ASSIGNMENTSSubject code: MB0036(4 credits)Set 1Marks 60SUBJECT NAME: BUSINESSINTELLIGENCE & TOOLSNote: Each Question carri...
and dice theinformation from their organization‟s numerous databases without having to waitfor their IT departments to dev...
The data contained within the boundaries of the warehouse are integrated. This means thatallinconsistencies regarding nami...
MB0036 – Business Intelligence & Toolsrelationships. The ER model is an abstraction tool as it can be used to simplify,und...
An ER model is represented by an ER diagram, which uses three basic graphicsymbols toconceptualize the data: entity, relat...
business rule, youc a n n o t t e l l w h i c h c o m p o n e n t s m a k e u p a p r o d u c t m o d e l . T od o t h i s...
inherited attributes and the sub entities have their own a t t r i b u t e s ( s u c h a snumber of cash registers and flo...
Use of a data warehouse brings in the following advantages for an organization:•End-users can access a wide variety of dat...
results from data mining include clustering, classifying, andestimating the thingsthat occur together. There are many kind...
•On-Line Transaction Processing (OLTP):This is the way the data is processed by an end user/a computer system. Here, theda...
An Executive Information System (EIS) is a set of management tools supporting theinformation and decision-making needs of ...
systems anddata sources can be a daunting task, and seems to put many businesses offimplementing it.The system vendors hav...
identifiers and in the second phase, the duplicates arereconciled periodically ether throughautomatic algorithms or manual...
changes into thetool and the metadata for the transformations get adjustedautomatically. But relying on thetransformation ...
SET 2Q.1 Explain business development life cycle in detail? [10 Marks]May 092012Ans.The Business development Lifecycle is ...
defined at this stage provide the necessary guidance to make the decisions. This process mainlyincludes the following acti...
6. Selecting a product, installing on trial, and negotiating the value/price.4 Dimensional ModelingA dimensional model pac...
continuous utilization of the system. This step may also include making some minorenhancements to the BI system.Revising t...
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Bi assignment

  1. 1. ASSIGNMENTSSubject code: MB0036(4 credits)Set 1Marks 60SUBJECT NAME: BUSINESSINTELLIGENCE & TOOLSNote: Each Question carries 10 marksQ1. Define the term businessintelligence tools? Briefly explain how the data from oneend gets transformed into information at theother end?Ans:Business intelligence tools. The various tools of this suite are:•Data Integration Tools:These tools extract, transform and load the data from the source databases to the targetdatabase. There are two categories; Data Integrator andRapid Marts. Data Integrator is anETL tool with a GUI. Rapid Marts is a packagedETL with pre -built data models forreporting and query analysis that makes initial prototype development easy and fast forERP applications.The important components of Data Integrator include;Graphicaldesigner:This is a GUI used to build and test ETL jobs for data cleansing,validation and auditing.Data integration server:This integrates data from different source databases.Metadata repository:T h i s r e p o s i t o r y k e e p s s o u r c e a n d t a r g e t m e t a d a t a a n d t h e transformation rules.Administrator:This is a web-based tool that can be used to start, stop, schedule andmonitor ETL jobs.•BI Platform:This platform provides a set of common services to deploy, use andmanage the tools andapplications. These services include providing the security, broadcasting, collaboration, metadataand developer services.•Reporting Tools and Query & Analysis Tools: These tools provide the facility for standard reports generation, ad hoc queries and dataanalysis.•Performance Management Tools:These tools help in managing the performance of a business by analyzing and tracking keymetrics and goals.•B u s i n e s s i n t e l l i g e n c e t o o l s a r e a t yp e o f a p p l i c a t i o n s o f t w a r e d e s i g n e d t oh e l p i n making better business decisions. These tools aid in the analysis andpresentation of data in a more meaningful way and so play a key role in the strategic planningprocess o f a n o r g a n i z a t i o n . T h e y i l l u s t r a t e b u s i n e s s i n t e l l i g e n c e i n t h e a r e a so f m a r k e t research and segmentation, customer profiling, customer support,profitability, andinventory and distribution analysis to name a few.•Various types of BI systems viz. Decision Support Systems, ExecutiveInformationSystems (EIS), Multidimensi onal Analysis software or OLAP (On-LineAnalyticalProcessing) tools, data mining tools are discussed further. Whatever isthe type, theBusiness Intelligence capabilities of the system is to let its users slice
