DATA INTEGRATION ISSUES FOR BUSINESS INTELLIGENCE

INTEGRATED ENTERPRISE INFORMATION SYSTEMS

Pierre F. Tiako

Center for Information Technology Research, Langston University, Oklahoma, USA

Keywords: Data Integration, Enterprise Information System, Business Intelligence, Distribution, Web Technology.

Abstract Business Intelligence (BI) provides the ability to access any type of data inside or across enterprises and to

analyze and present them as usable information. To work on business intelligence, an enterprise has to deal

with important problems relating to both (1) Data integration and (2) Analysis and presentation of data for

strategic decision-making. No matter what the application, the need for business intelligence applies

universally. This position paper focuses on data integration issues for business intelligence integrated

Enterprise Information Systems (EIS).

1 INTRODUCTION

Today’s enterprises are judged not only on the

quality of their products and services, but on how

well they share information with customers,

suppliers, shippers and banks in the Supply Chain.

The more widely available information is throughout

the enterprise, the more valuable it becomes. The

information actors in the Supply Chain are not in the

same location, but are distributed among them. Each

actor has its own data representations and

presentation mechanisms. This makes sharing and

exchanging information across partners extremely

difficult. The more integrated an enterprise becomes

the easier it is for customers and other partners to

access data they need. They are then empowered to

make their best decisions to buy or sell.

Several research and industrial contributions

(Builders, 1999; Acta, 1999; Enosys, 2002)

introduced the concept of business intelligence to

refer to an Internet-driven organization that uses

information in an intelligent fashion. Business

intelligence leverages the ubiquitous Internet

network to deploy information consistently and

accurately across departments and business units of

the same enterprise internally or among autonomous

partners externally involved in the Supply Chain.

Business intelligence doesn’t replace e-business;

rather, it is a critical part of an e-business strategy.

This report progress focuses on critical data

integration issues for business intelligence integrated

EISs. In continuation, this paper is organized as

follows. Section 2 is devoted to present concepts of

data integration for business intelligence. Section 3

presents critical issues for designing integrated EISs

for business intelligence. Section 4 includes

discussion on related work and perspectives.

2 BUSINESS INTELLIGENCE AND

DATA INTEGRATION

Business intelligence can be regarded as use of

information for strategic decision-making. From this

point of view, business intelligence mainly deals

with data integration and strategic decision-making

processes. BI supports the following cognitive

phases: (1) Exploration of the world of information,

(2) Interrogation of the base of information, (3)

Analysis of the base of information, (4) Annotation

based on the individual preferences and discoveries

(David and Thierry, 2003).

2.1 Business Intelligence

Business intelligence provides the ability to access

any type of data inside or across enterprises and to

analyze and present them as usable information. To

work on business intelligence, an enterprise has to

deal with important problems relating to both (1)

Data integration and (2) Analysis and presentation of

data. No matter what the application, the need for

business intelligence applies universally. For e-

commerce applications targeting the mass consumer

market, the most important aspect is intelligence

366

F. Tiako P. (2005).

DATA INTEGRATION ISSUES FOR BUSINESS INTELLIGENCE INTEGRATED ENTERPRISE INFORMATION SYSTEMS.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 366-369

DOI: 10.5220/0002538303660369

 SciTePress

about the likes, dislikes, demographics and behavior

patterns of customers and prospects; just as critical

for business-to-business vendors is analysis about

partners, suppliers, and manufacturing and

distribution activities. Without business intelligence,

competitive e-businesses are flying blind.

Integration challenge (Kiper, 1994; ETI, 2002)

requires different approaches to the problem that

supplements the traditional messaging solutions

offered by Enterprise Application Integration (EAI).

EAIs propose various architectural approaches to

integration, but most have a wide range of interfaces

and work by simply broadcasting transaction

information to other connected systems.

