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Integrating Knowledge Management and Business Intelligence Processes for Empowering Government Business Organizations

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Emergence of information technologies has transformed the way business marketing is done and how business enterprises are managing the resources and information. Trend of globalization has induced the fierce competitiveness among business enterprises within domestic and international markets. The major quest for the technologies is not limited to strategic value of an organization but also empower the organization work context by utilizing its resources. Knowledge management has emerged as the latest techno-management trend for improving the work process and creating value for business organization operations. Knowledge management offers various techno-managerial implications to business organization for strategic development. However, there are scarce evidences on business intelligence, strategic management decision support related to business organization adopting these offerings. Major objective of Business Intelligence is to extract the information and find the hidden knowledge from all sources of data. Business Intelligence offers to make decision for enhancement of any organizations goal. The broad overview of research articulates an understanding of government based organizations about the adoption of Knowledge management based Business Intelligence solutions and its challenges. Data mining is playing a key role in Knowledge Management based systems for business organizations and its implication lies in the implementation of data mining algorithm for exploring the huge amount of data, which determines the pure knowledge. Majority of the government organizational data remains in either unstructured form such as raw form of data (i.e. internal or external document) or with its employees in the form of experience. Knowledge management process deals with extraction of both tacit and explicit knowledge of organization for improving the performance of organization. However Business Intelligence (BI) on the other hand gained its importance with constant enhancement in technologies and tools for extracting the hidden knowledge and patterns. Hence it can be argued that both Business Intelligence and Knowledge Management are complimentary to each other for extracting and managing the knowledge. Thus it's very imperative for government organizations to have an integration of both Knowledge Management (KM) and Business Intelligence (BI) processes for enhancing the performance of the organization with respect to make organization decision for competitive environment and utilizing the organizational tacit knowledge. The paper focuses on how BI and KM integration affect the government business organization while discussing its implementation challenges. The paper tries to analyze the correlation between Knowledge Management and Business Intelligence and exploring a road map for data mining based framework for Knowledge Management focusing government based organizations. Current situation of knowledge management strategic decision making and role of knowledge must need to be addressed before proposing any framework for government organization. Paper provides a detailed extensive literature review which aims to describe the basics of Knowledge Management based systems and integrating Business Intelligence with Knowledge Management. Study will draw a distinction between individual and organizational knowledge as well as whether knowledge is playing a key role in strategic development or not?
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International Journal of Computer Applications (0975 8887)
Volume 114 No. 5, March 2015
36
Integrating Knowledge Management and Business
Intelligence Processes for Empowering Government
Business Organizations
Herison Surbakti
Information System Dept. Universitas Respati Yogyakarta
Yogyakarta Indonesia
ABSTRACT
Emergence of information technologies has transformed the
way business marketing is done and how business enterprises
are managing the resources and information. Trend of
globalization has induced the fierce competitiveness among
business enterprises within domestic and international
markets. The major quest for the technologies is not limited to
strategic value of an organization but also empower the
organization work context by utilizing its resources.
Knowledge management has emerged as the latest techno-
management trend for improving the work process and
creating value for business organization operations.
Knowledge management offers various techno-managerial
implications to business organization for strategic
development. However, there are scarce evidences on
business intelligence, strategic management decision support
related to business organization adopting these offerings.
Major objective of Business Intelligence is to extract the
information and find the hidden knowledge from all sources
of data. Business Intelligence offers to make decision for
enhancement of any organizations goal. The broad overview
of research articulates an understanding of government based
organizations about the adoption of Knowledge management
based Business Intelligence solutions and its challenges. Data
mining is playing a key role in Knowledge Management
based systems for business organizations and its implication
lies in the implementation of data mining algorithm for
exploring the huge amount of data, which determines the pure
knowledge.
Majority of the government organizational data remains in
either unstructured form such as raw form of data (i.e. internal
or external document) or with its employees in the form of
experience. Knowledge management process deals with
extraction of both tacit and explicit knowledge of organization
for improving the performance of organization. However
Business Intelligence (BI) on the other hand gained its
importance with constant enhancement in technologies and
tools for extracting the hidden knowledge and patterns. Hence
it can be argued that both Business Intelligence and
Knowledge Management are complimentary to each other for
extracting and managing the knowledge. Thus it’s very
imperative for government organizations to have an
integration of both Knowledge Management (KM) and
Business Intelligence (BI) processes for enhancing the
performance of the organization with respect to make
organization decision for competitive environment and
utilizing the organizational tacit knowledge.