  2. 2. and dice theinformation from their organization‟s numerous databases without having to waitfor their IT departments to develop complex queries and elicit answers.•Although it is possible to build BI systems without the benefit of a d a t aw a r e h o u s e , m o s t o f t h e s ys t e m s a r e a n i n t e g r a l p a r t o f t h e user-facing end ofthe data warehouse in practice. In fact, we can never think of building a data warehousewithout BI Systems. That isthe reason; sometimes, the words „data warehousing‟ and„businessintelligence‟ are being used interchangeably.•Figure 1.1depicts how the data from one end gets transformed to information at the other end forbusiness information.•Q2.What do you mean by data ware house? What are the major concepts andterminology used in the study ofdata warehouse?Ans:In simple terms, a data warehouse is the repository of an organization‟s historical data( a l s otermed as the corporate memory). For example, an organizationw o u l d g e t t h e information that is stored in its data warehouse to find out what day of theweek they sold themost number of gadgets in May 2002, or how employees were onsick leave for a specificweek.A data warehouse is a database designed to support decisionmaking in an organization. Here,the data from various production databases are copiedto the data warehouse so that queriescan be forwarded without disturbing the stability orperformance of the production systems.S o t h e m a i n f a c t o r t h a t l e a d s t o t h e u s e o f ad a t a w a r e h o u s e i s t h a t c o m p l e x q u e r i e s a n d analysis can be obtained over theinformation without slowing down the operational systems.While operational systems areoptimized for simplicity and speed of modification (online transaction processing, orOLTP), the data warehouse is optimized for reporting and analysis(online analytical processing,or OLAP). (The concepts of OLTP and OLAP are discussed inlater Units).A p a r t f r o m t r a d i t i o n a l q u e r y a n d r e p o r t i n g , a d a t a w a r e h o u s e p r o v i d e st h e b a s e f o r t h e p o w e r f u l d a t a a n a l ys i s t e c h n i q u e s s u c h a s d a t a m i n i n ga n d m u l t i d i m e n s i o n a l a n a l ys i s (discussed in detail in later Units). Making use of thesetechniques will result in easier accessto the information you need for informed decision making.haracteristics of a Data WarehouseAccording to Bill Inmon, who is considered to be the Father of Data warehousing, the data inaData Warehouse consists of the following characteristics:Subject orientedThe first feature of DW is its orientation toward the major subjects of the organization insteadofapplications. The subjects are categorized in such a way that the subject-wise collectionof information helps in decision-making. For example, the data in the datawarehouse of aninsurance company can be organized as customer ID, customername, premium, payment period, etc. rather auto insurance, life insurance, fire insurance, etc.Integrated
  3. 3. The data contained within the boundaries of the warehouse are integrated. This means thatallinconsistencies regarding naming convention and value representations need to be removedina data warehouse. For example, one of the applications of an organization mightcodegender as „m‟ and „f‟ and the other application might code the samefunctionality as „0′ and„ 1 ′ . W h e n t h e d a t a i s m o v e d f r o m t h e o p e r a t i o n a le n v i r o n m e n t t o t h e d a t a w a r e h o u s e environment, this will result in conflict.Time variantThe data stored in a data warehouse is not the current data. The data is a time seriesdata asthe data warehouse is a place where the data is accumulated periodically.This is in contrastto the data in an operational system where the data in thedatabases are accurate as of themoment of access.Non-volatility of the dataThe data in the data warehouse is non-volatile which means the data is stored in a read-onlyf o r m a t a n d i t d o e s n o t c h a n g e o v e r a p e r i o d o f t i m e . T h i s i s t h e r e a s o nt h e d a t a i n a d a t a warehouse forms as a single source for all decision system supportprocessing.Keeping the above characteristics in view, „data warehouse„can be defined asa subject-oriented, integrated, non-volatile, time-variant collection of data designedto support thedecision-making requirements of an organization.Q 3. What are the data modeling techniques used in data warehousing environment?Ans:There are two data modeling techniques that are relevant in a datawarehousingenvironment. They are Entity Relationship modeling (ERm o d e l i n g ) a n d d i m e n s i o n a l modeling.•ER modeling produces a data model of the specific area of interest, using two basicconcepts:Entities and the Relationshipsbetween them. A detailed ER model mayalso contain attributes, which can be properties of eitherthe entities or therelationships. The ER model is an abstraction tool as it can be used to simplify,understand andanalyze the ambiguous data relationships in the real business world.•Dimensional modeling uses three basic concepts:Facts, Dimensions and Measures.Dimensionalmodeling is powerful in representing the requirements of the businessuser in the context ofdatabase tables and also in the area of data warehousing.Both ER and dimensional modelingcan be used to create an abstract model of a specific subject. However, each of them hasits own limited set of modeling concepts and associatedn o t a t i o n c o n v e n t i o n s .C o n s e q u e n t l y, t h e t e c h n i q u e s s e e m d i f f e r e n t , a n d t h e y a r e i n d e e d differentin terms of semantic representation. There is much debate as to which methodis better and the conditions under which a specific technique is to be selected. There can benodefinite answer, understanding of the circumstances and the businessrequirements finallylead to selection of an appropriate technique.Entity- Relationship (E-R) ModelingBasic Concepts
