A consensus-oriented approach
Ralf Knackstedt, Karsten Klose, Björn Niehaves, Jörg Becker
European Research Center for Information Systems, University of Muenster, Leonardo-Campus 3, Münster, Germany
Keywords: Data Warehouse development, Management Information Systems, Process Reference Model, Theoretical
Foundation, Epistemology
Abstract: IS literature provides a variety of Data Warehouse development methodologies focussing on technical
issues, for instance the automatical generation of Data Warehouse or OLAP schemata from conceptual
graphical models or the materialization of views. On the other hand, we can observe a growing influence of
conceptual modelling in the move of general IS development which is specifically addressing early phase
design issues. Here, conceptual modelling solves communicational problems which emerge when for
instance IT personnel and business personnel work together, mostly having distinct educational and
professional backgrounds as well as using distinct domain languages. Thus, the aim of this paper is to
provide the foundation of a Data Warehouse development methodology in form of a process reference
model which is based on a conceptual modelling approach.
A broad variety of methods and procedural or phase
models aims at supporting the Data Warehouse
development process (Poe 1996, Hammergren 1996,
Hackney 1997, Devlin 1997, Inmon & Imhoff &
Sousa 1998, Golfarelli & Rizzi 1999). Nevertheless,
the lack of epistemological funding of research
methods and methodologies is apparent and
extensively discussed in the IS discipline (cp. for
example Hirschheim & Klein & Lyytinen 1995,
Keen 1980, Mingers 2001). Therefore, the aim of
this paper is do provide a process reference model, a
methodology, for Data Warehouse development
under special consideration of its theoretical-
epistemological fundaments.
We now specifically analyzed the consensus-
oriented approach on conceptual modeling
(Niehaves & Klose & Knackstedt & Becker, 2005)
which is characterized by an interpretivist position,
which is mainly colored by the critical linguistic
approach of (Kamlah & Lorenzen 1973). The
information models developed contain formalized
linguistic statements to be tested for validity in
combination with additional (empirical) research
methods. This is done through members of a
linguistic community in order to obtain consensus.
Therefore, elements of the semantic theory of truth
(Tarski 1944) and the consensus theory of truth are
considered and used.
In Section 2 we introduce the consensus oriented
approach. In Section 3 we specify certain elements
of the consensus-oriented approach and
operationalize them in the form of a research process
model. By this means, we are able to elucidate the
consensus-oriented approach to a Data Warehouse
development methodology in particular. We
conclude with a summary and an outlook in Section
The consensus oriented approach as a underlying
paradigm of our reference process model of data
warehouse development is related with the
assumption that there is a real world existing
independently from human speech and thinking
processes. Thus, we assume the ontological realism.
The approach aims to create a linguistic
community. Linguistic communities can be created
through the (re)construction of an ortho-language.
First parts of the language can be formed by the
alignment of individual (real world) objects to
nominators. In the context of IS-development
Knackstedt R., Klose K., Niehaves B. and Becker J. (2005).
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 499-505
DOI: 10.5220/0002534104990505
important nominators are terms such as ‘customer
Meier’, ‘product 4711’ etc. Based on nominators,
predicators (in our context e. g. ‘customer’ and
‘product’) are introduced in order to expose and
communicate similarities of individual objects.
Language has in this case immense impact on a
subject’s perceptional processes. It defines the very
basic perception and differentiation system. Shared
language means shared conceptualizations about the
real world among the members of a particular
linguistic community.
In the move of the consensus-oriented approach
to Data Warehouse development, two distinct
languages come into play: particular conceptual
modelling languages as well as natural languages.
Following Tarski (Tarski 1944) the creation of the
linguistic community takes place on two levels. On
the first level (here named T* object language)
conceptual model statements are expressed. For
instance, using Entity Relationship Models (ERM)
members of the linguistic community have to agree
upon the term ‘entity type’; in the case of Event
Driven Process Chains (EPC) they have to agree
upon the term ‘event’. Moreover, a distinction
between a) the language of model instances and b)
the language of the modeling method and technique
has to be made. On the second level (here named T*
meta language) members of the community have to
agree on a language which facilitates them to debate
about the truth and nontruth of the statements
represented in a model (including for instance
German or English). In the next step, the meta
language T* is used to discuss the modeling system
which is formulated on the first level using the T*
object language until a consensus of a group of
experts is achieved. Afterwards, the results can be
evaluated within the scope of the interpersonal
verification (Kamlah & Lorenzen 1973, Kamlah &
Lorenzen 1996). The formalized linguistic
statements contained in a conceptual model are
logically decomposed (deduction) until they are
accessible as elemental statements for purposes of
truth verification. This takes place by means of a
group of experts who obtain a consensus. The main
instruments are observation, experiments,
interviewing and the interpretation of texts (Kamlah
& Lorenzen 1996). The validity of statements in the
model can be confirmed, for example, in the case of
business specific models, with a single case. In case
of a pattern or reference model, however, the
generalized abstraction of different individual
verifications (induction) is necessary. Here, research
methods such as field experiments, surveys, case
studies or action research can be applied. Based on
these results a revision of the conceptual model is
Furthermore, the consensus oriented approach
results in the following epistemological positions:
Both empirical statements (Kamlah & Lorenzen
1996) and a priori statements can be made, which
may form the basis of conceptual models.
