Information Driven Modeling:

The Integration of Enterprise Ontology and Enterprise

Architecture for the Mature BI

Jorge Cordeiro Duarte and Mamede Lima-Marques

Centre for Research on Architecture of Information

University of Brasilia – UnB, Brazil

Abstract. There is a huge interest in the area of engineering ontologies for a

very wide range of applications. In modern enterprises and their business

process management and decision making, modeling and Business Intelligence

(BI) play a significant role. Information Technology (IT) contribution to

business strategies and decisions has been questioned. The problem is not with

IT, but with business complexity in modern times. The challenge of enterprise

intelligence is a mission for the whole enterprise, and depends on intensive

collaboration among organizational units. Enterprise BI requires new

approaches, methods and tools focused on integration. BI must be part of

Enterprise Architecture (EA). This paper offers a new insight on Business

Intelligence. It proposes an extension of Archimate methodology in order to

integrate EA and BI efforts with enterprise ontology to provide mature

enterprise decisions.

1 Introduction

Business Intelligence (BI) is neither a product, nor technology nor methodology [24].

BI is a powerful management approach that can deliver knowledge, efficiency, better

decisions, and profit to organizations.

BI initiatives can improve business performance in many ways, including better

targeted products, improved customer relationship management, and greater

operational efficiency. It can also result in reengineering of business processes.

When BI first came on the scene, it promised a lot but often failed to deliver.

Despite the potential, success is not necessarily guaranteed. In fact, surveys revealed

that more than 60 percent of companies with BI initiatives had only limited success in

meeting user expectations. Studies also indicated managerial or business customer

resistance to data warehousing for several reasons including the fear of losing control,

lack of technical skills, costs, and uncertainty about their utility.

As the deployment of a BI system requires a multimillion-dollar investment and

other resource mobilization, a well conducted project must take place. IT contribution

to business strategies and decisions has been questioned but the problem is not IT,

The problem is business complexity in modern times. Business Intelligence is one of

Cordeiro Duarte J. and Lima-Marques M. (2009).

Information Driven Modeling: The Integration of Enterprise Ontology and Enterprise Architecture for the Mature BI.

In Proceedings of the 1st International Workshop on Ontology for e-Technologies OET 2009, pages 52-65

DOI: 10.5220/0002229300520065

 SciTePress

the most complex initiatives a company can have. It deals with many enterprise

domains, each one with its own complexity.

First of all, BI projects are very complex. The challenge is how to control this

complexity. BI initiative must have approaches, methods and tools that deal

objectively with complexity. The challenge for enterprise is linking information to the

organization strategic goals, culture, and strengths while tracking the technological

and administrative complexity.

In spite of the increasing importance of BI, there is little interest in academic

research about this subject. This lack of research limits our understanding of how BI

projects can succeed. There are many open question related with BI: How we can take

control of complexity in BI initiatives? How projects can be successfully conduced?

We can distinguish two major challenges in a BI project, each one with its own

complexity: a business and a technical challenge. This paper makes an exploratory

study to enhance general understanding of BI Projects, but the main focus is to

explore business variables related to BI success, specifically at the enterprise

modeling stage. The paper proposes that good enterprise models can reduce the

complexity of business side of BI projects. It explores the influence of information

and ontology in enterprise modeling and proposes a new approach of modeling the

enterprise based on information modeling. This proposal extends Archimate language,

a new approach on Enterprise Architecture (EA). The contribution of this paper is a

proposal that reduces drastically the business side of BI projects by presenting a

methodology that supports the modeling and understanding of business.

This paper is organized as follows: session 2 discusses the BI benefits and

challenges. Session 3 looks at complexity in business and technology. Session 4

explores information modeling and ontologies: its evolution, contribution and

challenges. Session 5 studies the evolution and challenges of enterprise architecture.

Session 6 resumes the relation of BI with enterprise wide modeling. Session 7 makes

a proposal of extending the Archimate language in order to support information and

ontology approaches. Session 8 shows an application of this approach being

conducted in a insurance company, Finally, session 9 presents a conclusion and

propose future works.

2 Business Intelligence Concepts and Challenges

BI is neither a product nor a system. It is an architecture and a collection of integrated

operational as well as decision-support applications and databases that provide the

business community easy access to business data [24].

There is much to say about BI concepts, finality, methods and technology. For

those interested in business and technical side of BI we recommend Larissa Moss´

[13] and Steve Williams´ [24] books. We highlight some considerations made by the

authors:

 BI presents many benefits. It can increase sales, reduce costs and increase

profits.

 BI requires preconditions for success. There are Business preconditions, like

strategic alignment, process engineering and change management, and

technical preconditions like BI technical development and BI project

management.

 BI requires a methodology with many steps, like business analyses, data base

design and implementation and ETL design and implementation.

 Producing reliable information is the ultimate goal of BI. The ocean of data

created with the advances of science and technologies call for integration of

data coming from heterogeneous sources that are diverse in their purposes.