The paper focuses on how BI and KM integration affect the
government business organization while discussing its
implementation challenges. The paper tries to analyze the
correlation between Knowledge Management and Business
Intelligence and exploring a road map for data mining based
framework for Knowledge Management focusing government
based organizations. Current situation of knowledge
management strategic decision making and role of knowledge
must need to be addressed before proposing any framework
for government organization. Paper provides a detailed
extensive literature review which aims to describe the basics
of Knowledge Management based systems and integrating
Business Intelligence with Knowledge Management. Study
will draw a distinction between individual and organizational
knowledge as well as whether knowledge is playing a key role
in strategic development or not?
Keywords
Knowledge Management (KM), Business Intelligence (BI),
Data Mining, Knowledge, Data Warehouse
1. INTRODUCTION
In the era of knowledge and technical innovation, it has been
widely accepted that intangible assets of any business
organization will be key to its success. Knowledge is
supposed to be most important asset of any business
organization, which has the largest influence on
competitiveness, strategic development, and growth. Every
organization has individual and organizational knowledge
either in the form of raw data or information. Raw data or
information retains within organization in the form of implicit
knowledge and with limited resources. These information or
raw data needs to be processed to acquire knowledge through
the use of knowledge management & data mining approach.
Further knowledge can be made accessible to all through
knowledge management process. Several environmental
factors around which each business operates are:
globalization, fierce competition, changes in organization
structure, growth of information technology, and advent of
knowledge management process [1]. Thus, emergence of
Knowledge Management discipline has changed the direction
of business strategic planning.
In the context of business organization, knowledge
management is used to acquire the knowledge and
experiences it for strategic development. Reuse of preciously
acquired knowledge can be beneficial for preventing past
failure and used as a guideline for fixing recurrent issues. It
has been claimed that in business enterprise the knowledge
not only embedded to document and repositories but also with
enterprise routine, process, and practices [2].
Thus, knowledge is recognizing itself as one of the most
important assets of any organization. Knowledge is acquired
through the processing of available data of organization using
data mining approaches. Data mining is a tool for processing
the data to find out the relationship within the data that can be
beneficial for the user. Data mining has the potential to use is
as a powerful tool for the business intelligence but yet not
fully recognized [3]. With the proliferation of the new
technologies data mining has experienced an exponential
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growth and became an integral part of Knowledge
Management system. Data mining algorithms are applied to
explore the underlying data of business organization and after
processing it determines the effectiveness of knowledge.
The major focus of this paper is role of Knowledge
Management and Business Intelligence Processes for
government based organization. Government based
organizations means functional government agencies, various
departments who perform public services. The paper aims to
find how government organization managers adopt both KM
and BI processes in public sector. Study aims to find out the
interrelationship between Knowledge Management and
Business Intelligence, and utilize it for strategic development
and decision making.
In government based organization, there are extensive amount
of data which is used within organization for business policy
management, organization decision making, and growth &
development of organization. Since environment changes in
any organization drastically, thus any change in the data also
reflects the change in the system. The change can be related to
various categories such as:
Change in the quantity of data, it means with the growth
in any organization amount of data will be increased
substantially
With the increased amount of data, the correlation
between data also changes it means the relationship
between application system also changed
Therefore these organization need to understand the data,
process and mine the data to acquire knowledge from the
large amount of data and extract intact and practical
knowledge from random, vague, incomplete, and huge
amount of data. This extracted knowledge can be utilized for
decision making business intelligence. Primarily in the
Knowledge management process, knowledge discovery
process needs to apply data mining algorithms. Varieties of
algorithms are available in data mining such as genetic
algorithm, decision making, neural network, and fuzzy logic.