Conceptual modeling therefore derives its results via
theoretical reflection of the model contents, as well
as from the implementation of the model in
information systems and through observation.
Conceptual models are one form of artefacts of a
formalized language and can contain both empirical
and a priori knowledge. Both inductive and
deductive conclusions can be accessed firstly in the
context of the model creation and secondly in the
context of truth verification.
The consensus-oriented information modelling
approach provides a suitable foundation for a
methodological framework for the specification of
Data Warehouse systems. In terms of a process
reference model, types of tasks and procedure
recommendations can be identified. Thereby, the
creation of a linguistic community requires several
steps which have to be performed:
On the T* object language level a selection,
modification or development of an appropriate
modelling language is necessary. Moreover, it has to
be ensured that all project members have a common
understanding of the model constructs used. The
language-critique approach requires an explicit and
consistent introduction of terms. At the outset, basic
terms are introduced. Their definitions are based on
familiar terms which are shared in the understanding
of all project members. Normally, such generally
known terms can be taken from natural language.
Based on those terms, other terms are reconstructed
stepwise (explicitly introduced) unless it can be
assumed that their meaning is naturally known.
Thus, the terms received from the normalised
language are based on each other. But the derivation
of a term A must not use terms B which in turn
requires an introduction of term A. Hence, it is
necessary to ensure that terms are not reused in
different meanings.
For the multi-dimensional specification of Data
Warehouse requirements, a broad variety of
modelling techniques exists. The documentation of
multi-dimensional modelling techniques is often not
totally conforming to the demand of the language-
critique approach. For example, the introduction of
the modelling technique ADAPT of Bulos (Bulos
1996) (as many more do as well) emphasises a
detailed explanation of notation aspects in the form
of model examples and symbol lists. However, a
step-by-step introduction of the modelling technique
language constructs and their corresponding
documentation in the form of language-oriented
meta models is increasingly used and more and more
applied in the domain of Data Warehousing (for
example in the form of Entity-Relationship-Models
and additional textual explanations for the intended
semantics of the model elements).
An application of the language-critique approach
which can easily be transferred to the modelling of
Entity Relationship Models has been developed by
Wedekind (Wedekind, 1992) for example. Among
other things, he proposes the usage of the construct
operators subsumption, subordination, and
Using construct operators, core terms of Data
Warehousing can systematically be introduced. This
is presented by Holten by means of a modeling
technique for the specification of management views
in information warehouse projects (Holten 2003), for
example. Referring to his modelling technique the
following modelling constructs can be identified as
highly relevant in the context of Data Warehousing.
According to (Riebel 1979) Dimension Objects
are defined as all entities which can be related to a
decision in a business process (such as products or
customers). With respect to the analysing purpose,
Dimension Objects are assigned to Dimensions,
which arrange objects hierarchically (e.g. for the
analysis of product groups). Hierarchies can be
divided in several Hierarchy levels. Dimensions that
comprise identical Dimension Objects as leaf
elements are combined to Dimension Groupings.
Dimension Scopes represent the selection of several
Dimension Objects from a Dimension. A Dimension
Scope Combination can be regarded as a navigation
space of Dimension Objects that can be analysed by
the operations aggregation and disaggregation with
respect to the hierarchies of the combined
Dimension Scopes. Ratios define important aspects
of Dimension Objects such as invoice and payment
amount, gross margin, profitability, etc. and can be
organised in Ratio Systems, which define selected
relationships between Ratios in a mathematical or
business-logical sense. As business information can
not be expressed exclusively based on Ratios or
Dimension Objects, they are combined to
Information Objects. They describe the amount of
data (as combinations of Ratios and Dimension
Objects which is called Fact), a manager should
analyse by Dimensions and Ratio Systems.