 BI is a collaborative project and needs the participation of application lead

developer, business representative, data Administrator, data quality analyst,

database administrator, ETL lead developer, meta data administrator, project

manager and subject matter expert.

 BI presents a great rate of failure and users dissatisfaction.

 In order to reach success, BI projects must have some essential abilities: (1)

ability to align and govern strategic alignment, business-IT partnership and

BI portfolio management; (2) ability to leverage cultures like information

and analytics use, continuous process improvement and decision process

engineering and (3) ability to deliver technologies like DW and BI software.

Concluding, Enterprises are complex, technology is complex and BI is a complex

project. IT projects comprises business and technological sides. Rate of failures are

high. Business and IT must collaborate to BI success.

3 BI and Enterprise Maturity

BI is an initiative of great proportions. Taking its characteristics, perhaps BI can be

considered the enterprise initiative that depends most on the integration between

business and IT. BI project has few chance of success when we do not have enterprise

and IT maturity and when the relation between both is not also mature.

Enterprise maturity is a subject much discussed is literature with a well

established proposal standard like CMMI [4]. Maturity is related with the capacity of

coordinating continuously improving results. Maturity is provided by repeatable,

well-defined and managed programs.

IT maturity also presents standards, like COBIT [5] and ITIL [11]. COBIT is

related with IT governance and consists on leadership, organizational structures and

processes that ensure that the enterprise’s IT sustains and extends the organization’s

strategies and objectives. ITIL is a library of good practices for IT services.

Chen [1] proposes a framework for undestanding Business-IT Alignment. He

identifies the alignment approaches: alignment via architecture, alignment via

governance, and alignment via communications. These three approaches are

integrated in the BITAM-SOA Framework depicted in Figure 1.

Fig. 1. BITAM-SOA Service Engineering Schematic [2].

Cumps, Viaene and Dedene [7] identified the following approaches IT must have in

order to contribute to Business-IT Alignment:

 Change management

 Strategic IT planning

 Risk management

 Enterprise architecture management

 Performance management

 Program/portfolio management

 Service-level management

 Project management

Concluding, enterprise maturity, IT maturity, and Business-IT alignment are

preconditions of success in BI projects because it is almost impossible to succeed

without IT governance and good practices and well defined and managed enterprise

wide programs.

4 BI, Information Modeling and Ontologies

BI is related with enterprise intelligence and so it is related with data, information,

knowledge and ontologies in enterprises. Enterprise Information modeling and

ontologies are attracting attention of researches [20].

Data modeling is one of the most mature methodologies in information systems (IS).

It has well established standards and tools [2]. It is well accepted for the community.

Data Administrator is a profession well recognized. Enterprises has not acquired the

same standard in business information yet.

Despite many researches made by famous researchers like Davenport [8],

Choo[4] and Everdeen [11], information modeling and management in business side

have not been well understood yet and little practice has been presented to the subject.

Recent contributions to information and knowledge modeling have been done by

enterprise ontology research.

Ontology is defined as a specification of a conceptualization [10]. Ontologies

provide a number of useful features for intelligent systems, as well as for knowledge

representation in general and for the knowledge engineering process [9].

Ontologies can vary in expressivity and formalism, like figure 2 shows, and have

some specific languages, like OWL and specific tools like Protege [9].

Fig. 2. Expressivity and formalization on ontologies [9].

There are many potential applications of ontologies like collaboration, interoperation,

education and modeling. Many researchers found that ontologies can help

organizations in reducing complexity, modeling strategies [15] (figure 3) and other

enterprise elements, like information systems technology [20] (figure 4).

Fig. 3. Enterprise ontologie for enterprise performance [15].

Fig.4. Ontologies and information systems [20].

Concluding, ontologies can be of great value for BI projects helping understanding

the business by defining its main elements and showing the relation among then.

5 BI and Enterprise Architecture

BI is related with business and technology. So it demands models from business and

from technology and models showing the relation between both. EA is the discipline

that studies these kind of enterprise wide modeling.

We can find many researches about Enterprise Architecture (EA). For those

interested on the foundations, methods, languages and tools of EA we recommend

Lankhorst´ [17], Ross´ [21] and Weske´ [23] books. Follows a resume of EA from

these authors:

 EA defines a firm’s needs for standardized tasks, job roles, systems,

infrastructure, and data in core business processes.

 EA is a coherent whole of principles, methods, and models that are used in

the design of an enterprise’s organizational structure, business processes,

information systems, and infrastructure.

 EA identifies the main components of an organization and how they work

together to achieve defined business objectives.

 EA models components include personnel, business processes, technology,

financial information and other resources.

 Business and IT communities need to share a common understanding of the

principles underlying enterprise modeling.

 While administration professionals tend to consider information technology

as a subordinate aspect for experts to handle, by contrast computer scientists

often consider business goals and organizational regulations as terms that do

not deserve much attention.

 EA include high-level business aspects like business goals, strategies, and

value chains and also technical models, taking into account the different

stakeholders involved as figure 5 shows.