This paper is based on government based organizations, in
which Knowledge Management process needs to implement
for strategic development and decision making, and
organizational development for the social and economical
growth of the organization as well as improve its
competitiveness in the era of globalization. Research aims to
monitor, explore the evolution of business intelligence and
Knowledge management implementation as a means to
improve the work practice of business organization.
The fundamental purpose of the paper is to discuss the need of
integrating KM and BI for exploiting structured &
unstructured raw data, implicit information of the organization
and its challenges. This will helpful for creating an integrated
knowledge based decision support system framework for
government based organization which integrates both
Business Intelligence and Knowledge management.
2. LITERATURE REVIEW
Most researchers and practitioners agreed on the practical
implication of knowledge as one of the important assets of
any organization. Knowledge Management and Business
Intelligence are the two major areas of researchers concern.
Knowledge management is a tool for empowering the
knowledge within the organization [18], and useful for
decision making. However, Business Intelligence has affected
the business world the most for transforming the raw data into
knowledge. This can be used for prediction analysis. A dearth
research has been performed to explore Knowledge
Management, Business Intelligence and its applicability
within various application domains.
Authors have analyzed that Business intelligence is the broad
categorization of applications of processing large amount of
data for any organization to make prediction analysis [19],
[20]. Operations such as OLAP (online analytical processing),
data warehousing, data reporting, and business rule modeling
are used by Business Intelligence. However, Knowledge
Management is the process of knowledge acquiring and
creation, knowledge sharing and dissemination and
knowledge application. Authors have suggested that both
Business Intelligence and Knowledge management are
influenced by environment of the organization. The success
ration of Knowledge Management is directly proportional to
employee attitude [18], [21], [22]. Thus, there is a need of
common platform for the organization where both employer
and employee can share the knowledge.
In [23], author has proposed a scheme for transforming
Knowledge Management into Business Intelligence. Author
has also briefed certain parameters for implementing them to
organization for a common workflow. However, the new or
new solution cannot be added directly for the adoption
purpose. Tacit knowledge plays a vital role in all the phases of
any newly innovative process and implementation of tacit
Knowledge Management and can be helpful for handling new
problems.
Author has proposed a memory model for linking individual
knowledge to knowledge managements. However, the
practical implication of this model is very weak [23]. In [24],
author has analyzed the outcome of knowledge management
process over business intelligence and organizational
performance with the help of influential variable. Therefore, it
can be concluded that any knowledge management based
system is a handy tool for achieving completive advantages.
Some of the attempts have been done by the authors to
integrate knowledge management for real time Business
Intelligence and its benefits [25]. In [26], author has focused
on tacit knowledge and explained it as a vital component for
organization. However, management of tacit knowledge is a
challenging task. Thus, there is the need of a common
framework where tacit knowledge can be categorized into
various degrees.
In [27], author has stated that both knowledge management
and business intelligences are different from each other in
terms of common foundation. Thus the interrelation between
knowledge management and business intelligence needs to be
explored. Simply an insight can be concluded that business
intelligence is used for transforming data to knowledge,
whereas Knowledge Management can be used as a tool for
knowledge acquisition, knowledge sharing and to create new
knowledge.
In [28], author has investigated the advantages and
disadvantages of Knowledge Management, Business
Intelligence and further proposed KMBI framework for the
integration of Knowledge Management and Business
Intelligence. This framework consist of three different layers
namely data, presentation, and function integration.
With reference to various contexts, articles, paper reviewed,
and application of Knowledge management it is analyzed that
data mining is widely used toll for Knowledge Management
and Business Intelligence both. Since both Knowledge
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Management and Business Intelligence are correlated and can
be integrated for the better performance of an organization.
Both are complimentary of each other, thus both can result in
more effectiveness for government based business
organization.
3. KNOWLEDGE MANAGEMENT
3.1 Knowledge
Knowledge is defined as the mix frame of facts, expectation,
skills, and combination of relevant information collected
through experience, study, and reasoning, for enhancing the
ability of decision making and evaluating the right context [4]
[5]. However data, information and knowledge are the key
terms which are the set member of knowledge management
and may used interchangeably. Several arguments are made
by the researchers about these terms, and defined as:
Data can refer to unprocessed, unstructured collections of
random facts; Information refers to structured and processed
data having some sense to the user, whereas Knowledge refers
to the most refined and highly useful data for decision making
and problem solving.
