Knowledge Management Framework using Enterprise Architecture

and Business Intelligence

Oswaldo Moscoso-Zea

, Sergio Luján-Mora

, Cesar Esquetini Cáceres

and Norman Schweimanns

Faculty of Engineering, Equinoctial Technological University, Rumipamba y Burgeois, Quito, Ecuador

Department of Software and Computing Systems, University of Alicante, San Vicente del Raspeig, Alicante, Spain

Faculty of Systems Engineering, National Polytechnic School, Ladrón de Guevara E11-253, Quito, Ecuador

innoCampus, Technische Universität Berlin, Straße des 17. Juni, Berlin, Germany

Keywords: Knowledge Management, Enterprise Architecture, Business Intelligence.

Abstract: Knowledge Management (KM) has emerged as a tool which enables the efficient creation, use, distribution

and transfer of knowledge in organizations. In the core of KM there are three dimensions of analysis:

people, processes and technology. KM Frameworks presented in the past have had a strong theoretical

background, but they have not been well explained in terms of how to implement them in practice to cover

all KM dimensions. In this paper, a novel KM framework is presented. This framework was designed as a

practical guide to implement KM endeavours in organizations. To accomplish our research objective, two

management practices are incorporated in the framework: Enterprise Architecture and Business Intelligence.

Enterprise Architecture allows companies to visualize organizational objects in different areas (business,

applications and technology) through the use of models. Moreover, Business Intelligence technologies as

data warehouses, data mining and visualization can enable the capture, transfer and the creation of new and

purposeful knowledge. This work is intended to be a good resource for companies or individuals that want

to implement a KM initiative.

1 INTRODUCTION

Knowledge Management (KM) has emerged as a

discipline which enables the efficient creation, use,

distribution and transfer of knowledge in

organizations (Campbell, 2006). Innovations in

science and technology have led to the emergence of

intensively information-based organizations. These

organizations need to transform this information into

knowledge to secure competitiveness and improve

decision making.

The core dimensions that need to be examined in

a KM project are: people, processes and technology

(Edwards, 2011). Knowledge derived from these

dimensions should be analyzed and stored using

different information repositories. A Knowledge

Management Framework (KMF) enables

organizations to conduct and implement KM

initiatives. KMFs are the foundation for developing

information infrastructure and information systems

to manage knowledge properly. Karemente, Aduwo,

Mugejjera, and Lubega (2009), describes different

KMFs; however, none of these integrates and

analyzes the three knowledge dimensions as a whole

and are difficult to use in practice.

As a result of a university research project, a

KMF was developed. This framework details how a

KM implementation should be done in order to

capture explicit and implicit knowledge derived

from the three knowledge dimensions previously

mentioned. Moreover, two management practices

are included in the framework to accomplish our

objective: Enterprise Architecture (EA) and

Business Intelligence (BI).

EA is defined as “a coherent set of principles,

methods and models that are used in the design,

realization and maintenance of an enterprise’s

business architecture, organizational structure,

information architecture and technology architecture

with respect to the corporate strategy” (Lankhorst,

2009). The purpose of EA is to optimize the

processes of an organization into a cohesive

environment that is open to change and supportive to

the business strategy (The Open Group, 2011).

On the other hand, BI is “the conversion of

organizations resources to knowledge. It is the data

mining and the integration of information from

corporate data warehouses to produce large amounts

244

Moscoso-Zea, O., Luján-Mora, S., Cáceres, C. and Schweimanns, N.

Knowledge Management Framework using Enterprise Architecture and Business Intelligence.

In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 1, pages 244-249

ISBN: 978-989-758-187-8

of information needed for effective decision making

process and for planning strategically to achieve a

competitive advantage in its industry” (Barakat, Al-

Zu’bi, and Al-Zegaier, 2013). In this paper, a KMF

supported by EA and BI is presented. The

framework was designed as a practical guide to

implement KM in organizations.

The rest of the paper is structured as follows:

Section 2 presents the theoretical background; Section

3 explains how the KM framework was developed;

and Section 4 provides conclusions of the work.

