. integrated data
B. Obtaining consistent data and creating (1) minimizes the need for duplicate data entry and
meaningful reports. This has been a major reconciliation of inconsistent information thus
challenge for most of the organizations as the reducing manpower needs,
information needs keeps changing for various (2) drives management decisions to improve
reasons and very frequently, no consensus of operation, and
what is the ‗appropriate‘ data for different (3) improves the intelligence
questions, and only few individuals have been It should be noted as well that BI seeks to
able to access and make use of data minimize the ―cost of distrust.‖ If the integrated
successfully. data are not trusted, then users will seek the ability
Decision makers have routinely depended to ―massage‖ that data based on the perception or
on IT experts to compile data, sometimes waiting the actuality of its inaccuracy, and frequently then to
days or weeks for the needed answers. However, optimize a portion of the overall business.
day-to-day activities demand timely and accurate But this optimization of the part does not optimize
information and increasing demand for more the whole—if data inaccuracies are not corrected at
information to make business decisions which at their source (within the transactional systems and
present has been very difficult to provide on short associated business processes) then the cycle will
notice. continue.
Enormous amounts of integrated data will BI seeks fundamentally to address this
lead to faster and better decision making. The cycle by providing high-quality data and tools and
objectives of the Business Intelligence (BI) associated business processes, so that the BI way is
Implementation are to provide a new approach to clearly recognized as the superior way.
data management, presentation and analytics. Goals for BI are at two levels. Firstly, there is the
Implementing Business Intelligence enables to goal to enable a smooth (i.e. non-interrupted)
access and retrieve financial, research, personnel transition from the old to the new system, so as not
and miscellaneous data from one single source, give to disrupt the current operation.
easy and secure access of relevant data to all levels Secondly, a more challenging goal is to create a data
of management or units, spend minimal time on environment that will answer questions that have
data retrieval, but dedicate significant time to data not been previously possible. Much of the
analysis and decision making, make data, reports, implementation plan focuses on data migration, data
analysis and models available to a broad user base. warehousing and reporting.
Business intelligence primarily focuses on processes As vast amounts of data are collected, new
and people. To this purpose the BI team will need to opportunities to analyze and model data will present
ensure data conversion and data warehousing themselves. The now ready availability of statistical
consistent with the needs of current and future tools to perform data mining will allow the
organizational goals. exploration of the relationships among data in order
Business Intelligence effort encompasses to extract value and ultimately new insights.
data conversion and cleansing, data warehousing A plan to manage the transition and not interrupt
and data consumption. Only ―good‖ data will be the flow of business reporting is key to the success
converted and migrated into the data warehouse; of the BI implementation.
older (non-cleansed) data is stored for historical
purposes only. 6. Audience of Business Intelligence
The data warehouse allows secure storage It is important to understand the audience
and access to data from the new transactional of the business intelligence project or data. This
systems and will allow additional storage of other would help in analyzing the requirements of
applications and individual departmental needs. different business users and building an efficient
Rapid advances in processing speed, innovative data database which is helpful to the actual end users.
warehouse design and pioneering concepts of data Power-users / developers: This can be a
display such as dashboards are opening up new group of technicians that produce reports that they
opportunities for data management. use themselves or that they distribute to others. The
project will serve this audience by providing access
5. Objectives of Business Intelligence to a richer set of transaction data and by providing
The objective of BI is to increase its technological efficiencies that allow them to spend
operational effectiveness. Decision makers need to less time ‗pulling‘ information and more time
have easy access to data that is timely, accurate and ‗analyzing‘ information.

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To mitigate the risk of inappropriate use of
Operational Decision Makers: This is a group of data, the BI team advocates that a balance be struck
managers or leaders in administrative and academic between electronically restricting data access and
organizations. These managers, directors, business training users to handle data responsibly. Besides
officers, departmental managers who are typically the enormous cost, if too many technology barriers
dependent upon dedicated reports today. This leads are built into the system, data users will circumvent
to a disparity in access to and use of information for the new data system, and proliferation of different
day-to-day decision making. The project will serve data repositories will continue, thus defeating the
this audience by providing them a set of dashboard purpose of BI.
reports that they can individually access through an
easy-to-use web self-service reporting portal. 8. Success Critieria of Business Intelligence
Success criteria of a business intelligence
Executive Decision Makers: This group of deans, application include:
AVPs and VPs are reliant upon dedicated report • Positive feedback from users on vastly improved
writers today as well. This audience generally does access to data and information
not have ‗direct‘ access to information, but their • Perfect reproducibility and consistency of data
position means that they generally have when queried though different channels
programming staff who can provide information to • Improved communications and decision making
support their decisions. Unfortunately, this at all levels.
information frequently needs to be ‗cobbled
together‘ from a number of different sources, lacks 9. Methodology of Data Warehouse
context (relationship to the same number last year or This section explains the steps to develop
in another unit), and is generally provided on- and deploy a data warehouse. In addition it will
demand only (i.e., no notifications or early show differences between DW Methodology and
warnings). The project will serve this audience by Traditional IT Methodologies.
providing self-service dashboard reports that blend The Data Warehousing Methodology is
current information with contextual information. BI organized into the different phases. Like all software
will also serve this audience by beginning to produce implementations, Business Intelligence
a dashboard that quickly displays some of the implementations follows all Software Development
indicators. Life Cycle Management guidelines, which start with
There is in reality an overlap between the establishing scope or goals and identifying key
different audiences. Generally, BI intends to provide stakeholders, users and managers to guarantee the
a richer set of information to a significantly broader credibility and commitment with Business
audience and to shift the allocation of time away Intelligence project.
from ‗pulling and maintaining‘ information and Developing data warehouses is definitely different
toward analyzing and using information. than developing other IT systems and so requires a
It is also important to note that self-service reporting different methodology.
provided through the BI project is not just a tool Data Warehousing Methodology:
technology, or project – it is a disruptive change to • Use of data is exploratory and less predictable
decision makers at all levels. • Multidimensional Modeling
• Focus is on loading and presenting data
7. Access and Security of Business Intelligence Traditional IT Methodology:
From an access and security standpoint the • Automated processes are repeated and
vision is to ensure that decision makers at all levels predictable
have access to data needed to perform their jobs. • ERD Data Modeling
Any data directly accessible by the public should be • Focus is on rapid on-line updating of data
highly controlled. General security should be Data warehousing is not simply creating a
provided through encryption for users and for set of reports that are run periodically. It involves
applications that access information. questions that may lead to initially unpredicted
Authentication should be provided to the places. Requirements describe the needed solution in
web application. Access to highly sensitive data such business terms. In the analysis phase
as social security numbers, credit card numbers, detailed requirements for data warehouse are
banking information, driver license numbers and defined.
benefit information should be granted on a ‗need Acknowledgements
only‘ basis and available only to the authorized The satisfaction and euphoria that
users. accompanies the successful completion of any task
would be incomplete without the mention of the
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Vol. 2, Issue 5, September- October 2012, pp.1689-1694
[1] S.Nasira tabassum has received her Masters [2] SK.Althaf ahammed has received his Masters
Degree in computer application from degree in computer application from
M.J.College of Science and Technology, m.k.university madhurai (T.N). He is currently
Affiliated to Osmania University, Hyderabad working as an Associate professor in Shadan
and Pursuing M.Tech in Software Engineering Group of Colleges, Hyderabad.
from Nizam Institute of Engineering and
Technology, Deshmukhi, Nalgonda Dist,
Affiliated to JNTU Hyderabad, AP India.

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