STRATEGIC INFORMATION REQUIREMENTS ELICITATION
Definition of Aggregated Business Entities
Gianmario Motta and Giovanni Pignatelli
Information and Systems Department, University of Pavia, Via Ferrata n.1 I-27100 Pavia, Italy
Keywords: Strategic Information Requirements, Aggregated Business Entities.
Abstract: This paper presents a universal meta-model for Strategic Information Requirements Elicitation and a
methodology to generate and use strategic information models. The framework fits the methodological gap
that exists in the Strategic Information Requirements and supports the analyst in (a) define structured high
level information requirements and (b) assess informational support from a variety of perspectives. The
meta-model enhance the e-TOM Aggregate Business Entity concept by the adding the concepts of
specialization and decomposition. The methodology uses several perspective to assure the robustness of
information requirements, their coverage on the IT infrastructure and the ownership of information.
Specifically the methodology includes various steps, namely the selection, customization, refinement and
validation of the ABEs, evaluation of the informative support and sensitivity analysis. The model can be
used for analysis, audit and strategic planning and may be leaned to CASE tools.
1 INTRODUCTION
In engineering, a requirement is a singular
documented need of what a particular product or
service should be or do. It is most commonly used in
a formal sense in systems engineering or software
engineering. It is a statement that identifies a
necessary attribute, capability, characteristic, or
quality of a system in order for it to have value and
utility to a user (Wikipedia).
Strategic Information Requirements analysis
describes high-level information requirements of the
whole enterprise or of a major portion. Strategic
requirements are aggregated and independent from
technology. The key point is to get not only a
framework where to accommodate requirements
collected by interviewing people, but to have a
rationale to define the information domains of a
given enterprise. This normative approach shortens
time and allows a better quality. Of course
normative framework should be refined, however it
provides a robust starting point. The output of
analisys is a schema that describes aggregated
information to be further analyzed. Actually, no
current methodologies or models define a structured
approach to strategic information modelling. The
aggregated schema can be the first step of a top
down strategic design or be used in IT strategic
planning to assess the coverage of information needs
by existing databases or the impact of business and
Technological discontinuities on information
domains.
2 STATE OF ART AND POSITION
The need of a structured approach to identify a
business information strategy emerged in the early
years of IT, when IT started to automate entire
business processes, such as the production cycle, or
became a real tool for management. In short we can
identify some main approach categories.
In integrated approaches the analyst identifies
the information used by business processes. This
approach provides a cross-organisational view and
enables the identification of global information
requirements.
375
Motta G. and Pignatelli G. (2008).
STRATEGIC INFORMATION REQUIREMENTS ELICITATION - Definition of Aggregated Business Entities.
In Proceedings of the Tenth International Conference on Enterprise Information Systems - ISAS, pages 375-380
DOI: 10.5220/0001725603750380
Copyright
c
 SciTePress

Generality
Normative capacity
Completeness of
domains
BSP/ISP
BSC
eTOM
Figure 1: Approaches comparison.
This approach provides a cross-organisational
view and enables the identification of global
information requirements. These methods are
effective to define inter-functional requirements. An
old timer and very popular in Eighties is Business
Systems Planning (BSP), that uses extensively
double entry grids. BSP identifies data classes and
associates processes and data classes in a grid, that
shows which data are used by which process. BSP is
a comprehensive but time consuming methodology,
and, specifically, does not provide a normative
framework to define what data classes consider. The
subsequent Information Strategy Planning (ISP)
by James Martin dominated the Eighties and
Nineties; it is an integration of BSP, ER modelling
and DFD and other requirements engineering
models. The integration is based also on specific
CASE. Therefore, ISP extended but did not give a
normative approach for strategic information
requirements.
In the Nineties and New Century the success of
ERP (Enterprise Resource Planning) and CRM
(Customer Relationship Management) software
suites has generated normative models both for IT
processes and database schemas. Enterprise buy a
solution, that can be customized by high level tools
as workflow etc. ARIS is a well known example of
an integrated analysis methodology developed for
ERP that models data, organization and business
processes. At a more strategic level, normative
industry models have grown. Specifically eTOM -
Shared Information Data Model (SID) addresses
information needs of shared information/data in
telecommunications industry. SID uses within a real
strategic view the Business
Entities and Attributes concepts. A Business Entity
is a thing of interest to the business, while Attributes
are facts that describe the entity. eTOM meta-model
is very promising, but it is not general, since it is
exclusively oriented on telecommunications, nor
provides an axiomatic approach to identify Entities.