2 BACKGROUND

The research objective of this work is to present a KM

framework which can be used in practice to capture,

use and transfer knowledge. In this section, the

literature research made for this work is presented.

2.1 Knowledge Management

Knowledge is one of the key resources that can

strengthen the positioning of an organization

(Curado, 2006). In order to sustain a competitive

advantage, a resource should be valuable, rare and

imperfectly imitable (Wernerfelt, 1984).

Organizational knowledge meets these

characteristics; therefore, it must be captured and

managed appropriately. Knowledge can be defined

as experience, facts, know-how, processes, beliefs,

that increase an organizational or individual’s

capability (Karemente et al., 2009).

KM is “a process of identifying, capturing and

leveraging the collective knowledge in an

organization to help the organization compete”

(Alavi and Leidner, 2001). Moreover, KM is

“concerned with the exploitation and development

of the knowledge assets of an organization with a

view to furthering the organization’s objectives”

(Rowley, 2000). The reasons for KM include staff

turnover, information overload, increasing need of

expert staff, improved decision making and

digitalization of organizational knowledge.

From the definitions, two important tasks are

necessary to implement KM. Firstly, it is necessary

to develop the technological infrastructure for

facilitating knowledge capturing and sharing; and

secondly, to establish mechanisms and procedures

for retaining knowledge from people and processes.

In order to accomplish these objectives, researchers

have developed KMFs with different approaches.

Nevertheless, a generally accepted framework has

not been established (Heisig, 2009).

2.2 Enterprise Architecture

Enterprise Architecture (EA) supports in describing

the current state (as-is situation) of an organization

and proposes the best alternative solutions for the

desired outcome (to-be situation). EA can be seen as

a map that incorporates methods and techniques to

create architectures in different layers of an

organization. US Federal Enterprise Architecture

Management Office defines EA as “a management

practice to maximize the contribution of an agency’s

resources, IT investments, and system development

activities to achieve its performance goals” (FEA

Program Management Office, 2007).

EA addresses the need to manage increasing

complexity and deal with continuous change by

providing a holistic view of the organization,

including their organizational components and their

relations. EA is often viewed as a management

practice that supports digitalization of knowledge to

improve the performance of organizations (de Vries

and van Rensburg, 2008).

Figure 1 shows a pyramid with the

organizational architecture layers as: people,

business, applications and technology. The circular

arrows sequentially depict the process for

implementing EA in an organization: getting the

stakeholders involved, establishing management and

control, defining the architecture process, the

creation of the as-is and to-be scenario, development

of a sequencing plan, using and maintaining the EA.

Figure 1: Enterprise Architecture Based on: (Tucker and

Debrosse, 2003).

2.3 Business Intelligence

The term Business Intelligence (BI) was coined and

became popular in the 1990s (Chen, Chiang, and

Storey, 2012). According to (Gartner Inc., 2013), BI

builds upon a set of tools and applications that

Knowledge Management Framework using Enterprise Architecture and Business Intelligence

245

enable the analysis of vast amounts of information

(Big Data) to improve decision making and

performance of organizations. To accomplish this

objective, decision makers require having access to

all organization´s data, to analyze the business, its

requirements and its trends.

The main technology in a BI project is a data

warehouse. The data warehouse is a data repository

which is populated from the integration of different

operational data sources maintained in different

units of the organization. An efficient analysis of

data requires powerful analysis tools. Two main

types of analysis tools exist: Online Analytical

Processing (OLAP) and Data mining tools. OLAP

tools use multidimensional views of aggregate data

to provide access to corporate information for the

purpose of improving decision making. Data mining

uses software techniques for finding hidden patterns

and trends in large databases to support strategic

decisions (Connolly and Begg, 2005).

3 PROPOSAL OF KNOWLEDGE

MANAGEMENT FRAMEWORK

As mentioned previously, in the core of KM there

are three dimensions of analysis: technology, people

and processes. Hence, a successful implementation

of a KM initiative in organizations must take into

account mechanisms to effective capture, use and

transfer knowledge acquired from the three stated

dimensions. The design of the framework is

intended to put order in the KM process. Moreover,

a practical framework can support managers in the

creation, capture, digitalization of knowledge and

decision making.