  4. 4. MB0036 – Business Intelligence & Toolsrelationships. The ER model is an abstraction tool as it can be used to simplify,understand andanalyze the ambiguous data relationships in the real business world.•Dimensional modeling uses three basic concepts:Facts, Dimensions and Measures.Dimensionalmodeling is powerful in representing the requirements of the businessuser in the context ofdatabase tables and also in the area of data warehousing.Both ER and dimensional modelingcan be used to create an abstract model of a specific subject. However, each of them hasits own limited set of modeling concepts and associatedn o t a t i o n c o n v e n t i o n s .C o n s e q u e n t l y, t h e t e c h n i q u e s s e e m d i f f e r e n t , a n d t h e y a r e i n d e e d differentin terms of semantic representation. There is much debate as to which methodis better and the conditions under which a specific technique is to be selected. There can benodefinite answer, understanding of the cir cumstances and the businessrequirements finallylead to selection of an appropriate technique.Entity- Relationship (E-R) ModelingBasic ConceptsAn ER model is represented by an ER diagram, which uses three basic graphicsymbols toconceptualize the data: entity, relationship, and attribute.EntityAn entity is defined to be a person, place, thing, or event of interest to the businessor theorganization. It represents a class of objects, which are things in the real business worldthatcan be observed and classified by their properties and characteristics. In general, an entityhasits own business definition and a clear boundary definition that is required to describe whatisincluded and what is not.In a practical modeling project, the team members share a definitiontemplate for integrationand a consistent entity definition in the model. In case of a high-levelbusiness modeling, anentity can be very generic, but it must be quite specific in thedetailed logical modeling.There are four entities; PRODUCT, PRODU CT MODEL,PRODUCT COMPONENT, andCOMPONENT in the ER diagram (Refer Figure 4.1) and arerepresented as rectangles.Fig. 4.1: A Simple ER Model5
  5. 5. An ER model is represented by an ER diagram, which uses three basic graphicsymbols toconceptualize the data: entity, relationship, and attribute.EntityAn entity is defined to be a person, place, thing, or event of interest to the businessor theorganization. It represents a class of objects, which are things in the real business worldthatcan be observed and classified by their properties and characteristics. In general, an entityhasits own business definition and a clear boundary definition that is required to describe whatisincluded and what is not.In a practical modeling project, the team members share a definitiontemplate for integrationand a consistent entity definition in the model. In case of a high-levelbusiness modeling, anentity can be very generic, but it must be quite specific in thedetailed logical modeling.There are four entities; PRODUCT, PR ODUCT MODEL,PRODUCT COMPONENT, andCOMPONENT in the ER diagram (Refer Figure 4.1) and arerepresented as rectangles.Fig. 4.1: A Simple ER Modelhe four diagonal lines on the corners of the PRODUCT COMPONENT entity represent thattheentity is „an associative entity‟ and the entity is to resolve the many-to-manyrelationship between two entities. PRODUCT MODEL and COMPONENT are independent ofeach other but have a business relationship between them. A PRODUCT MODELconsists of manycomponents and a component is related to many product models. With this
  6. 6. business rule, youc a n n o t t e l l w h i c h c o m p o n e n t s m a k e u p a p r o d u c t m o d e l . T od o t h i s , yo u c a n d e f i n e a r e s o l v i n g e n t i t y . F o r e x a m p l e , t h eP R O D U C T C O M P O N E N T e n t i t y c a n p r o v i d e t h e information about whichcomponents are related to which product model.I n E R m o d e l i n g , n a m i n g t h eentities is important for easy understanding andc l e a r communication. It is expressed grammatically in the form of a noun ratherthan a verb andt h e c r i t e r i a f o r s e l e c t i n g a n e n t i t y n a m e d e p e n d o nh o w w e l l t h e n a m e r e p r e s e n t s t h e characteristics and scope of the entity.Also, defining a unique identifier of an entity is the most critical task. These uniqueidentifiers are called candidate keys. Among them, you canselect the key that is most commonlyused to identify the entity, called „primary key‟.RelationshipRelationships represent the structural interaction and association among the entitiesin amodel and they are represented with lines drawn between the two specific entities.Generally,a relationship is named grammatically by a verb (such as owns, belongs,and has) and ther e l a t i o n s h i p b e t w e e n t h e e n t i t i e s c a n b e d e f i n e d i n t e r m s o ft h e c a r d i n a l i t y. C a r d i n a l i t y represents the maximum number of instances of one entitythat are related to a single instancein another table and vice versa. Thus the possible cardinalitiesinclude one-to-one (1:1), one-to-many (1:M), and many-to-many (M:M). In a detailednormalized ER model, any M:Mrelationship is not shown because it is resolved to anassociative entity.AttributesAttributes describe the characteristics of properties of the entities. The ProductID,Description, and Picture are attributes of the PRODUCT entity in Figure 4.1. T h ename of an attribute has to be unique in an entity and should bes e l f - explanatory to ensure clarity. For example, rather naming date1 and date2, youm a y u s et h e n a m e s ; o r d e r d a t e a n d d e l i v e r y d a t e . W h e n a n i n s t a n c e h a s n o value foran attribute, the minimum cardinality of the attribute is zero, which means eithernullable or optional.In Figure 4.1, you can see the characters P, m, o, and F that standfor primarykey, mandatory, optional, and foreign key. The Picture attribute of thePRODUCTentity is optional, which means it is nullable. A foreign key of an entity is definedtobe the primary key of another entity. In figure 4.1, the Product ID attribute of t h ePRODUCT MODEL entity is a foreign key as it is the primary keyo f t h e PRODUCT entity. These foreign keys are useful in determining therelationshipssuch as the referential integrity between the entities.Other ConceptsSupertype and SubtypeAn entity can have subtypes and supertypes and the relationship between a supertype entityandits subtype entity is an IS A relationship. An IS A relationship is used where one entity isa generalization of several more specialized entities. The supertype and subtype relationshipisrepresented by a triangle on the relationship. Figure 4.2 shows an example of supertypeands u b t y p e e n t i t i e s w h e r e i n S A L E S O U T L E T i s t h e s u p e r t y p e o fR E T A I L S T O R E a n d CORPORATE SALES OFFICE and RETAIL STORE,CORPORATE SALES OFFICE aresubtypes of SALES OUTLET. Here, each subtype entityinherits attributes from its supertypeentity.Also, each subtype entity can have its owndistinctive attributes. In the example providedabove, Region ID and Outlet ID are
