assignment of mb 0036

7/27/2019 Assignment of MB 0036

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MB 0036-Business Intelligence and ToolsEnsure that you answer all questions according to the marks allocated (not more than 400words for a 10-mark question and not more than 200 words for a five-mark question).

The total page limit shall not exceed 8 pages of A-4 size

Q.1 Define the term business intelligence tools? Discuss the roles inBusiness Intelligence project?Answer-Business Intelligence (BI) is a generic term used to describe leveraging theorganizational internal and external data, information for making thebest possible business decisions. The field of Businessintelligence is very diverse andcomprises the tools and technologies used to access and analyze various typesof business information. These tools gather and store the data and allow the user to viewand analyze the information from a wide variety of dimensions and thereby assist thedecision-makers make better business decisions. Thus theBusinessIntelligence (BI) systems and tools play a vital role as far as organizations areconcerned in making improved decisions in thecurrent cut throat competitive scenario. In simple terms, BusinessIntelligence is anenvironment in which business users receive reliable, consistent, meaningful and timelyinformation. This data enables the business users conduct analyses that yield overallunderstanding of how the business has been, how it is now and how it will be in the nearfuture. Also, the BI tools monitor the financial and operational health of the organizationthrough generation of various types of reports, alerts, alarms, key performanceindicators and dashboards.

Although it is possible to build BI systems without the benefit of a data warehouse, mostof the systems are an integral part of the user-facing end of the data warehouse inpractice. In fact, we can never think of building a data warehouse without BI Systems.That is the reason; sometimes, the words data warehousing and business intelligenceare being used interchangeably. A typical BI Project consists of the following roles and

the responsibilities of each of these roles are detailed below:Project Manager: Monitors the progress on continuum basis and is responsible for thesuccess of the project.Technical Architect: Develops and implements the overall technical architecture of the BIsystem, from the backend hardware/software to the client desktop configurations.Database Administrator (DBA): Keeps the database available for the applications to runsmoothly and also involves in planning and executing a backup/recovery plan, as well asperformance tuning.ETL Developer: Involves himself in planning, developing, and deploying the extraction,transformation, and loading routine for the data warehouse from the legacysystems.Front End Developer: Develops the front-end, whether it be client-server or over theweb.

OLAP Developer: Dexlops the OLAP cubes.Data Modeler: Is responsible for taking the data structure that exists in the enterpriseand model it into a scheme that is suitable for OLAP analysis.QA Group: Ensures the correctness of the data in the data warehouse.
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Q.2. What do you mean by data ware house? What are the major concepts and

terminology used in the study of data ware house?Answer-

In computing, a data warehouse (DW) is adatabase used forreporting and analysis. Thedata stored in the warehouse is uploaded from the operational systems. The data maypass through anoperational data storefor additional operations before it is used in theDW for reporting.

A data warehouse maintains its functions in three layers: staging, integration, andaccess. Staging is used to store raw data for use by developers. The integration layer isused to integrate data and to have a level of abstraction from users. The access layer isfor getting data out for users.Data warehouses can be subdivided into data marts. Data marts store subsets of datafrom a warehouse.This definition of the data warehouse focuses on data storage. The main source of thedata is cleaned, transformed, catalogued and made available for use by managers and

other business professionals fordata mining, online analytical HYPERLINK"http://en.wikipedia.org/wiki/OLAP"processing , market research and decision support(Marakas & O'Brien 2009). However, the means to retrieve and analyze data, to extract,transform and load data, and to manage the data dictionary are also consideredessential components of a data warehousing system. Many references to datawarehousing use this broader context. Thus, an expanded definition for datawarehousing includes business intelligence tools, tools to extract, transform andload data into the repository, and tools to manage and retrieve metadata.

A common way of introducing data warehousing is to refer to the characteristics of a
http://en.wikipedia.org/wiki/Computinghttp://en.wikipedia.org/wiki/Databasehttp://en.wikipedia.org/wiki/Databasehttp://en.wikipedia.org/wiki/Reportinghttp://en.wikipedia.org/wiki/Reportinghttp://en.wikipedia.org/wiki/Uploading_and_downloadinghttp://en.wikipedia.org/wiki/Operational_data_storehttp://en.wikipedia.org/wiki/Operational_data_storehttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/OLAPhttp://en.wikipedia.org/wiki/OLAPhttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Data_dictionaryhttp://en.wikipedia.org/wiki/Business_intelligence_toolshttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Metadatahttp://en.wikipedia.org/wiki/Computinghttp://en.wikipedia.org/wiki/Databasehttp://en.wikipedia.org/wiki/Reportinghttp://en.wikipedia.org/wiki/Uploading_and_downloadinghttp://en.wikipedia.org/wiki/Operational_data_storehttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/OLAPhttp://en.wikipedia.org/wiki/OLAPhttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Data_dictionaryhttp://en.wikipedia.org/wiki/Business_intelligence_toolshttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Extract,_transform,_loadhttp://en.wikipedia.org/wiki/Metadata