Various researchers have proposed several classification
methods for classifying the knowledge. The classification of
knowledge is helpful to the organizations for processing and
managing their various available knowledge resources. Most
widely accepted classification of knowledge is: Explicit and
Tacit knowledge.
Explicit knowledge contains the knowledge, which has been
already processed in the form of visual, text, diagrams, tables,
manuals, and specific documents. Acquisition of explicit
knowledge is easy, since it is in the form of table, manuals,
and document; so as easy to manage too. In case of
government based business organization explicit knowledge
may contain such as business specification, product
specification, contracts, and customer data [4] [6].
However, tacit knowledge refers to most valuable knowledge
as it is in the form of experience, skills, and communications.
It remains as understanding of people and expressed in the
form of language [4] [6] [7]. Tacit knowledge is very
beneficial to find best solutions and managing the
organization on the basis of previously known knowledge.
The only issue with tacit knowledge is, it cannot be articulated
as it remains in the form of experience and skills. Since, tacit
knowledge is personal, as it is retained in mind in the form of
experience, skill and perceptions, hence very tough to
manage, share and articulate it [7]. In case of government
based organization tacit knowledge may include work such as
process, project dealing, problem solving, and expert
opinions.
Some authors have proposed that some part of the tacit
knowledge can be acquired and converted into explicit
knowledge. Several authors have proposed an hierarchy to
have an understanding of data, information and knowledge
types as shown in figure 1 [4] [6] [8].
Figure 1: Relationship among Data, Information,
Knowledge, and Wisdom
Except these, several attempts has been done by the authors
for categorizing knowledge such as codified knowledge,
common knowledge, social knowledge and embodied
knowledge.
3.2 Knowledge Management
Knowledge management is an important part of any process
management system and highly applicable to business
organizations. It is an integral part for linking knowledge to
business process management. Several authors have proposed
various definitions for knowledge management. Knowledge
management can be defined as the paradigm which is used for
exploring knowledge resource, exploiting and sharing all
knowledge (explicit, tacit) resource for enhancing the
performance of any organization [9] [10]. Knowledge
management provides framework and tools for knowledge
acquisition, sharing, and creating knowledge for aiding end
users for problem solving, and decision making.
Knowledge management system can be defined as the system
for managing knowledge within organization for creating,
acquiring, and sharing of knowledge. The basic idea of a
knowledge management system is to provide newly created
knowledge to the users for solving new problems and reusing
of previous knowledge. Further researchers have provided
different views on knowledge management system such as
incorporating strategic decisions, processes and user
component with knowledge management [9].
Knowledge management system has proved their success
within organization. With the successful acquisition, creation,
and sharing of knowledge; organizations are improving the
performance, organizations learning for gaining competitive
advantage. On the basis of previous available research on
knowledge management system over business organization,
the benefit can be summarized as in figure 2 [4] [10].
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Figure 2: Overview of KMS (Knowledge Management
System)
Researchers and practitioners have discussed challenges and
barriers that may cause the performance degradation using
knowledge management system. Hence, there is a need of
more planned, structured and coherent approach for utilizing
knowledge in more efficient way so that it can prove more
beneficial for government organizations.
3.3 Knowledge Management Models
Limited processes, procedures and structured approach lead
the development of more structured approach for managing
knowledge resources within organization. To overcome these
challenges knowledge management modeling is used for
creating and managing the knowledge. Models provide an
easy presentation of a real system using its main features.
Modeling is helpful for providing structured methods for
understanding, implementing and evaluating knowledge
management system. However, researchers argued over the
advantage and disadvantage of these models while
implementing for the organization.
3.3.1 Generic Knowledge Management Models
Many generic models, methods have been developed by the
researchers for enhancing knowledge management. Some of
the well known models are SECI Model [10], knowledge map
[11], Ontology based knowledge management [12], Activity
based knowledge management [13], and knowledge
management models [14].
3.3.1.1 SECI Model
SECI model is used for creating knowledge using four
different modes known as Socialization, Internalization,
Combination and Externalization as represented in figure 3
[10] .