 EA can use a variety of process languages, including Workflow nets, Event-

driven Process Chains, Yet Another Workflow Language, and the Business

Process Modeling Notation.

 EA can have a specific language like Archimate (Figure 6).

 The lack of a pattern difficult the maturity of the process.

Fig. 5. EA models [18].

Fig. 6. Archimate Language [17].

EA provides enterprise wide models of business, information, systems and

technology. It is an essential part of business side of BI. So, we can conclude that

having an EA program, we have enterprise knowledge and BI is likely to succeed.

6 A Mature BI Business Side

The main purpose of BI is to provide support to enterprise decisions. BI presents

business and technological sides. In previous sections we analyzed many business

sides like enterprise maturity, IT maturity and enterprise wide modeling efforts like

EA and knowledge modeling.

We found some related works at literature linking EA with BEA [22] [23] but

they are not information driven and do not use Ontologies. Considering the state of

the art of BI, data modeling, information modeling, knowledge modeling, enterprise

modeling, ontologies and EA we find they have much in common and must

collaborate. We can summarize our analyses in the following remarks:

 Business side of BI must have strong sources of Knowledge about enterprise

objects.

 Information and knowledge models approaches are evolving in enterprise

side and can provide sources of knowledge about enterprise objects.

 Enterprise wide modeling is evolving, with the maturity of EA methodology

and tools that can help business people understand the enterprise objects.

 Enterprise mature approaches can provide the environment BI needs to

succeed in its business side.

 IT mature approaches can provide the technical environment BI needs to

succeed.

 Business-IT alignment mature approaches can provide the global

environment BI needs to succeed.

Enterprise wide models are evolving and we need studies analyzing the impact of it in

organizations. All the remarks above are related with BI projects, based on theory and

must be proved with case studies. We suppose organizations do not have sufficient

practice for a large survey so we must validate this theory in a case study. We have

intention to make this study but first we need an integrated approach to BI which is

given in the next section.

7 Information Driven Modeling: EA for BI with Information

Modeling and Ontologies

Enterprise Modeling needs a language to integrate enterprise objects. We have many

modeling languages for specific purposes. When talking about modeling enterprise

objects we can find ontologies languages like OWL. EA approach tries to consolidate

different models in enterprise frameworks. A specifically language for EA called

Archimate has recently appeared.

We have found Archimate a good methodology for EA that can be also a good

contribution to BI. But we propose to extend Archimate for information and business

strategies modeling.

Figure one shows our proposal of Archimate extension. It presents the following

elements that Archimate does not consider:

 Information activity;

 Control;

 Decision;

 Mission, goals and strategies

This proposal is based on information. What drives analyses is information activity.

We identify an information object and then its activities. It brings MER to business,

identifying information entities, information activities and establishing information

relations with all business objects.

The benefit of this approach is that is easier for people to understand. Figure 7

shows the proposal of Archimate extension.

Fig. 7. Archimate extended.

8 A Case Study

We are applying the approach described in a big insurance company in Brazil that has

a BI project in one of its units. This Unit comprises more ten million clients, twenty

million contracts and two millions transactions monthly. The BI project is very

complex in business and technical side.

In business side the complexity was reduced by adoption or the Archimate

methodology extended with the information approach. This method helps to identify

information and its relations ant also the relations of each information activity to

strategies, controls and operations.

With the use of Archimate (by a Vision stencil) people can easily understand also

the relation of information with data in the application and in the datawarehouse.

With protégé people can see the information defined and its relations.

Figure 8 shows a information object, its activities and each relation with operations,

controls and decisions. Figure 9 shows protege and information relations. Figure 10

shows information relations with data.

Fig. 8. Information and business relations.

Fig. 9. Information and its relations.

Fig. 10. Information and its relation with data.

The project is not concluded but results can be seen in adopting this approach. Objects

relations became clear. Documentation is better. Modeling activities are faster.

Overall time of activities is lower.

9 Conclusions

In this paper we analyzed BI projects, their business side and their relation with

enterprise wide modeling efforts like EA and enterprise ontologies. We explored the

benefits of the integration of BI with enterprise wide modeling efforts. We also

explored the relation of BI with enterprise maturity and IT maturity take the

information first.

Based in this theoretical analyses we conclude that enterprise wide modeling

reduce BI complexity. We also conclude that enterprise maturity and IT maturity

approaches helps BI projects to succeed.

We have proposed to model the enterprise using Archimate language in order to

provide BI with an integrated view of the organization based on information activity.

This approach was tested in a real application and proved its efficiency.

This study contributes to researchers and practitioners with a broader vision of

BI. It proposes an approach based on the integration of BI with enterprise modeling to

reduce the complexity. Certainly EA and ontologies with Archimate extended and

Protege help to reduce BI complexity and contribute to its success. BI is a field

research with many open questions. Our proposal is to continue research in tools. EA

tools must be better integrated better. Ontologies tools need to be integrated with ea

tools.

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