AN ARCHITECTURAL APPROACH TO ANALYZE

INFORMATION QUALITY FOR INTER-ORGANIZATIONAL

SERVICE

Shuyan Xie

School of Computing, Dublin City University,Glasnevin, Dublin 9, Dublin, Ireland

Markus Helfert

School of Computing, Dublin City University,Glasnevin, Dublin 9, Dublin, Ireland

Keywords: Information Architecture, Information Quality, Inter-organizational Service, Emergency Medical Service.

Abstract: In inter-organizational service (IOS) system, the quality of information exchanged and shared by involved

organizations is important. Although information quality (IQ) has been emphasized for decades, IQ

problems still widely exist. For a significant class of information related to semantic issues, it is necessary to

improve information quality not just by working on the information/data itself. However, this is not

commonly understood and often leaves little doubt about the effectiveness of the current approach. We

consequently propose an architectural approach to enhance IQ for IOS: enterprise-level information

architecture allows a rich contextual environment to guarantee IQ, and provides a traceable path to measure

IQ across organizations. It is demonstrated in an emergency medical service enterprise.

1 INTRODUCTION

In the class of applications that heavily depends on

information quality (IQ), a typical approach to solve

the IQ problem usually starts and ends with the

activities scoped to the physical data storage level

(Wang et al., 1995). But most of the efforts have

only been successful to a certain degree (Drake et

al., 2004). Given that in business applications

information exists within the context of business

processes, the attempts to solve IQ problems at the

purely physical information level are not effective

(Drake et al., 2004). The physical level does not

capture the requisite semantics to accurately

communicate information across processes. As a

result, most of the semantic information issues exist

at the exchange processes and organizational

boundaries. For inter-organizational service (IOS)—

we see it embedded in one enterprise—the top

(enterprise) level is the focal point with the highest

probability for data discrepancy (Eden and Kazman,

2003). Enterprise level information models are

practically absent in current organizations, and

therefore lacks of effective communication for the

information across enterprise wide, organization or

service boundaries.

This paper proposes to extend the information

centric approach and focuses on creating an

enterprise architectural view to analyze information.

The reminder of the paper is structured as follows:

Section 2 outlines the research methodology.

Background is discussed in section 3. Section 4

presents our information architecture model, which

is embedded in an Emergency medical service

(EMS) case. We conclude our paper in section 5 by

summarizing the main contributions together with

some remarks for further research.

2 RESEARCH METHODOLOGY

This research is mainly concerned with theory

building and thus it can be classified as being

interpretive in nature. Interpretative research does

not predefine dependent and independent variables,

but focuses on the full complexity of human sense

making as the situation emerges (Anderson et al.,

2005). The architectural approach for IQ analysis

438

Xie S. and Helfert M..

AN ARCHITECTURAL APPROACH TO ANALYZE INFORMATION QUALITY FOR INTER-ORGANIZATIONAL SERVICE.

DOI: 10.5220/0003503604380443

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 438-443

ISBN: 978-989-8425-56-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

presented in this paper has been illustrated by

reviewing existing relevant literature in the domain

of data, information quality, architecture, enterprise

architecture (EA) and IA. The theoretical proposal

presented in this paper consists of concepts and their

relationships, as identified from literature, which

have been further demonstrated in real case study.

Thus, this research can be described as being

interpretive and grounded in literature.

3 BACKGROUND

3.1 Information Quality

Information is difficult to manage from business

standpoint. More specifically, it is not the

information itself that is difficult, the problems lie

with the people and processes using that

information. So, in order to improve IQ under

sharing environment across organizations, we must

focus from dynamic perspective: the process of

distributing information. Common definition of IQ is

“fitness for use”. Information are high quality if they

are fit for their intended uses in operations, decision

making, and planning (Wang, 1998). In this sense,

when information shared and exchanged among the

parties are deemed of high quality if free of defects

and process desired features. It point to the notion

that IQ needs to be measurable and being measured

appropriately while they are shared by multiple

organizations.

Data is produced by measurements or

observation (Drake et al., 2004), which brings to an

important concept—a notion of data context or

metadata—that is critical to the success of IQ

improvement efforts. One of the causes for poor IQ

problems is the lack of sufficient information

context. To solve the poor IQ problem, information

context should be defined and well understood

(Drake et al., 2004, Eden and Kazman, 2003).

3.2 Architecture Approach

The word architecture is used whenever a high-level

overview of interrelated components wanted to be

defined, and when the relationships among them are

complex and difficult to understand. Architecture is

generally can be captured as a set of abstractions

about the system that provides enough essential

information to form base for communication,

analysis, and decision making. (Foegen and

Battenfeld, 2001, IEEE, 2000, Kazman et al., 1996,

Kruchten et al., 2006, Rechtin, 1992). This points to

the notion that architecture allows better

understanding on the components and their

relationships. In-depth discussion of various

architectures is out of scope for this paper. We focus

on EA to analyze enterprise-level IA.