Figure 3: SECI Model
Author has proposed knowledge creation can be a nonstop
process, to recreate company in it by creating new knowledge.
Creation of a new knowledge is a specialized activity where
each user in an organization act as a knowledge worker and it
begins with each individual.
Socialization is the process for acquiring and sharing
experiences or tacit knowledge through observation and
conversations. Externalization is used for transforming tacit
knowledge into explicit knowledge. Combination process is
used for combing various available explicit knowledge
sources for creating new knowledge. Internalization is the
process for developing skills, experiences by continuous
learning and by applying existing explicit knowledge to form
tacit knowledge.
3.3.1.2 Activity based Knowledge Management
Model
Activity based model is proposed especially for activities of
construction projects [13]. Author has proposed that
information and knowledge from all sources are classified and
stored as activity unit, hence named activity based modeling.
Major aim of this model is reusing the knowledge and easy
knowledge acquisition. Integrated definition function (IDF)
modeling method is used for defining the model of knowledge
management. Activity based knowledge divides the process
into top-level and sub-level phase. Top-level phases are such
as knowledge acquisition; knowledge extraction, knowledge
storage, knowledge sharing, and knowledge update [13].
However sub-level phases consists the division of top-level
phases into small tasks.
3.3.1.3 Knowledge Map
Using activity based knowledge management model as the
base mode. This model is used for acquiring and re-presenting
the knowledge known as knowledge map [11]. Knowledge
map is the schematic graphical representation which tells what
knowledge resource is available and missing in knowledge
management. It’s an easy way for user to find the required
knowledge. This model uses previous knowledge map of
similar activity to form new one. Authors have proposed
knowledge management architecture for describing
components of knowledge management. This architecture
consists of four different layers as Interface layer, Access
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layer, Application layer, and database layer [11] [13] as
shown in figure 4.
Figure 4: Knowledge Management Model
3.3.1.4 Tacit-Explicit Knowledge Continuum
A model has been proposed for transforming tacit knowledge
into explicit knowledge in organization; there should be clear
understanding about dynamic nature of knowledge [15].
Author has also argued that knowledge can also be harmful
for organizations, if it is invalid, misleading, discouraging,
and unsatisfactory for organization. Knowledge is dynamic in
nature as it changes with time. Author has suggested in place
of extract knowledge from users, it should be more productive
like creating a knowledge culture for sharing knowledge
through face to face communication.
However various other knowledge management models such
as IMPaKT, e-COGNOS are proposed by authors depending
on different organizations. Reviewing the literature and
analysis helps in identifying the characteristics, their
relationships, and components for knowledge management
modeling related to government based organizations.
4. BUSINESS INTELLIGENCE
4.1 Business Intelligence
Knowledge management plays vital role in strategic decision
making within organization. On the other hand Business
Intelligence has gained significant importance in business
world due to wide range of technologic advancement.
Business Intelligence offers wide range of solutions such as
advertising business, improving economy, and customer
support. Business Intelligence can be defined as the
combination of data analytical tools for gathering and
effective use of organization information to improve business
management. Some authors have argued business intelligence
as online decision making process. Authors have suggested
that Business Intelligence as a set of components such as data
warehouse, data mining, OLAP, and decision support system
[16] [17]. Figure 5 depicts the input and processing of
Business Intelligence process.
Figure 5: Inputs and Outputs of Business Intelligence
Basic description of Business Intelligence components are
described in next subsection.
4.1.1 Data Warehouse
Data warehouse is a system used for data repository and data
analysis. Data warehouse stores the data into repositories,
where it is processed and organized for strategic decisions.
Information in the warehouse is stored in the form of Meta
data. Metadata helps the user to understand what and where
the data is available, and how to access it and when to use the
data. Data warehouse system are categorized such as data
mart, OLAP (Online analytical processing), and OLTP
(Online transaction processing).
4.1.1.1 Data Mart
Data marts are the simpler form of data warehouse which is
especially focused for single task such as for any individual
departments. Data mart uses the data either from internal
operations or from external data source.
4.1.1.2 OLAP (Online Analytical Processing)
OLAP is a multidimensional model which support roll-up,
and drill operations. OLAP are low volume transactions, but
consists of complex queries. OLAP applications are widely
accepted by data mining techniques.