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data warehouse as set forth by William Inmon:Subject OrientedIntegratedNonvolatile

Dimension:

A variable, perspective or general category of information that is used to organize andanalyze information in a multi-dimensional data cube.Drill Down:The ability of a data-mining tool to move down into increasing levels of detail in a datamart, data warehouse or multi-dimensional data cube.Drill Up:The ability of a data-mining tool to move back up into higher levels of data in a datamart, data warehouse or multi-dimensional data cube.Executive Information Management System (EIS):

A type of decision support system designed for executive management that reportssummary level information as opposed to greater detail derived in a decision supportsystem.

Extraction, Transformation and Loading (ETL) Tool:

Structured Query Language (SQL):A standard programming language used by contemporary relational databasemanagement systems.Synchronization:The process by which the data in two or more separate database are synchronized sothat the records contain the same information. If the fields and records are updated inone database the same fields and records are updated in the other.

Q.3. what are the data modeling techniques used in data warehousing

environment?Answer-Two data modeling techniques that are relevant in a data warehousingenvironment are ER modeling and dimensional modeling.ER modeling produces a data model of the specific area of interest, using two basicconcepts: entities and the relationships between those entities. DetailedER Modeling

A prerequisite for reading this book is a basic knowledge of ER modeling.Therefore we do not focus on that traditional technique.


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Figure 12. A Sample ER Model. Entity, relationship, and attributes in an ER diagram.

Basic ConceptsAn ER model is represented by an ER diagram, which uses three basic graphic symbolsto conceptualize the data: entity, relationship, and attribute.

An entity is defined to be a person, place, thing, or event of interest to the business orthe organization. An entity represents a class of objects, which are things in the realworld that can be observed and classified by their properties and characteristics. Insome books on IE, the term entity type is used to represent classes of objects and entityfor an instance of an entity type. In this book, we will use them interchangeably.6.3.1.2 Relationship

A relationship is represented with lines drawn between entities. It depicts the structuralinteraction and association among the entities in a model.6.3.1.3 Attributes

Attributes describe the characteristics of properties of the entities. In Figure 12,Product ID, Description, and Picture are attributes of the PRODUCT entity. Forclarification, attribute naming conventions are very important. An attribute name shouldbe unique in an entity and should be self-explanatory. For example, simply saying date1or date2 is not allowed, we must clearly define each. As examples, they could be definedas the order date and delivery date.Dimensional ModelingIn some respects, dimensional modeling is simpler, more expressive, and easier tounderstand than ER modeling. But, dimensional modeling is a relatively new conceptand ot firmly defined yet in details, especially when compared to ER modelingtechniques.This section presents the terminology that we use in this book as we discussdimensional modeling.Basic Concepts


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.Dimensional modeling has several basic concepts: Facts Dimensions Measures (variables)

Q.4 Discuss the categories in which data is divided before structuring it into data

ware house?Answer-A Data Warehouse is not an individual repository product. Rather, it is an overallstrategy, or process, for building decision support systems and a knowledge-basedapplications architecture and environment that supports both everyday tactical decisionmaking and long-term business strategizing.Data Warehouses and Data Warehouse applications are designed primarily to supportexecutives, senior managers, and business analysts in making complex businessdecisions. Data Warehouse applications provide the business community with access toaccurate, consolidated information from various internal and external sources.

The primary objective of Data Warehousing is to bring together information fromdisparate sources and put the information into a format that is conducive to makingbusiness decisions. This objective necessitates a set of activities that are far morecomplex than just collecting data and reporting against it. Data Warehousing requiresboth business and technical expertise and involves the following activities:

Accurately identifying the business information that must be contained in the WarehouseIdentifying and prioritizing subject areas to be included in the Data WarehouseManaging the scope of each subject area which will be implemented into the Warehouseon an iterative basisDeveloping a scaleable architecture to serve as the Warehouses technical andapplication foundation, and identifying and selecting the hardware/software/middlewarecomponents to implement itExtracting, cleansing, aggregating, transforming and validating the data to ensureaccuracy and consistency

Defining the correct level of summarization to support business decision makingEstablishing a refresh program that is consistent with business needs, timing and cyclesProviding user-friendly, powerful tools at the desktop to access the data in theWarehouseEducating the business community about the realm of possibilities that are available tothem through Data WarehousingEstablishing a Data Warehouse Help Desk and training users to effectively utilize thedesktop toolsEstablishing processes for maintaining, enhancing, and ensuring the ongoing successand applicability of the WarehouseIn the Data Warehouse model, operational databases are not accessed directly toperform information processing. Rather, they act as the source of data for the Data

Warehouse, which is the information repository and point of access for informationprocessing. There are sound reasons for separating operational and informationaldatabases, as described below.The users of informational and operational data are different. Users of informational dataare generally managers and analysts; users of operational data tend to be clerical,operational and administrative staff.Operational data differs from informational data in context and currency. Informationaldata contains an historical perspective that is not generally used by operational systems.