2.2 Hierarchical view of Integrated

EISs for Business Intelligence

Each project related to tools integration (Brown,

1992) must be categorized according to the five

dimensions of integration proposed by Wasserman

(1990), Thomas and Nejmeh (1992). The

dimensions are presentation, platform, control data,

and process integration. For software environments

resulting from integrated project support

environment projects, Brown (1992) considers three

aspects of integration (see figure 1): presentation,

control and data integration. Presentation integration

deals with the way users interact with EISs using

services of user interface manager. Control

integration aims at coordinating tools execution

(Brown and Wallnau, 1996). A tool invokes another

for realizing an action, rather than implementing that

action. Data integration deals with the problems of

sharing or exchanging data between tools.

2.3 Research and Industrial

Approaches

Industrial solutions dealing with data integration

favorable to business intelligence (see table 1) can

be grouped into the following categories: (1) EAI

systems, which are vital to the creation of

collaborative e-business applications because they

enable the integrity of data and business rules to be

preserved; (2) Business-to-business integration

(B2Bi) systems that focus on extending EAI

between companies across the Internet (they

typically do not offer in-depth data integration

capabilities beyond Web-scraping technologies); and

(3) Enterprise Information Integration (EII) that can

be distinguished from the other by its underlying

technology that provides real-time, seamless access

to any data, anywhere, for any application.

Depending on each enterprise’s unique situation,

EII can be applied to perform rapid, high-level data

integration either as a stand alone solution or within

the framework of a larger integration project.

Because EII technologies work over standard web

connections to access an array of data sources

(Dossick and Kaiser, 1996) they can be invoked as a

web service or embedded within an integration

application rapidly and easily.

Category Definition Function Technology

(Industry)

Enterprise

Application

Integration

(EAI)

Systems

Systems that

link disparate

enterprise

applications

Integrate

applications at

business

process level

with focus on

transaction.

RMI Applet

(Java Soft)

OLE

(Microsoft)

ODBC JDBC

APIs

(Research)

Control

Integration

(SoftBench,

2002)

Field(Reiss,

1994)

Approaches that

control tool

during

integration

process

Handle

integration

messages

exchanges

between EIS

CORBA

(OMG)

DCE (OSF)

SOM/DS0M

(IBM)

(SOM, 1994)

(Industry)

Business-to-

business

integration

(B2Bi)

Systems

Systems that

automate point-

to-point trading

transactions

among partners

Help migrate to

the web without

disrupting

existing

business

practices

HTML

CORBA

(OMG)

(Research)

Data

Integration

(Boudjlida,1

995)

Contributions

that deal with

data integration

during the

integration

process

Support

semantic and

syntactic

information

relating to data

during

integration

CORBA

(OMG)

DCE (OSF)

SOM/DS0M

(IBM)

(Industry)

Enterprise

Information

Integration

(EII)

Systems

Systems that

access,

integrate, and

unite data from

multiple sources

Provide real-

time data access

within and

through

corporate

firewalls.

XML

CORBA

(OMG)

Table 1: Existing Technologies for integrated

EIS

End User

Presentation

Control

Data

User Interface

Manageme

Message

Repository

Protocol

Figure 1: Hierarchical view of an EIS's architecture.

DATA INTEGRATION ISSUES FOR BUSINESS INTELLIGENCE INTEGRATED ENTERPRISE INFORMATION

SYSTEMS

367

The same research contribution dealing with data

integration in favor of business intelligence can be

group into the following categories: (1) data

integration, which is concerned with data

representation and naming. Significant contribution

carried out in data integration approaches proposes a

dynamic approach based on an abstract model,

which captures data semantics and constraints. The

model includes a set of constructors, which allow

defining complex structures and a set of operators

that determine the transformations that could be

applied to change one type to another. (2) Control

integration, which is concerned with logic or

structure of programming model. Control also

implies that a tool can decide whether and how

much to share operations it supports and data it

manages. Thus a tool could be added or removed

from an EIS; it could participate in more than one

interoperating EIS.