4.1.2 Data Mining
Data mining is the process of mining the available data to
discover the hidden patterns, trends, and correlation among
the data. Huge amount of data is stored into data warehouse
and processed using data mining tools and techniques.
4.1.2.1 ETL (Extraction, Transfer, and Load)
ETL process is the group of three different processes for
extracting and loading into database. Extraction process refers
to data extraction from various sources including internal and
external sources. Transfer process refers to data cleaning for
correlating the inconsistent, missing, and invalid data sets.
Finally load refers to load the cleaned data into data
warehouse.
5. INTEGRATION OF BUSINESS
INTELLIGENCE AND KNOWLEDGE
MANAGEMENT
Business Intelligence and Knowledge Management both have
shown significant improvement for organizational
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performance. However, both Business Intelligence and
Knowledge Management are different but used for knowledge
discovery and decision making. There are various arguments
by researcher for discussing whether Business Intelligence is
part of Knowledge Management or Knowledge Management
is part of Business Intelligence. Simply Knowledge
management deals with both tacit and explicit knowledge,
whereas Business Intelligence deals with only explicit
knowledge [22]. In [28] [29], authors have proposed that
integration of Knowledge management and Business
Intelligence will broaden the research area of knowledge,
while improving the intelligence. Business Intelligence
process converts data into information then into knowledge
which finally used for meeting user requirements. However,
major emphasis of Knowledge management is on knowledge
and improves the utilization process. In [27], author has
proposed an architecture which is an extension of business
intelligence model. In [16], author has argued that no
knowledge framework which can support analytical
knowledge generated by business intelligence. Author has
proposed a research direction and framework for intelligent
Knowledge management. In [30], author has proposed
architecture for integrating Business intelligence and
knowledge management with three different layers as
interface, data level integration, and system level integration.
The authors have proposed their views on integration of
Business intelligence and knowledge management for
achieving more intelligence [27] [29].
The major benefits of integrated framework are:
It ensures to provide highest quality of services to
individual users in the global market.
Tacit knowledge (user experiences, user skills,
understanding) can be useful for business intelligence.
It provides both understanding of business context,
context interpretation for user benefits.
6. BASIC FRAMEWORK FOR KM & BI
INTEGRATION IN GOVERNMENT
ORGANIZATIONS
Management of knowledge is of much importance for
government for dealing with the challenges of knowledge
economy. Government organizations facing many challenges
such as: administrative, executive, and fierce competitiveness
for achieving organizational goal. Today government
organizations need knowledge work and knowledge workers
for creating & sharing the knowledge to enhance interpersonal
and organizational skills. Knowledge management and
business intelligence has the potential to strengthen the
effectiveness and competitiveness of government sectors.
Thus there is a need of having a combined integrated
framework of Business Intelligence and Knowledge
management for achieving this goal.
Several researchers has proposed a variety of models,
integrated framework, and perspectives explained in literature
review section in detail. Most important factors or elements
while defining the framework for the government
organization are: users (organizational and general user),
processes, and technology.
On the basis of available literature review, knowledge
management model; initial scope of the integrated framework
has been proposed for the government organizations. The
initial scope represents the possible outcomes and features
which are expected from the BI, KM integrated framework.
Figure 6 represents the possible features that are expected
from the integrated framework. On the basis of expected
outcomes of integrated framework, it can consist of several
layers.
Government organizations have both external and internal
data. Internal data contains data of organization either
structured or unstructured, whereas external data contains user
data. Except it experience, skill, and understanding of
organization people can also taken into account. The
integrated framework needs to handle both explicit and tacit
knowledge. Thus, a layer can be used of handling the
knowledge source. Further unstructured and structured data
needs to be stored for further processing. After processing the
unstructured and structured data knowledge can be extracted
as KDD (Knowledge Discovery in Database) or BI processes.
Finally extracted knowledge can be visualized and integrated
with decision support system.
On analyzing the expected outcomes from framework it can
be argued that there can be possible interaction between
Knowledge Management processes and Business Intelligence
Processes. The major aspect of the integrated framework is
inclusion of both explicit and tacit knowledge which benefits
organizational decision goals as well as work skills of
employee using tacit knowledge. Extractions of tacit
knowledge and its utilization is really a challenging task for
any organization and have many positive influences.