3.1 Technology

The first dimension of analysis in a KM process is

technology. Technology is defined by

(BusinessDictionary, 2015) as “The purposeful

application of information in the design, production,

and utilization of goods and services, and in the

organization of human activities”. In this paper,

technology is referred to as objects used by humans

(tools, software, hardware, machines) for KM.

Information repositories for EA and databases are

the core technologies that support KM.

EA repositories store the objects and processes

modeled from the different architectural layers in an

organization. On the other hand, databases store data

generated from different applications. There are two

main sources in which information can be found

Online Transaction Processing (OLTP) and Online

Analytical Processing (OLAP). The source of data

for OLTP databases is operational data. The main

purpose of OLTP databases is to run and to control

fundamental business data with a highly normalized

design. Data for OLAP databases is integrated and

loaded from different operational sources into a

multidimensional database namely a data

warehouse. The purpose of OLAP databases is to

improve business analysis and decision making. In a

KM implementation, information can be extracted

and processed from these two repositories. Many

methods and techniques can be used to extract useful

knowledge from databases. Some of the most used

techniques in a knowledge discovery process are

data mining and machine learning.

3.2 People

People dimension is one of the pillars for the

exploration and exploitation of knowledge in

organizations. According to Churchman (1975)

“knowledge resides in the user and not in the

collection of data”. Thus, a mechanism should be

designed within the proposed framework in order to

capture and to transfer knowledge from people in

organizations. It is important to note that the staff

turnover rate in the United States in 2014 was 11%

in all industries (Compensation Force, 2014). This is

an indicator that strategies should be implemented to

maintain and transfer knowledge from these and

other groups of employees that are leaving

organizations.

The cost of training of new employees without

the efficient capture of people’s knowledge can

increase exponentially. According to the Association

for Talent Development, the average of spending on

employee training within US is around $1208 per

year and per employee (Association for Talent

Development, 2015). We believe that this value can

be decreased if we plan staff turnover accordingly

and establish mechanisms for the capture of

knowledge with existing technology, for example by

using learning management systems (Sanchez-

Gordon, Calle-Jiménez, and Luján-Mora, 2015).

3.3 Processes

Processes are described by (Edwards, 2011) as “the

way people, organizations and even technology

actually do things”. The importance of processes in

KM initiatives are described in different papers (Bou

and Sauquet, 2004) (Newell, Robertson, Scarbrough,

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

246

and Swan, 2002). Identification and digitalization of

the core processes of an organization is an important

step in a KM initiative. It facilitates the transfer of

knowledge of tasks performed by staff since

processes are divided into activities and procedures

are created for easier interpretation. Processes are

modeled normally in a Business Process

Management (BPM) software or in an EA tool. The

process models and architectures created in this

software become an essential part of the knowledge

base of the organization.

3.4 Proposed Knowledge Management

Framework

A successful implementation of a KM initiative

greatly depends on a well-defined method that

supports the creation, capturing, use, distribution and

transfer of knowledge. Organizational knowledge is

created from different interdependent objects in

different domains: strategy, product, services,

information technology, applications, business

processes and people (Lankhorst, 2009). Explicit

and implicit knowledge can be derived from these

domains. Explicit knowledge is knowledge that can

be formulated, documented and reproduced.

Implicit knowledge also known as tacit knowledge is

knowledge that is difficult to document or formulate,

and is normally associated with human knowledge.

Thus, the proposed framework intends to

comprehensively create mechanisms to guide the

KM process to capture knowledge from all the

organizational dimensions. This framework was

conceived as a part of a research project in a private

university. The main goal of the research project is

the design of a knowledge management framework

(KMF) and the development of a web application

prototype supported by databases, data mining and

business intelligence tools for the planning process

in the university.

One of the main objectives of the university is to

position itself as a research and teaching institution,

through the production, management and transfer of

new knowledge based on institutional research lines.

One of the projects implemented in the past year was

the establishment of an institutional diagnosis in

order to create a new model of corporate

governance.