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The technology used for operational processing frequently differs from the technologyrequired to support informational needs.The processing characteristics for the operational environment and the informationalenvironment are fundamentally different.

Q.5 Discuss the purpose of executive information system in an organization?

Answer-Implementing an Executive Information System (EIS)

An EIS is a tool that provides direct on-line access to relevant information about aspects

of a business that are of particular interest to the senior manager.

Introduction

Many senior managers find that direct on-line access to organizational data is

helpful. For example, Paul Frech, president of Lockheed-Georgia, monitored employee

contributions to company-sponsored programs (United Way, blood drives) as a surrogate

measure of employee morale (Houdeshel and Watson, 1987). C. Robert Kidder, CEO of

Duracell, found that productivity problems were due to salespeople in Germany wasting

time calling on small stores and took corrective action (Main, 1989).

Executive Information Systems differ from traditional information systems in the

following ways:

They are specifically tailored to executive's information needs.

They are able to access data about specific issues and problems as well as

aggregate reports

They provide extensive on-line analysis tools including trend analysis, exception

reporting & "drill-down" capability

Purpose of EIS

The primary purpose of an Executive Information System is to support managerial

learning about an organization, its work processes, and its interaction with the external

environment. Informed managers can ask better questions and make better decisions.

Vandenbosch and Huff (1992) from the University of Western Ontario found that

Canadian firms using an EIS achieved better business results if their EIS promoted

managerial learning. Firms with an EIS designed to maintain managers' "mental models"


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were less effective than firms with an EIS designed to build or enhance managers'

knowledge.

This distinction is supported by Peter Senge in The Fifth Dimension. He

illustrates the benefits of learning about the behaviour of systems versus simply learning

more about their states

Contents of EIS

A general answer to the question of what data is appropriate for inclusion in an

Executive Information System is "whatever is interesting to executives." While this

advice is rather simplistic, it does reflect the variety of systems currently in use.

Executive Information Systems in government have been constructed to track data about

Ministerial correspondence, case management, worker productivity, finances, and human

resources to name only a few.

While the above indicates that selection of data for inclusion in an EIS is difficult,

there are several guidelines that help to make that assessment.

EIS measures must be easy to understand and collect. Wherever possible, data

should be collected naturally as part of the process of work. An EIS should not add

substantially to the workload of managers or staff.

Q.6 Discuss the challenges involved in data integration and coordination

process?

Answer-Data Integration PrimerChallenges to Data IntegrationOne of the most fundamental challenges in the process of data integration is settingrealistic expectations. The term data integration conjures a perfect coordination ofdiversified databases, software, equipment, and personnel into a smoothly functioningalliance, free of the persistent headaches that mark less comprehensive systems of

information management. Think again.The requirements analysis stage offers one of the best opportunities in the process torecognize and digest the full scope of complexity of the data integration task. Thoroughattention to this analysis is possibly the most important ingredient in creating a systemthat will live to see adoption and maximum use.

As the field of data integration progresses, however, other common impediments andcompensatory solutions will be easily identified. Current integration practices havealready highlighted a few familiar challenges as well as strategies to address them, asoutlined below.


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Heterogeneous DataChallengesFor most transportation agencies, data integration involves synchronizing hugequantities of variable, heterogeneous data resulting from internal legacy systems thatvary in data format. Legacy systems may have been created around flat file, network, or

hierarchical databases, unlike newer generations of databases which use relational data.Data in different formats from external sources continue to be added to the legacydatabases to improve the value of the information. Each generation, product, and home-grown system has unique demands to fulfill in order to store or extract data. So dataintegration can involve various strategies for coping with heterogeneity. In some cases,the effort becomes a major exercise in data homogenization, which may not enhance thequality of the data offered.

StrategiesA detailed analysis of the characteristics and uses of data is necessary to mitigate issueswith heterogeneous data. First, a model is chosen-either a federated or data warehouseenvironment- that serves the requirements of the business applications and other uses

of the data. Then the database developer will need to ensure that various applicationscan use this format or, alternatively, that standard operating procedures are adopted toconvert the data to another format.Bringing disparate data together in a database system or migrating and fusing highlyincompatible databases is painstaking work that can sometimes feel like anoverwhelming challenge. Thankfully, software technology has advanced to minimizeobstacles through a series of data access routines that allow structured query languagesto access nearly all DBM and data file systems-relational or non-relational.

assignment of mb 0036

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