Various EA frameworks define several views

that focus on specific aspects such as business,

technology, information, and so on, to reduce the

complexity. IA has been indicated as one important

component for EA (IEEE, 2000, Laudon and

Laudon, 2002). IA defines and establishes the

information component of the EA by providing

abstract representations of corporate information.

This is where information requirements are specified

at a high level, typically as subject areas, entities,

and relationships. In doing so, all other EA

components must be included. These relationships

characterize how and by whom data is used and

where it flows. The IA is used for understanding the

information needed and used by people in

performing tasks and business processes.

Information is created by processes and tasks and is

shared with other processes and tasks (Rood, 1994).

Figure 1 presents an understanding on the trend

and focus of IA based on the timeline. IA is

originated with static structure for information

management. Researchers initially identify and

employ the need for flexible IA in considering of the

dynamic information environment. With the

increasing dynamic requirements, researchers begin

to focus on IA framework to demands under

different situations (Campbell and Hummel, 1998,

Duncan and Holliday, 2008, Ray et al., 2003, Riva

and Rodriguez, 2002). From the time trend, it shows

increasing attention on dynamic aspects in terms of

the changeable and situational environment, as for

example the structure or design of the environment

with the data collection, data exchange. As dynamic

concept is developing, such concept is applied back

to and strengthened the context of static IA

(Sherman, 2002).

Definition of architecture has been discussed by the

researchers and practitioners, but there are no single

defined concept is accepted. In general, there are

two basic approaches that can be noticed regarding

these definitions, one sees architecture as a

descriptive concept (show as close circle in Figure

1) that factually describes the characteristics of

existing artifacts, whereas the other sees architecture

as prescriptive concept (show as open circle in

Figure 1) that defines how artifact should be

realized. It indicates that the former approach allows

information elements exchanged and shared to be

described and mapped for IQ assessment and

measurement; the later approach indicates that from

design point of view, enterprise-level IA allows a

contextual environment to design to guard the IQ.

AN ARCHITECTURAL APPROACH TO ANALYZE INFORMATION QUALITY FOR INTER-ORGANIZATIONAL

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Figure 1: Information Architecture Research.

From the discussion and analysis, we describe

that enterprise-level IA as a set of different

information elements so connected or related as to

perform a unique function, which is not performable

by the elements alone. All indicate that the

enterprise-level IA contains data definitions of the

enterprise constituencies as well as the relationships

among these constituencies. This again point to the

notion that architecture provides a consistent

contextual environment. We define that IA

represents/defines the structure of information,

including static aspects as a mapping showing the

information elements, the interfaces and

relationships between the various information

elements (IEs), and dynamic aspect that the

relationship of how the information shared and used

across organizations. Thus, it allows a path for better

IQ measurement by tracing the process.

4 ARCHITECTURAL ANALYSIS

FOR IQ

4.1 Architecture as Metadata Source

Architecture can be seen as metadata source. A rich

contextual environment (metadata) needs to exist,

and a comprehensive set of models is needed to

produce these models. The EA modeling efforts can

produce these models (Fuller and Morgan, 2006).

Time-critical services such as EMS introduce

complexities to multi-organizational information

sharing, including the need for timely information in

a form that can be trusted and used by emergency

responders (Dawes and Prefontaine, 2003, Horan

and Schooley, 2007). IQ problems exists in EMS

(Fisher and Kingma, 2001), however they often find

it difficult to assess their current IQ. As mentioned

previously, a notion of information context is

absolutely critical to the success of any IQ

improvement effort. This notion is at the crux of the

poor information quality problem—insufficient

information context (metadata).

A comprehensive set of models is necessary to

produce this desired metadata. To work with such

architectural complexity, some decomposition

method s needed. One such method is the layered

model. The information layer is where elements

concerning information and data are captured and

managed. We introduce the layered model and give

an overview of the main constructs available for

modeling IA. A possible layering can constitute a

conceptual layer at the top, the logical layer in the

middle, and the physical layer on the bottom (Bruel

et al., 2002). This model assumes an information-

centered approach. Adapted from National

Intelligent Transportation System (ITS) Architecture

“provides a common structure for the design of

intelligent transportation systems” and prescribes a

general model that supports the development of

many different designs (2003). A simplified three-

layered model is shown in figure 2. As with the

other layers of the core meta model, it is split into

the following views:

Conceptual—where defines the ‘what’.

Information terms this means ‘what’ information

concepts are required within each domain.

Logical— where defines the ‘how’. In

information terms this is the next level of abstraction

down, where defines ‘how’ the information concept

are used. In this layer, it presents a functional view

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Figure 2: Enterprise-level Information Architecture Three-layered Model.

that consists of specifications that are used to

perform user services. In this view, the functions are

represented in a set of data flow diagrams.