After analyzing the raised processes and the

outputs of this project a need was identified. The

identified need was to create a KMF for the planning

area of the university to ensure the efficient

management of knowledge and knowledge related

activities. The purpose of the framework is to

support planning, implementation and control of

knowledge related projects and programs required

for the effective management of intellectual capital.

Before the design of the framework started, a

series of interviews was realized with different

stakeholders in order to discover their knowledge

requirements and to structure the framework. The

importance of the three dimensions of knowledge

was confirmed in the interviews. Moreover, certain

activities to include in the framework were

identified. Some of these activities were: discovering

of knowledge in existing databases, digitalization of

existing processes and the definition of mechanisms

to convert tacit knowledge from different people in

the organization into explicit knowledge. The

novelty of the framework resides in the use of EA

and BI to cover all the stated dimensions. Figure 2

depicts the designed framework.

The component in the right presents an analysis

on how explicit knowledge is produced by using BI

and EA tools. This box receives implicit knowledge

as an input. The implicit knowledge is produced by

people and processes in the organization. The

knowledge discovery process inside the box has the

following steps: analysis of existing databases and

files, extraction of useful information,

transformation to the target database format and

loading. This process known as ETL (Extraction,

Transformation, Loading) prepares data into a

customizable format, cleans data with errors and

eliminates duplicates. The purpose of this step is to

load quality data into the target database in order to

improve the analysis processes.

A data warehouse is the best target database for

analysis. A data warehouse conceptual design

consists of a set of dimension tables, fact tables and

their relations. The populated data warehouse can be

analyzed using BI and data mining tools to discover

knowledge. Data mining and machine learning are

popular methodologies for the knowledge discovery

process. There are different methods and techniques

that can be used.

On the other hand, digitalization of knowledge

can be captured in an EA tool. An EA tool supports

the creation of architectures to translate implicit

knowledge into models which describe

organizational structures (people), business

processes, applications and technological

infrastructure.

Most EA tools are based on the Archimate

standard (Schekkerman, 2011). Archimate language

allows the design of architectures in different

domains and the creation of relations between the

different objects of the organization. The

Knowledge Management Framework using Enterprise Architecture and Business Intelligence

247

Figure 2: Proposed Knowledge Management Framework.

digitalization process includes an analysis of the

different units and departments of the organization.

Interviews must be realized with all the staff in order

to document and model the different activities and

processes realized in all architecture layers. The

modeling of EA projects enables the capture and

collection of implicit knowledge from the employees

and the transformation to explicit knowledge in the

forms of views and viewpoints of the architectures.

EA and BI are the main methodologies of creation

of explicit knowledge. It is important to present

explicit knowledge in an easy and understandable

way. The framework suggests the presentation of

knowledge by using a KM system which can be

developed in a web environment. The results of the BI

and EA process can be visualized and analyzed in this

KM system. The output of the component in the right

is explicit knowledge in the form of reports and

dashboards that are presented to be used by people in

all levels of the organization and can support in the

design or redesign of new and existing processes. The

explicit knowledge is the main input of the box in the

left of the framework. Explicit knowledge can support

and enhance decision making activities and can

increase knowledge levels of the people in the

organization. It supports as well the transfer of

knowledge to new employees. As seen in the

framework the KM process is a cycle in which

knowledge is produced in a daily basis.

4 CONCLUSIONS

KM is a practice that organizations are incorporating

to improve the creation, use, distribution and transfer

of knowledge. The implementation of KM must be

guided by a KMF. Many KMFs exist in the

literature. However, these frameworks do not

present practical mechanisms to gather and analyze

all the knowledge dimensions: people, processes and

technology.

The use of BI and EA tools bridges the gap of

capturing all the knowledge dimensions. On the one

hand, BI allows the transformation of simple

information in valuable knowledge by applying data

mining methods and techniques. On the other hand,

EA supports the digitalization of implicit knowledge

from people and processes by creating architectures in

different domains. These architectures facilitate the

transfer and distribution of knowledge to different

levels of people in the organization. Some benefits of

using this framework are: reduced training costs of

staff turnover, improved decision making processes

and the creation of a knowledge repository.