A comparison of core terms used in the
modelling technique by Holten with terms used in
other modelling techniques (cp. Figure 1)
emphasises the necessity of the construction of a
language community (cp. Holten 2003, Holten &
Dreiling & Schmid 2002 [MetaMIS], Sapia &
Blaschka & Höfling & Dinter 1998, Hahn & Sapia
& Blaschka 2000 [ME/RM], Bulos 1996 [ADAPT],
Golfarelli & Maio & Rizzi 1998, Golfarelli & Rizzi
1999 [DFM]). On the one hand, the comparison
underlines that identical modelling constructs are
named in different ways (synonyms). On the other
hand, different modelling constructs are allocated
with idem terms (homonyms). Furthermore, several
modelling constructs remain generally unconsidered
or dissimilar considered (for example in
combination or in a multiple application of several
constructs) in the different modelling techniques.
In case of a missing general understanding of
terms used on instance level (for example the
product lines ‘food’ and ‘non-food’), an introduction
of these terms (in particular dimensions and ratios or
ratio systems) by means of the language-critique
approach is necessary as well. Synonyms and
homonyms especially occur with respect to ratios in
different enterprise departments (for example
turnover with or without taxes). For the construction
of language communities, glossaries and rules for
the determination of ratios (developed in consensus)
should be an integral element of a conceptual
modelling technique.
Moreover, the construction of a linguistic
community comprises an agreement on a language
which makes it possible to discuss about the new
developed or modified modelling language and its
model artefacts. Normally, a selection of a natural
language such as English, Spanish or German is
sufficient. Against the background of consensus-
oriented modelling, this language is called T* meta
language. In the context of Data Warehouse systems
specification, using T* meta language mainly aims
to achieve a consensus between project members
about necessary information needs.
Figure 1: Comparison of Data Warehouse Modelling
In the context of consensus building, consensus-
oriented modelling requires that project members
speak the same language. Moreover, they have to be
rational’ and ‘competent’ in the project domain (cp
in detail Kamlah & Lorenzen 1996).
In the context of Data Warehouse development,
especially a consensus about information needs is
required. Literature debates of information need
analysis are often based on a model including
several overlapping circles (cp. Figure 2(a)).
Following Szyperski (Szyperski 1980), the first
circle represents the amount of information which is
actually available in an organization. The second
circle represents the amount of information
objectively’ needed for decision making or task
performing. The third circle comprises the amount
of information a user considerssubjectively’ to be
relevant for his/her task. Within the third circle,
another circle exists which represents the amount of
information that is explicitly demanded and
articulated by a user. The model implicates that
information represented in the first circle
(information demanded by managers) and third
circle (the available information) should be modified
with respect to the “objective” information need (cp.
the two arrows in Figure 2(a)).
In the context of consensus-oriented modelling, a
discussion and representation of an ‘objective’
information need is inappropriate, since in Data
Warehouse projects exclusively information needs
articulated and identified by project members are
relevant. Following our presented epistemological
position, this information need has to be interpreted
as ‘subjective’. Thereby, a consensus about the
“subjective” information need is required. On the
one hand, different perceptions are based on
dissimilar individual experiences of project
members. On the other hand, they are particularly
based on the methodical foundation of the
formulated information needs. In this context,
amongst others, the following three approaches can
be distinguished (cp. Figure 2(b)):
End user involvement: The participation of end
users in the Data Warehouse development
process is indispensable. Their involvement
ensures the acceptance of the Data Warehouse
system through the consideration of individual
preferences and habits (for example the
understanding of ratios). Manager often the
information overload problem as they often
demand the total amount of data available which
is referred to a certain topic (Ackoff 1967). The
differentiation between identified and articulated
information needs made by Szysperski indicates
that not only methodical conditions for an
analysis of management information needs (for
example in the form of observations, interviews,
and surveys) have to be established.
Furthermore, suitable cultural conditions are
required which facilitate and motivate Data
Warehouse users to express their information
Methods for specifications of information needs:
The development of methods for a theoretically
funded identification and specification of
information needs has been a major issue in
information systems research in the last decades.
Several information requirements engineering
methods and approaches (especially in the MIS
domain) have been developed and evaluated
(Martin 1983, Munro & Davis 1977, Sethi &
Teng 1988, Rockart 1979). Nevertheless, the
problem of information requirements engineering
is considered to have deficiencies in theory.
Use of reference models: Reference models are
increasingly applied in the requirements
Figure 2: Information need analysis
Information supply
(currently available internal and
external data sources)
Subjective information need
(Information considered to be
relevant by the problem solver)
Information request
(actual information requested by
Objective information need
(problem-specific required
Information need formulated by
managers based on experiences
Information supply
(currently available internal and
external data sources)
Information need derived from
theoretical approaches (e.g. goal,
task or process analysis)
Information need derived from
reference models
Information need consolidated in
Extendable list of
information need
specifications by
„rational“ and
specification phase of Data Warehouse Projects.