Despite the number of research and industrial

approaches to address, they are almost partial

because each of them is expressively aimed at

addressing a particular aspect of integration. Again

no matter what the application, the need for business

intelligence applies universally. The idea of data

integration for business intelligence is to provide a

more general architecture supporting tool

integration, for we need to combine a set of

mechanisms in developing a complete and coherent

system, as presented below.

3 CRITICAL ISSUES FOR

DESIGNING INTEGRATED EII

FOR BUSINESS INTELLIGENCE

The critical issues for designing Integrated

Enterprise Information Systems for Business

Intelligence (IEISBI) are listed below. IEISBI have

to address these issues in order to be competitive.

Recent research, as well as new products, focuses

on Web Services as a means to integrate data.

However, Web Services are new technology and all

the requirements discussed above and issues listed

below still apply. For instance, semantic Web

Services (WSMO, 2004) addresses the requirements

and issues taking all aspects into consideration.

(a) Communication. IEISBI technology must

provide reliable and secure communication

between back end applications in order to

ensure overall consistency

(b) Distribution. IEISBI applications are

distributed in the sense that each has its own

separate storage or database management

systems with each separately controlled and

managed

implemented based on their own data model and

due to the particular management focus the data

models differ in general not complying with a

common standard.

(d) Mediation. Due to heterogeneity of IEISBI

applications, objects are represented in different

data models that have to be mediated if objects

are sent between applications.

(e) Process management. Multi-step business

processes across IEISBI applications require

process management (Tiako, 1998) to

implement the particular invocation sequence

logic.

(f) Reliability. The integration of IEISBI

applications must be reliable in order to neither

loose data nor accidentally introduce data by

retry logic in case of failures

(g) Replication. Replication is a mechanism that

ensures that changes of an object are

automatically propagated to duplicates of this

object. The various representations act as if they

are one single representation

(h) Security. Communication of data between

IEISBI applications through EIS technology

must ensure security to avoid improper access.

4 RELATED WORK AND

PERSPECTIVES

Here we briefly present some EIS tools available in

the market and susceptible to favor business

intelligence by data integration. WebFOCUS

(Builders, 1999) connects core business systems,

enabling data to flow freely from one to another.

ETI (2002) automate the process of data collection,

transformation, and migration between incompatible

systems in heterogeneous computing EISs.

ActaWorks (Acta, 1999) solutions connect ERP

systems to business to business web applications, to

business to commerce web applications and to

direct-to-the-web data access applications.

Informatica (2000) outlines the current state of

convergence of e-business and business intelligence,

and shows how its data integration solutions support

the key infrastructure demands of e-businesses.

Enosys (2002) shows how XML presents significant

advantages as the data representation language for

modern e-business applications and addresses their

needs better.

ICEIS 2005 - DATABASES AND INFORMATION SYSTEMS INTEGRATION

368

The emerging of the Internet has accentuated the

need for supporting a broad diffusion of information

and the openness of EISs. To meet these needs, user-

friendly interfaces must be based on HTTP protocol

(Riva and Ramoni, 1996) and HTML. So, user

interfaces should benefit from standards of scripting

programming languages such as JavaScript

(Flanagan, 2002) for increasing the interactivity with

users.

The distribution of components of an EIS can be

realized by coupling Web technologies and

distributed object approaches (Brose et al., 2002)

such as CORBA or COM, including a Java

implementation of its specifications. The resulting

platform built on top of this kind of association are

opened, and characterized by the following

properties:

(a) Neutrality of the platform. Means independent

of the underlying operating system and

hardware architecture.

(b) Portability of components. Components of an

EIS can be moved from one platform to another,

with little effort, by adapting the new

component for its integration in an already

existing EIS.

the Web browser is completely separated from

the server that supports the processing and the

storage of data. Interoperable tools which make

up an EIS are installed on different computers.

This way of distributing components of an EIS, by

using the Web as support, is adopted by different

approaches, which are grouped here in two

categories. The difference between these proposals

lies in methods the Web accesses legacy server

applications and their data. Data integration issue for

business intelligence presented in this paper differs

from the above solutions by its openness. The future

of this work will study the issues presented above.

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