REFERENCES

Alavi, M., & Leidner, D. E., 2001. Knowledge

Management and Knowledge Management Systems.

MIS Quarterly, 25(1), 107–136.

Association for Talent Development, 2015. 2014 State of

the Industry Report: Spending on Employee Training.

Retrieved November 20, 2015, from

https://goo.gl/MpccrZ.

Barakat, S., Al-Zu’bi, H. A., & Al-Zegaier, H., 2013. The

role of business intelligence in knowledge sharing.

European Journal of Business & Management, 5(2),

237–243.

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

248

Bou, E., & Sauquet, A., 2004. Reflecting on quality

practices through KM theory. Knowledge

Management Research & Practice, 35–47.

BusinessDictionary, 2015. Technology Definition.

Retrieved November 20, 2015, from

http://goo.gl/a266MR.

Campbell, H. M., 2006. The role of organizational

knowledge management strategies in the quest for

business intelligence. Engineering Management

Conference, 2006 IEEE International, 231–236.

Chen, H., Chiang, R. H. L., & Storey, V. C., 2012.

Business Intelligence and Analytics: From Big Data

To Big Impact. Mis Quarterly, 36(4), 1165–1188.

Churchman, W., 1975. The design of Inquiring Systems:

Basic Concepts of Systems and Organizations.

American Educational Research Journal, 12-1, 94–96.

Compensation Force, 2014. 2014 Turnover Rates by

Industry. Retrieved November 30, 2015, from

http://goo.gl/hGEuFg.

Connolly, T., & Begg, C., 2005. Database Systems. Essex,

England: Pearson Education Limited.

Curado, C., 2006. The knowledge-based view of the firm.

Instituto Superior de Economia E Gestao, (1959), 18.

de Vries, M., & van Rensburg, A., 2008. Enterprise

Architecture - New business value perspectives.

Southafrican Journal of Industrial Engeneering, 19,

1–16.

Edwards, J., 2011. A Process View of Knowledge

Management : It Ain ’ t What you do , it ’ s the way

That you do it. Journal of Knowledge Management,

9(4), 297–306.

FEA Program Management Office, 2007. FEA Practice

Guidance, (November). Retrieved from

https://goo.gl/QIqI1V.

Gartner Inc., 2013. Gartner Business intelligence.

Retrieved November 9, 2015, from

http://goo.gl/LmJRG3.

Heisig, P., 2009. Harmonization of Knowledge Magagement-

comparing 160 KM frameworks around the globe.

Journal of Knowledge Management, 13(4), 4–31.

Karemente, K., Aduwo, J. R., Mugejjera, E., & Lubega, J.,

2009. Knowledge Management Frameworks.

Strengthening the Role of ICT in Development, 35–57.

Lankhorst, M., 2009. Enterprise Architecture at Work

Modelling Communication and Analysis (2nd ed.).

Berlin Heidelberg: Springer-Verlag.

Newell, S., Robertson, M., Scarbrough, H., & Swan, J.,

2002. Managing Knowledge Work and Innovation

(2nd ed.). Palgrave macmillan.

Rowley, J., 2000. From learning organisation to

knowledge entrepreneur. Journal of Knowledge

Management, 4(1), 7–15.

Sanchez-Gordon, S., Calle-Jiménez, T., & Luján-Mora, S.,

(2015). Relevance of MOOCs for Training of Public

Sector Employees. In 14th International Conference

on IT Based Higher Education and Training (pp. 1–5).

Caparica.

Schekkerman, J., 2011. Enterprise Architecture Tool

Selection Guide. Institute for Enterprise Architecture

Developments.

The Open Group, 2011. TOGAF ® Version 9.1. Retrieved

from http://goo.gl/djuv15.

Tucker, R., & Debrosse, D., 2003. Enterprise Architecture

Roadmap for Modernization. Enterprise

Modernization Issue, 7(2).

Wernerfelt, B., 1984. A Resource-based View of the Firm.

Strategic Management Journal, 5, 171–180.

Knowledge Management Framework using Enterprise Architecture and Business Intelligence

249