Reference models provide useful means to
reduce the effort of information modelling,
because they can be used as a starting point for
the construction of project-specific models.
Thus, reference models provide best (or
common) practice solutions for information
modelling projects. By means of reference
models, opinions and experiences of external
experts concerning the design of Data
Warehouse systems are additionally and
indirectly involved. The efficiency and
effectivity of reference model applications can be
increased by the use of configurative reference
Accounting for different advantages and
disadvantages of the presented approaches, we
propose a multi-methodological procedure. By this
means, the acceptance of the Data Warehouse is
ensured through the involvement of end users.
Moreover, we overcome restrictions of an
information need which is exclusively formulated by
managers with an extended consideration of
methods for the specification of information needs.
Finally, reference models extend the (indirect)
participation of additional experts.
Following the consensus oriented approach, a
verification of the consensus consolidated is needed.
For an inter-personal verification in the context of
Data Warehouse projects, several artefacts
(distinguishable in granularity and implementation
orientation) can be taken into account. One
possibility is to decompose requirements
specification models in single statements.
Afterwards, these statements are verified (for
example relationships within dimensions of multi-
dimensional models or the consistence of ratio
Another possibility is to implement a complete
Data Warehouse system on basis of requirements
specification models. This realisation may result in
the fact that there is no longer a consensus about
certain parts of the requirements specification
models. Instead of a complete implementation of the
Data Warehouse system, usually prototypes are
developed and realised for verification purposes.
Based on the verification, it may be necessary to
modify the information modelling results. These
modifications may affect specific information
models but also selected modelling methods (for
example due to the fact that additional
representations are required). Therefore, the T*
object language is used again. For the discussion of
necessary modifications, the T* meta language is
We summarise our results concerning the
development of Data Warehouse systems based on
Process reference model for Dat
(1) Selection of
T* meta language
Documentation of T*
meta language
(2) (5)
Development of T*
object language
Meta models,
glossary of the
domain language
(3) (8) Definition of
project goals
Project goals
(4) (8)
Engineering of
information needs
Data Warehouse
specification based
on experiences
of managers
(4) (8) Appli-
cation of theroetical
approaches (goal,
tasks or process
Data Warehouse
specification based
on information need
(4) (8)
Identification and
adaptation of
reference models
Adaptaded reference
(6) Consolidation
of consensus with
respect to
information needs
Data Warehouse
(7) Verification of
Data Warehouse
Data Warehouse
Documentation of the
verification of single
Data Warehouse
the consenus-oriented approach in terms of a process
reference model in Figure 3. Ovals represent types
of tasks. Rectangles symbolise types of documents
which are assigned to tasks. Documents can be
outputs which are created by tasks or represent
inputs which are used for the accomplishment of
tasks. Thereby, types of tasks may comprise other
tasks. Numbers assigned to tasks illustrate the
(reading) order of the process reference model.
However, several tasks are cross-sectional since they
are passed through more than once.
The project goal needs to be defined in advance
to the requirements specification. It facilitates the
coordination of parallel information need
engineering. Moreover, it particularly describes the
context of management tasks that should be
supported by the Data Warehouse system. The
project goal definition itself is a model which is
represented in the T* object language which requires
the development of a language community as well.
Our framework emphasises the phase of
conceptual modelling of Data Warehouse projects.
Logical and physical aspects are only addressed in
the context of the interpersonal verification. Thus,
our approach has to be combined with other works,
which stress logical and physical aspects of Data
Warehouse development.
In comparison with other existing Data
Warehousing procedure model approaches, the
presented framework uses the consensus-oriented
approach of conceptual modelling as a specific
theoretical foundation. Instead of practical
argumentation or mathematical deductions, our
approach is based on the language-critique
philosophical work of Kamlah and Lorenzen. Their
work is used as a basis, because it comprehensively
addresses the communication problems between
Data Warehouse project members. Furthermore, our
approach emphasises the explication of its
underlying epistemological assumptions, which is
associated with the definition of the consensus-
oriented modelling approach (cp. Niehaves et al.,
2005; Niehaves, 2004).
Ackoff, R. L., 14 (1967), S. B-147 bis B-156., 1967.
Management Misinformation Systems. Management
Science, 14 (1), B 147-156.
Bulos, D., 1996. A New Dimension. OLAP Database
Design. Database Programming & Design, 9 (6), 33-
Devlin, B., 1997. Data Warehouse. From Architecture to
Implementation. Reading, UK et al.