  7. 7. inherited attributes and the sub entities have their own a t t r i b u t e s ( s u c h a snumber of cash registers and floor space of the RETAILS T O R E subentity). The practical benefit of supertyping and subtyping is that they make a datamodelmore directly expressive. Just by looking at the ER diagram, you can see that sales outletsarecomposed of „retail stores‟ and „corporate sales offices‟.Fig. 4.2: Supertype and Subtype Other important concepts in the area of ER modeling are „domain‟ and „normalization‟.•A domain consists of all the possible acceptable values and categories that areallowed for anattribute. It is the set of all real possible occurrences. The format or data type, such as integer,date, and character, provides a clear definition of domain.The practical benefit of domain is thatit is imperative for building the data dictionaryor repository, and for implementing the databaseconsequently.• Normalization is a process of assigning the attributes to entities which in a wayreduces dataredundancy, avoids data anomalies, provides a solid architecture for updating data, andreinforces the long-term integrity of the data model (the thirdnormal form is usually adequate).Dimensional ModelingDimensional modeling is a relatively new concept compared to ER modeling. This method issimpler,more expressive, and easier to understand. This technique is mainly aimed atconceptualizing andvisualizing data models as a set of measures that are described bycommon aspects of the business. Itis useful for summarizing and rearranging the dataand presenting views of the data to support data analysis. Also, the technique focuses onnumeric data, such as values, counts, and weightsQ 4. Discuss the categories in which data is divided before structuring it into datawarehouse?Ans:The Data Warehouses can be divided into two types:•Enterprise Data Warehouse•Data MartEnterprise Data WarehouseThe Enterprise data warehouse consists of the data drawn from multiple operational systemso fan organization. This data warehouse supports time -series and trenda n a l ys i s a c r o s s different business areas of an organization and so can be used for strategicdecision-making.Also, this data warehouse is used to populate various data marts.Data MartAs data warehouses contain larger amounts of data, organizations often create „datamarts‟that are precise, specific to a department or product line. Thus data mart is aphysical andlogical subset of an Enterprise data warehouse and is also termed as adepartment-specificdata warehouse. Generally, data marts are organized around a singlebusiness process.There are two types of data marts; independent and dependant. The data is feddirectly fromthe legacy systems in case of an independent data mart and the data is fed from theenterprisedata warehouse in case of a dependent data mart. In the long run, thedependent data martsare much more stable architecturally than the independent data marts.Advantages and Limitations of a DW System
  8. 8. Use of a data warehouse brings in the following advantages for an organization:•End-users can access a wide variety of data.•Management can obtain various kinds of trends and patterns of data.•A warehouse provides competitive advantage to the company by providing the data andtimely information.•A warehouse acts as a significant enabler of commercial business applications viz.,Customer Relationship Management (CRM) applications.However, following are the concerns that one has to keep in mind whileu s i n g a d a t a warehouse:•The scope of a Data warehousing project is to be managed carefully to attain the definedcontent and value.•The process of extracting, cleaning and loading the data and finally storing it into adatawarehouse is a time-consuming process.•The problems of compatibility with the existing systems need to be resolved before buildingadata warehouse.•Security of the data may become a serious issue, especially if the warehouse is webaccessible.•Building and maintenance of the data warehouse can be handled only through skilledresources and requires hugeinvestment.MB0036 – Business Intelligence & ToolsData Warehouse Concepts and TerminologyVarious concepts and the key terms used in the study of data warehouse are provided below.•Dashboard:This is a reporting tool that consolidates aggregates and arranges measurements,metrics(measurements compared to a goal) on a single screen so that information can bemonitoredat a glance.•Data Management:This is the process of controlling, protecting, and facilitating access to data in orderto provide the end users with timely access to the data they need.•Data Mining (or Data Surfing):T h i s i s a t e c h n i q u e g e a r e d f o r t h e t yp i c a l u s e r w h o d o e s n o t k n o w e x a c t l yw h a t h e i s searching for, but is looking for particular patterns or trends. Datamining is the process of sifting through large amounts of data to produce datacontent relationships. It can predictf u t u r e t r e n d s a n d b e h a v i o r s , a l l o w i n gb u s i n e s s e s t o m a k e p r o a c t i v e , k n o w l e d g e - d r i v e n decisions. The most valuable