Last not least business and management oriented
approaches have the objective of selecting key
information needs. In Eighties and Nineties Critical
Success Factor (CSF) has been a popular approach
to spot information for management. In the Nineties
and in New Century, Balanced Score Card (BSC)
and 6Sigma had an enormous success not only as
models for overall strategic and management control
but also as models for management information
needs. However, these models are partial, since they
analyze the management side and not the operations.
The orientation of these approaches modelling
are represented on three axes (
Figure 1). The axis of
generality represents the suitability of the approach
to the whole range of industries: the wider the range
the higher the generality. The axis of normative
capacity measures the ability to suggest the “right”
information requirements. The axis of completeness
of domains shows the capacity of considering all
information uses, namely management, analytic, and
operational. Each approach family excels on some
axis, but does not offer a comprehensive coverage.
This is specifically the aim of our approach.
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3 THE ABE MODEL
An Aggregate Business Entity (ABE) is a well-
defined set of information and operations that
characterize a highly cohesive, loosely coupled set
of business entities (TMForum,2003).
However, what candidate ABEs an
organization has? You need a guide to discover
them. This is precisely our purpose.
The guide combines several concepts. First is a
generalization of the business entity concept. These
include a hierarchy of classes, where the first level is
stakeholders, resources, context, output.
Second comes a classification of information in
three levels according to the time variability, namely
Master Data (structural the entity properties),
Transaction Data (properties of events) and
Analisys Data (properties for management and
governance). The result of the combination is a level
zero grid (Table 1) that crosses the two main
information classification criteria, elaboration level
and business domain. Each cell of the grid
represents a standard ABE that could be seen as a
couple (D,E) where D is the domain and E is the
elaboration level.
Table 1: The ABE standard grid.
INFORMATION TYPE
Master
Data
Trans-
action
Data
Analysis
Data
INFORMSTION DOMAIN
Stakeholders
Law LAM LAT LAA
Competitor COM COT COA
Customer CUM CUT CUA
Supplier SUM SUT SUA
Broker BRM BRT BRA
Shareholder SHM SHT SHA
Resources
Personnel PEM PET PEA
Plants PLM PLT PLA
Raw
materials
RAM RAT RAA
Cash CAM CAT CAA
Con-
text
Structure STM STT STA
Project PJM PJT PJA
Region REM RET REA
Output
Process PRM PRT PRA
Product PDM PDT PDA
Service SEM SET SEA
4 THE ANALYSIS
METHODOLOGY
Our approach enhances the e-TOM ABE concept by
the adding the well known concepts of specialization
and decomposition; itincludes:
1. Developing the ABE grid:
a. Step 1 Selection
b. Step 2 Customization, refinement and
validation
2. Using the ABE grid:
a. Step 3 Assessment of the information
support
b.Step 4 Sensitivity analysis
4.1 Step 1: Selection
The analyst starts with the standard list of Table 1,
and (1) defines the scope of analysis on the standard
domain and grid levels and (b) adds properties to the
selected ABEs, that are labelled with a P
xxx
in Table
2. Of course the analyst ca use a knowledge base of
normative models.
Table 2: An example of personalized grid.
ELABORATION LEVEL
Master
Data
Transaction
Data
Analysis
Data
DOMAIN
Stakeholders
Law
PLAA1
PLAA2
PLAA3
Competitor
PCOA1
PCOA2
Customer
PCUM1
PCUM2
PCUT1
Supplier PSUM1
PSUT1
PSUT2
Broker
PBRM1
PBRM2
PBRT1
PBRT2
PBRT3
PBRA1
PBRA2
4.2 Step 2: Customization, Refinement
and Validation
The step customizes the set of ABE that is specific
to the individual enterprise within the analysis
scope. An example of such customization is Table
3
where the standard domain “customer” has been
specialized in the domains “private” and
“enterprise”. Similarly master data have been
STRATEGIC INFORMATION REQUIREMENTS ELICITATION - Definition of Aggregated Business Entities
377

specialized in “Identification and “Social” and the
same happens with Transaction data.
In short the output grid is obtained by primitives
of Creation, Specialization, Decomposition on
standard information levels and domains. In the grid,
a cell identifies a candidate ABE of an individual
enterprise. The process is iterative, with refinement
and validation sessions with key business
representatives. The output grid can be used for a
variety of purposes, such as assessing the functional
coverage of ABE by actual database systems, the
impact of business discontinuities etc.