Physical – The physical information view

captures a particular view of information managed.

It is a physical representation of important interfaces

and components, and it divides the logical

architecture functions into a number of high level

classes.

The model shows that enterprise information

models lie in both the conceptual and in the logical

layers, and provide the foundation for consistent

interaction between these layers.

Reflection to the National ITS Architecture

(2003), this IA framework is developed along two

dimensions: horizontal and vertical. Traceability

along both dimensions is necessary for the vertical

(between conceptual, logical, and physical) and

horizontally within each layer but across the

organizational boundaries. It is important to

emphasize the business process is needed to be

involved as the foundation for this IA model.

In the proposed three layered view of the IA, the

conceptual layer includes the highest possible level

of abstraction and therefore it captures the

foundational components and their relationships.

Thus, the top layer model is very stable and is not

subject to change unless the most essential

underlying structures change. The information

elements that are defined at this level are cross

referenced among the layers. Every element has at

least one conceptual definition that references it. The

reverse is also true: there is no information element

presented that does not exist in the conceptual layer.

In the EMS case, where EMS is seen as a single

enterprise that consists of multiple organizations,

each individual organization will need to have its

own three-layered organization-level IA model,

where top-level organizational information concepts

and the corresponding information elements will be

mapped unambiguously to the enterprise-level

model concepts. In this sense, only the elements that

have their counterparts at the enterprise level can be

possibly mapped. By relating each organizational

level definition to the common enterprise level

equivalents, we are eliminating semantic mismatch

between different organizations. Since the

information elements are cross-referenced with the

process specification, there is enough contextual

information to correlate information elements at the

enterprise and individual organizational levels.

The logical layer of this enterprise focused IA

model emphasizes information-related

considerations and defines specifications for

enterprise-level information. There are

data/information that needs to be constructed to

support the business processes defined at the top

layer of the model. By defining information

requirements in terms of the business processes,

another major cause of low data quality is

eliminated: the disconnect between the business and

information view (Mukhopadhyay et al., 1995).

Under EMS case which is also known as a multi-

organizational enterprise, it is quite common for

more than one system to be operating on information

elements from objects defined at the top conceptual

layer. Under this model, each system specification

will define its own unique information attribute, but

all these attributes are in turn mapped to the one

element at the top layer. This top-down

decomposition helps to alleviate a problem that is

similar to “departmental information silo”

(McGuffog, 1997).

Such proposed model for IA is necessary for IQ

analysis and improvement, especially for a complex

AN ARCHITECTURAL APPROACH TO ANALYZE INFORMATION QUALITY FOR INTER-ORGANIZATIONAL

SERVICE

441

socio-technical enterprise like EMS enterprise that

involves a strong dynamic aspect of the information

elements.

4.2 Architecture Provides Dynamic IQ

Measurement

In our description of information architecture, IA

indicates: (1) Information elements (IEs), (2)

Structure of IEs in an enterprise, (3) Information

relationship/flow/exchange among all the involved

organizations. We envision the structural

architecture from static and dynamic aspects for

EMS case. As showed in Figure 3, IEs (incident

information, patient information etc.) can be

presented as one way sequence, end-to-end

sequence, or two-way sequence while shared across

Organizations (components). By mapping the

structure in a static view and the information flow

process, it enables the dynamic consideration to

monitor information/data that is generally isolated

within each of the individual organizational

environment. It allows to detect the information

quality gap (accuracy, relevance, completeness etc.).

Figure 3 shows a high-level overview information

transmitted across organizations in emergency case.

As knowing the static information structure and the

dynamic information flow, we can systematically

analyze and measure the IEs in each process. The

way how information evolves and is connected

provides a path to trace complex information

relationship. We can trace the changes within and

across the data stores—Computer Aided Dispatch

(CAD), Patient care records (PCR), and Hospital

Information system (HIS)—that allows us to

measure the data quality from end-to-end, following

the concept of information manufacturing systems

that produces information products of which quality

can be measured (Pham Thi and Helfert, 2007).

5 CONCLUSIONS

The main contribution of this paper is proposing an

approach to describing enterprise-level IA and using

these descriptions to indicate that IQ can be

improved if architectural concept is enhanced.

Enterprise-level IA can be rich metadata source to

guard IQ as the information definition is traceable

across organizations vertically and horizontally;

Descriptive IA mapping allows IQ assessing and

measuring both statically and dynamically. A robust

traceability mechanism is necessary for high-quality

information. The architectural models provide a

foundation for the information traceability and thus

quality information. We demonstrate it within the

EMS case. This paper further confirmed the project

idea of that information focused architecture

provides a tool for information assessing and

measuring, and therefore improve the quality.

Figure 3: Information Flow across Organizations.

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