Golfarelli, M., Maio, D. and Rizzi, S., 1998. The
Dimensional Fact Model – A Conceptual Model for
Data Warehouse. International Journal of Cooperative
Information Systems, 7 (2-3), 215-246.
Golfarelli, M. and Rizzi, S., 1999. Designing the Data
Warehouse: Key steps and crucial issues. Journal of
Computer Science and Information Management, 2
Hackney, D., 1997. Understanding and Implementing
Successful Data Marts. Reading, UK et al.
Hahn, K., Sapia, C. and Blaschka, M., 2000.
Automatically Generating OLAP Schemata from
Conceptual Graphical Models. In Proceedings of the
ACM Third International Workshop on Data
Warehousing and OLAP (DOLAP 2000), Washington
D. C., USA, 10. November 2000.
Hammergren, T., 1996. Data Warehousing. Building the
Corporate Knowledge Base. London, UK et al.
Hirschheim, R., Klein, H. and Lyytinen, K., 1995.
Information Systems Development and Data
Modeling: Conceptual and Philosophical
Foundations. Cambridge University Press.
Holten, R., 2003. Specification of Management Views in
Information Warehouse Projects. Information
Systems, 28 (7), 709-751.
Holten, R., Dreiling, A. and Schmid, B., 2002.
Management Report Engineering – A Swiss Re
Business Case. In From Data Warehouse to Corporate
Knowledge Center (E. Maur and R. Winter Ed.), 421-
437, Springer, Heidelberg, Germany et al.
Inmon, W. H., Imhoff, C. and Sousa, R., 1998. Corporate
Information Factory. New York/NY, U.S.A. et al.
Kamlah, W. and Lorenzen, P., 1973. Logical
Propaedeutic. Lanham/MD.
Kamlah, W. and Lorenzen, P., 1996. Logische
Propädeutik. Vorschule des vernünftigen Redens. 3
Edition. Stuttgart, Weimar.
Keen, P. G. W., 1980. MIS Research: Reference
Disciplines and a Cumulative Tradition. In
Proceedings of the First International Conference on
Information SystemsEd.), 9-18, Philadelphia/PA.
Martin, E. W., 1983. Information Needs of Top MIS
Managers. MIS Quarterly, 7 (3), 1-11.
Mingers, J., 2001. Combining IS research methods:
towards a pluralist methodology. Information Systems
Research, 12/2001/3, 240-259.
Munro, M. C. and Davis, G. B., 1977. Determing
Management Information Needs: A Comparision of
Methods. MIS Quarterly, 1 (2), 55-67.
Niehaves, B., 2004. A Framework for Analysing the
Epistemological Assumptions of Research Methods. In
Proceedings of the Innovation Through Information
Technology. 2004 IRMA International Conference
(M. Khosrow-Pour Ed.), 57-60, New Orleans/LA,
Niehaves, B., Klose, K., Knackstedt, R. and Becker, J.,
2005. Epistemological Perspectives on IS-
Development – A Consensus-Oriented Approach on
Conceptual Modeling. Accepted for the Second
International Workshop on Philosophy and
Informatics (WSPI 2005), Ulm, Germany.
Poe, V., 1996. Building a Data Warehouse for Decision
Support. New Jersey.
Riebel, P., 1979. Gestaltungsprobleme einer
zweckneutralen Grundrechnung. Zeitschrift für
betriebswirtschaftliche Forschung, 31 (1), 863-893.
Rockart, J. F., 1979. Chief Executives define their own
Data Needs. Harvard Business Review, 30 (Mar-Apr),
Sapia, C., Blaschka, M., Höfling, G. and Dinter, B., 1998.
Extending the E/R Model for the Multidimensional
Paradigm. In Proceedings of the International
Workshop on Data Warehouse and Data Mining
(DWDM’98), 105-116, Singapur, 19.-20. November
Sethi, V. and Teng, J. T. C., 1988. Choice of Information
Requirements Analysis Method: An Integrated
Approach. INFOR, 26 (1), 1-16.
Szyperski, N., 1980. Informationsbedarf. In
Handwörterbuch der Organisation (E. Grochla Ed.),
904-913, 2. Ed. Stuttgart.
Tarski, A., 1944. The Semantic Concept of Truth and the
foundation of semantics. Philosophy and
Phenomenological Research, 4/1944, 341-375.
Wedekind, H., 1992. Datenbanksysteme I. Eine
konstruktive Einführung in die Datenverarbeitung in
Wirtschaft und Verwaltung. 2. Ed. Mannheim et al.