  9. 9. results from data mining include clustering, classifying, andestimating the thingsthat occur together. There are many kinds of t ools that play a role ind a t a m i n i n gand they include neural networks, decision trees, visualization,g e n e r a l algorithms, fuzzy logic, etc.•Data Modeling:A method used to define and analyze data requirements needed to support thebusinessfunctions of an organization.•Data Profiling:Data Profiling is a critical step in data migration that automates theidentification of problematic data and metadata, and enableso r g a n i z a t i o n s t o c o r r e c t i n c o n s i s t e n c i e s , redundancies and inaccuracies in theirdatabases.•Data Visualization:Data visualization involves examining the data represented by dynamic images rather than purenumbers. These are the techniques that turn the data into information by using the highcapacityof the human brain to visually recognize patterns and trends•Decentralized Warehouse:A remote data source that users can query/access via a central gateway that providesalogical view of corporate data in terms that users can understand. The gatewayparses anddistributes queries in real time to remote data sources and returns result sets back tousers.•Drill-down:This is the capacity to browse the information through a hierarchical structure asshown below.•External Data Source:This is the data that is not available in the OLTP system s, but is required toenhance theinformation quality in the data warehouse. The examples of this datainclude the data of thec o m p e t i t o r s , i n f o r m a t i o n o f t h e r e g u l a t o r y a n dg o v e r n m e n t b o d i e s , r e s e a r c h d a t a o f t h e professional bodies and universities.•Metadata:Metadata is data about data. The examples of metadata include data element descriptions,datatype descriptions, attribute descriptions, and process descriptions.On-Line Analytical Processing (OLAP):This is a category of software technology that en ables the users gaini n s i g h t i n t o d a t a through fast, consistent, interactive access to a wide variety of possibleviews of informationthat has been transformed from raw data to reflect the real dimensionality ofthe organization.This is implemented in a multi-user client/server mode and offers consistentlyrapid responset o q u e r i e s , r e g a r d l e s s o f d a t a b a s e s i z e a n d c o m p l e x i t y .T h i s s o f t w a r e i s a l s o c a l l e d Multidimensional Analysis Software.
  10. 10. •On-Line Transaction Processing (OLTP):This is the way the data is processed by an end user/a computer system. Here, thedata isdetail oriented, highly repetitive with larger amounts of updates and changes. The majortask of these systems is to perform on-line transaction and query processing. These systemscover m o s t o f t h e d a y - t o - d a y o p e r a t i o n s o f t h e o r g a n i z a t i o n , s u c h a sp u r c h a s i n g , i n v e n t o r y, manufacturing, payroll, banking, accounting and registrationMB0036 – Business Intelligence & Tools•Operational Databases:These are detail oriented databases defined to meet the needs of complex processesof ano r g a n i z a t i o n . H e r e , t h e d a t a i s h i g h l y n o r m a l i z e d t o a v o i d d a t ar e d u n d a n c y a n d d o u b l e - maintenance. A large number of transactions take place everyhour on these databases and area l w a ys “ u p t o d a t e ” a n d r e p r e s e n t a s n a p s h o t o ft h e c u r r e n t s i t u a t i o n . C o n t r a s t t o t h e s e databases, there are Informational databasesthat are stable over a period of time to representa situation at a specific point in time in the past.Architecture of a Data WarehouseThe architecture describes the overall system of a Data Warehouse from variousperspectivess u c h a s d a t a , p r o c e s s , a n d i n f r a s t r u c t u r e t o s t u d y t h e i n t e r -r e l a t i o n s h i p s a m o n g v a r i o u s components.•The data perspective includes the source and target data structures and so it aids the userinunderstanding what data assets are available in a data warehouse and how they are related.•The process perspective is primarily concerned with communicating the process and flow of datafrom the originating source system through the process of loading the data warehouseandextracting data from the warehouse.•The infrastructure or technology perspective details the various hardware and softwareproductsused to implement the distinct components of the overall system.Depending upon the specifics of an organizational situation, the following types ofDataWarehouse architectures are provided below:•Basic architecture of a Data warehouse•Architecture of a Data warehouse with Staging area•Architecture of a Data warehouse with Staging area and Data martsFig 2.1 shows a simple architecture of a data warehouse wherein the end usersdirectlyaccess the data derived from several source systems through the data warehouse.Q 5. Discuss the purpose of executive information system in an organization?Ans:
  11. 11. An Executive Information System (EIS) is a set of management tools supporting theinformation and decision-making needs of management by combining information availablewithin the organisation with externalinformation in an analytical framework. EIS are targeted at management needs to quickly assess the status of a business or sectionof business. These packages are aimed firmly at the type of business user who needs instantandup to date understanding of critical business information to aid decision making.The ideabehind an EIS is that information can be collated and displayed to the user withoutmanipulationor further processing. The user can then quickly see the status of his chosendepartment orfunction, enabling them to concentrate on decision making. Generally an EISis configured todisplay data such as order backlogs, open sales, purchase order backlogs, shipments, receiptsand pending orders. This information can then be used to make executivedecisions at a strategiclevel.The emphasis of the system as a whole is the easy to use interface and the integration withavariety of data sources. It offers strong reporting and data mining capabilities which can provideall the data the executive is likely to need. Traditionally the interface was menudriven with eitherreports, or text presentation. Newer systems, and especially the newer Business Intelligencesystems,which are replacing EIS, have adashboard or scorecard type display.Before thesesystems became available, decision makers had to rely on disparate spreadsheetsand reportswhich slowed down the decision making process. Now massive amounts of relevant informationcan be accessed in seconds. The two main aspects of an EIS system areintegration andvisualisation. The newest method of visualisation is theDashboard and Scorecard.TheDashboardis one screen that presents key data and organisational informationon an almostreal time and integrated basis. The Scorecard is another one screen display withmeasurementmetrics which can give a percentile view of whatever criteria the executivechooses.Behind thesetwo front end screens can be an immensedata processing infrastructure, or acouple ofintegrateddatabases, depending entirely on the organisation that is using thesystem. Thebackbone of the system is traditional server hardware and a fast network. TheEIS software itselfis run from here and presented to the executive over this network. Thedatabases needs to be fullyintegrated into the system and have real-time connections both inand out. This information thenneeds to be collated, verified, processed and presented to theend user, so a real-time connectioninto the EIS core is necessary.Executive Information Systems come in two distinct types: onesthat are data driven, andones that are model driven. Data driven systems interface with databasesand datawarehouses. They collate information from different sources and presents them to theuser inan integrated dashboard style screen. Model driven systems use forecasting, simulationsanddecision tree like processes to present the data.As with any emerging and progressive market,service providers are continually improvingtheir products and offering new ways of doingbusiness. Modern EIS systems can also present industry trend information and competitorbehaviour trends if needed. They can filter and analyse data; create graphs, charts and scenariogenerations; and offer many other optionsfor presenting data.There are a number of ways to link decision making to organisational performance. Fromadecision makers perspective these tools provide an excellent way of viewing data.Outcomesdisplayed include single metrics, trend analyses, demographics, market shares and amyriadof other options. The simple interface makes it quick and easy to navigate and calltheinformation required.For a system that seems to offer business so much, it is used byrelatively few organisations.Current estimates indicate that as few as 10% of businesses use EISsystems. One of thereasons for this is the complexity of the system and support infrastructure. Itis difficult tocreate such a system and populate it effectively. Combining all the necessary
  12. 12. systems anddata sources can be a daunting task, and seems to put many businesses offimplementing it.The system vendors have addressed this issue by offering turnkey solutions forpotentialclients. Companies like Actuate and Oracle are both offering complete out of theboxExecutive Information Systems, and these arent the only ones. Expense is also an issue.Oncethe initial cost is calculated, there is the additional cost of support infrastructure,training,andthe means of making the company data meaningful to the system.Does EIS warrant all of thisexpense? Green King certainly thinks so. They installed aCognos system in 2003 and their firstfew reports illustrated business opportunities in excessof £250,000. The AA is also using aBusiness Objects variant of an EIS system and theyexpect a return of 300% in three years.(Guardian 31/7/03)An effective Executive Information System isnt something you can just setup and leave it todo its work. Its success depends on the support and timely accurate data it getsto be able to provide something meaningful. It can provide the information executives need tomakeeducated decisions quickly and effectively. An EIS can provide a competitive edgeto business strategy that can pay for itself in a very short space of time.Q6. Discuss the challenges involved in data integration and coordination process?Ans:In general, most of the data that the warehouse gets is the data extractedf r o m a c o m b i n a t i o n o f l e g a c y m a i n f r a m e s ys t e m s , o l d m i n i c o m p u t e ra p p l i c a t i o n s , a n d s o m e client/server systems. But these source systems do notconform to the same set of businessrules. Thus they may often follow different namingconventions and varied standards for datarepresentation. Thus the process of data integration andconsolidation plays a vital role. Here,the data integration includes combining of allrelevant operational data into coherent data s t r u c t u r e s s o a s t o m a k e t h e m r e a d yfor loading into data warehouse. It standardizes thenames and datarepresentations and resolves the discrepancies. So me of thec h a l l e n g e s involved in the data integration and consolidation process