Table 3: An example of specialization of the domain
“Customer”.
INFORMATION TYPES
Master
Data
Transaction Data
Analysis Data
Identification
Social
Man-Machine
transaction
Machine-
Machine
transaction
Customer
Private
Enter-
prise
4.3 Step 3: Assessment of the
Information Support
To evaluate how ABE are actually supported and /
or used, we cross ABEs with business processes,
organizational structures, IT applications and IT
architecture. The grids describe relations G
information classes I to information users U
(business processes, organizational structures, IT
applications and IT architectural elements):
G = {U,I,A}
(1)
Figure 2: ABE Relations Metamodel.
As shown in (Table 4) each entity of the resulting
information meta-model contains also two self-
relations, representing decomposition and
specializations. The meta-model may be used to
assess both as-is and to-be scenarios from a variety
of perspectives.
The Information and Database grid assesses to
what extent databases cover a domain of ABEs. In
Table 4 the coverage given by actual databases
(Laboratory, Financial, Reservation) of a healthcare
institution is assessed. The coverage looks poor and
no integrated of patient and service data are possible.
Of course assessment metrics is qualitative and
reflects a joint evaluation by analysts and user, but,
management know where gaps are.
Other grids allow other and complementary
analyses. The Information and Application grid
assesses the use of information by applications in
terms of information lifecycle and/or qualitative
metrics
The Information and Organizational structure
grid identifies information ownership and it is a key
point to set up the growingly important data policies
of enterprises.
The grid of Information and processing levels
identifies where information is distributed in the
different levels of processing architecture (client,
server, mobile devices) and it is a key to define
security and privacy strategies (who really manages
data?).
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Table 4: Assessment of ABE coverage (absent, poor, average and good) in a generic Healthcare Institution by using
qualitative evaluation scale (M = Master information ; T = Transaction Information; I = Indicators information).
LAB FINANCIAL RESERV
Completetness Crrrectness Timeliness Completetness Crrrectness Timeliness Completetness Crrrectness Timeliness
Regulatio
n
M
Regulation ID
Privacy Laws
Healthcare regulations
T
Certification
Events
List of Certifications
I
Certification KPIs Certification levels
Customer
Emergency
M
ID Master data
Properties Patient Record
T
Emergency
events
Reception
Prescriptions
Treatments
Other
Release Referral & payments
I
Process KPIs Quality - Service -Cost
Hospital
M
ID Master data
Properties Patient Record
Care process
events
Reservation
Check-in
Prescriptions
Treatments
Patient management
Release Referral & payments
I
Process KPIs Quality - Service -Cost
Day hospital
M
ID Master data
Properties Patient Health Record
T
Day hospital
events
Reservation
Check-in
Treatment
Release Referral & payments
I
Process KPIs Quality - Service -Cost
Commis- sioner
Healthcare
authorities
M
ID Master Data
Properties Financial data
T
Events
Advance payments
Reimbursements
I
Process KPIs Quality - Service -Cost
Personnel
Financial
M
ID Master data
Properties
Job data
Skill and education
T
Events Presences & Payroll
Certifications Education
I
KPIs Performance & potential
Technical &
Medical
M
ID Master data
Properties
Job data
Skill and education
T
Events
Presences & Payroll
Career
Certifications Skill certificates
I
KPIS Performance & potential
STRATEGIC INFORMATION REQUIREMENTS ELICITATION - Definition of Aggregated Business Entities
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4.4 Step 4: Sensitivity Analysis
The objective of the sensitivity analysis is to identify
which information domains are involved by strategic
actions or discontinuities, by assessing impact of a
variety of business variables e.g.:
1. Business Discontinuity: it evaluates the impact
of the enterprise strategies e.g. mergers,
acquisitions, new products, new services
(which ABE will be affected and how much?)
2. Technology Discontinuity: it evaluates the
impact of technology changes on information
3. Sensitive information: it evaluate the impact
of regulations e.g. privacy, security etc.
5 CONCLUSIONS
The paper proposes a universal meta-model for
strategic information modelling and a methodology
based to generate and use strategic information
models. The model generalizes some normative
concepts born in an industry normative model
(eTOM) . In short:
 It is normative, and guides the analyst and
management to identify the “right”
information requirements
 It is cross- industry
 It is strategic and avoids useless details
 It easy to understand for management and
supports a what-if analysis of business
strategic alternatives
 It can be linked to detailed information
requirements analysis
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