are as follows.Identification of an EntitySuppose there are three legacy applications that are in use in your organization; oneis theorder entry system, second is customer service support system, and the third is themarketingsystem. Each of these systems might have their own customer file tosupport the system.Even most of the customers will be common to all these threefiles, the same customer oneach of these files have a different unique identification number.As you need to keep a single record for each customer in a data warehouse, youneed to getthe transactions of each customer from various source systems and thenmatch them up toload into the data warehouse. This is an entity identificationproblem in which you do notknow which of the customer records relate to the samecustomer. This problem is prevalentwhere multiple sources exist for the same entities andthe other entities that are prone to thistype of problem include vendors, suppliers, employees, andvarious products manufactured by a company.In case of three customer files, you have to designcomplex algorithms to match records froma l l t h e t h r e e f i l e s a n d g r o u p s o f m a t c h i n grecords. But this is a difficult exercise . If thematching criterion is toot i g h t , t h e n s o m e r e c o r d s m i g h t e s c a p e t h e g r o u p s . S i m i l a r l y, a particulargroup may include records of more than one customer if the matchingcriteriond e s i g n e d i s t o o l o o s e . A l s o , y o u m i g h t h a v e t o i n v o l v e y o u ru s e r s o r t h e r e s p e c t i v e stakeholders to understand the transaction accurately.Some of the companies attempt this problem in two phases. In the first phase, theentire records, irrespective whether they are duplicates or not, are assigned unique
  13. 13. identifiers and in the second phase, the duplicates arereconciled periodically ether throughautomatic algorithms or manually.Existence of Multiple SourcesAnother major challenge in the area of data integration and consolidation resultsfrom asingle data element having more than one source. For instance, cost values are calculatedandu p d a t e d a t s p e c i f i c i n t e r v a l s i n t h e s t a n d a r d c o s t i n g a p p l i c a t i o n .S i m i l a r l y, yo u r o r d e r processing application also carries the unit costs for all products.Thus there are two sourcesavailable to obtain the unit cost of a product and so therecould be a slight variation in their v a l u e s . W h i c h o f t h e s e s ys t e m s n e e d s t o b ec o n s i d e r e d t o s t o r e t h e u n i t c o s t i n t h e d a t a warehouse becomes an importantquestion. One easy way of han dling this situation is to prioritize the two sources, or youmay select the source on the basis of the last update date.Implementation of TransformationThe implementation of data transformation is a complex exercise. Youm a y h a v e t o g o beyond the manual methods, usual methods of writing conversion programswhile deployingthe operational systems. You need to consider several other factors to decide themethods to be adopted. Suppose you are considering automating the data transformationfunctions, youhave to identify, configure and install the tools, train the team on thesetools, and integratethem into the data warehouse environment. But a combination of bothmethods proves to beeffective. The issues you may face in using manual methods andtransformation tools arediscussed below.Manual MethodsThese are the traditional methods that are in practice in the recent past. Thesemethods area d e q u a t e i n c a s e o f s m a l l e r d a t a w a r e h o u s e s . T h e s e m e t h o d si n c l u d e m a n u a l l y c o d e d programs and scripts that are mainly executed in the datastaging area. Since these methodsc a l l f o r e l a b o r a t e c o d i n g a n d t e s t i n ga n d p r o g r a m m e r s a n d a n a l y s t s w h o p o s s e s t h e specialized knowledge in thisarea only can produce the programs and scripts.Although the initial cost may bereasonable, ongoing maintenance may escalate the costwhile implementing thesemethods. Moreover these methods are always prone to errors . Another disadvantageof these methods is about the creation of metadata. Even if the in -house programsrecord the metadata initially, the metadata needs to be updated every time thechanges occur inthe transformation rules.Transformation ToolsThe difficulties involved in using the manual methods can bee l i m i n a t e d u s i n g t h e sophisticated and comprehensive set of transformationtools that are now available. Use of these automated tools certainly improves efficiencyand accuracy. If the inputs provided intothe tools are accurate, then the rest of the workis performed efficiently by the tool. So youhave to carefully specify the requiredparameters, the data definitions and the rules to the transformation tool.A l s o , t h et r a n s f o r m a t i o n t o o l s e n a b l e t h e r e c o r d i n g o f m e t a d a t a . W h e n yo u s p e c i f yt h e transformation parameters and rules, these values are stored as metadata by the tool andthismetadata becomes a part of the overall metadata component of the datawarehouse. Whenchanges occur to business rules or data definitions, you just have to enter the
  14. 14. changes into thetool and the metadata for the transformations get adjustedautomatically. But relying on thetransformation tools alone without using the manualmethods is also not practically possible.Transformation for Dimension Attributes Now we consider the updating of the dimension tables. The dimension tables are more stableinnature and so they are less volatile compared to the fact tables. The fact tableschangethrough an increase in the number of rows, but the dimension tables changethrough thechanges to the attributes. For instance, we consider a product dimensiontable. Every year,rows are added as new models become available. But what about theattributes that are withint h e d i m e n s i o n t a b l e . Y o u m i g h t f a c e a s i t u a t i o n w h e r et h e r e i s a c h a n g e i n t h e p r o d u c t dimension table because a particular product wasmoved into a different product category. Sothe corresponding values must be changed inthe product dimension table. Though most of the dimensions are generally constant over aperiod of time, they may change slowly.http://www.scribd.com/doc/44415081/MB0036-Business-Intelligence-amp-Tolls-Fall-10
  15. 15. SET 2Q.1 Explain business development life cycle in detail? [10 Marks]May 092012Ans.The Business development Lifecycle is a methodology adopted for planning, designing,implementing and maintaining the BI system. Various steps involved in this approach aredepicted below.Each of the phases in the above life cycle is described below.Project PlanningDeveloping a project plan involves identification of all the tasks necessary to implement the BIproject. The Project Manager identifies the key team members, assigns the tasks, and developsthe effort estimates for their tasks. There is much interplay between this activity and the activityof defining the Business Requirements and aligning the BI system/data warehouse system withthe business requirements is very crucial. Therefore you need to understand the businessrequirements properly before proceeding further.Project ManagementThis is the phase wherein the actual implementation of the project takes place. The first step hereis to define the business requirements and the implementation is carried out in three phases onthe basis of the requirements. The first phase (includes technical architecture design, selectionand installation of a product) deals with technology, the second phase (includes DimensionalModeling, Physical Design, ETL Design & Development) focuses on data and the last phase(includes BI Application Specification, BI Application Development) deals with design anddevelopment of analytical applications. The steps in these phases are discussed below.1 Defining the Business RequirementsBusiness requirements are the bedrock of the BI system and so the Business RequirementsDefinition acts as the foundation of the Lifecycle methodology. The business requirements
  16. 16. defined at this stage provide the necessary guidance to make the decisions. This process mainlyincludes the following activities: Requirements planning Collecting the business requirements Post-collection documentation and follow-up2 Technical Architecture DesignCreation of the Technical Architecture includes the following steps:1. Establishing an Architecture task-force2. Collecting Architecture-related requirements3. Documenting the Architecture requirements4. Developing a high-level Architectural model5. Designing and specifying the subsystems6. Determining Architecture implementation phases7. Documenting the technical Architecture8. Reviewing and finalizing the Architecture3 Selection and Installation of a ProductThe selection and the installation of a business intelligence product is carried out in the followingsteps:1. Understanding the corporate purchasing process2. Developing a product evaluation matrix3. Conducting market research4. Shortlisting the options and performing detailed evaluations5. Conducting a prototype (if necessary)
  17. 17. 6. Selecting a product, installing on trial, and negotiating the value/price.4 Dimensional ModelingA dimensional model packages the data in a symmetric format whose design goals are obtainingthe user know-how, query performance, and resilience to change. In this step, a data-modelingteam is formed and design workshops are conducted to create the dimensional model. Once themodeling team is confident of the model prepared, the model is demonstrated and validated witha broader audience and then documented.5 Physical DesignIn this step, the dimensional model created in the previous step is translated into a physicaldesign. The physical model includes the details viz., physical database, data types, keydeclarations, permissibility of nulls.6 ETL Design & DevelopmentETL stands for Extraction, Transformation, and Loading. ETL tools are used to extract the datafrom the operational data sources and to load the same into a data warehouse.7 BI Application SpecificationIn this step, a set of analytical applications are identified for building a BI system based on thebusiness requirements definition, type of data being used, and the architecture of the warehouseproposed.8 BI Application DevelopmentThis is step wherein a specific application (tool) is selected from the identified applications foractual implementation of the BI system.9 DeploymentThis is the step wherein the technology, data and analytical application tracks are converged. Thecompletion of this step can be assumed as the completion of actual building of the BI system.10 Maintenance & GrowthDuring this step, the project team provides the user-support to the end-users of the system. Also,the team involves in providing the technical support required for the system so as ensure the
  18. 18. continuous utilization of the system. This step may also include making some minorenhancements to the BI system.Revising the Project PlanningAs the project makes progress, the project manager of the project has to revise the project plan toaccommodate the new business interests, concerns raised by the end-users.http://www.scribd.com/doc/75437915/MI0036-SET-1-amp-SET-2http://www.scribd.com/doc/75437878/MI0034-SET-1-amp-SET-2http://www.scribd.com/santosh143hsv143

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