7. CONCLUSION & FUTURE WORK
Potentially this research would assist in the development of a
integrated model for Business Intelligence and Knowledge
Management which helps government based organization for
strategic development. This will be helpful for evaluating
their existing knowledge management system. It can work as
guidance to the organization to utilize their knowledge base at
both individual and organizational level. Business Intelligence
can be useful for Knowledge Management in a collaborated
form. This expended integration will improve the
effectiveness of knowledge at individual and organizational
level.
Implementation of integrated KM and BI framework for
government organization can improve quality and efficiency
of public services. In general it can be argued that government
sectors are mostly lagging behind the private sectors. Hence in
this fierce competition in the era of globalization government
organization needs to adopt these Knowledge Management
and Business Intelligence processes to empower themselves.
In this paper a base framework with its possible expected
outcomes and detailed literature survey has been proposed.
Further research aims to develop new knowledge management
framework integration with business intelligence that enables
the strategic development, decision making, and resources
utilization with in government organization. This model will
be helpful for categorizing the knowledge and use of
knowledge for predictive analysis. This proposed model will
be evaluated and validated with current integration models of
knowledge management and business intelligence.
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Figure 6: Basic integrated framework for BI and KM in Government Organizations
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IJCATM : www.ijcaonline.org
... Decision Support System (DSS) is the association of man and machine for provision of authentic and useful information in order to support management in decision making.OLAP is one of the important components of BI used in process. OLAP has several other traditional forms [11]. Some of them are classification, sequential patterns, regression and link analysis.Thus, BI process is a relevant approach to analysis knowledge data that required a proper process to capture and analysis tacit knowledge. ...
... Models are helpful in categorizing data warehouse sections and establishing data mining techniques. "People Centric BI and KM" model states that employees are the main tool in both BI and KM which communicate information to users or customer [11]. A good BI model must encompass organizational functions and operations [2]. ...
... A model is established in [10], stating steps of interchange between BI and KM covering context of decision making. Knowledge worker have strong collaboration and cooperation with IT tools for better performance and socialization, where BI framework is presented which lead to better integration of KM processes with tools of BI [11]. [10]He discussed with logic that how data mining, a tool of BI and KM can lead to better results. ...
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The purpose of this paper is to identify and analyze integration of Knowledge Management (KM) and Business Intelligence (BI) in order to achieve competitive edge in context of intellectual capital. Methodology includes review of literatures and analyzes the interviews data from managers in corporate sector and models established by different authors. BI technologies have strong association with process of KM for attaining competitive advantage. KM have strong influence from human and social factors and turn them to the most valuable assets with efficient system run under BI tactics and technologies. However, the term of predictive analytics is based on the field of BI. Extracting tacit knowledge is a big challenge to be used as a new source for BI to use in analyzing. The advanced approach of the analytic methods that address the diversity of data corpus – structured and unstructured – required a cognitive approach to provide estimative results and to yield actionable descriptive, predictive and prescriptive results. This is a big challenge nowadays, and this paper aims to elaborate detail in this initial work.
... Por otro lado la teoría organizacional de gestión de conocimiento o KBV por sus siglas en inglés "Knowledge Based View" es el proceso de difusión o esparcimiento del conocimiento en la empresa. Este proceso consta de cinco etapas: adquisición, creación, compartir, diseminación y aplicación del conocimiento (Surbakti, 2015). ...
... Existen diferentes herramientas o componentes claves de análisis de datos que son utilizados por los procesos de inteligencia de negocios, estos son: las base de datos corporativas "data warehouse", minería de datos o "data mining", OLAP por sus siglas en inglés, Online Analytical Processing, ETL por sus siglas en inglés Extract, Transform and Load y DSS también por sus siglas en inglés Decision Support System (Langseth y Vivatrat, 2003;Curko, Vuksic y Loncar, 2009;Surbakti, 2015;Muhammad, Ibrtahim, Bhatti, y Waqas, 2014). Estos componentes se encuentran en la Figura 9.1. ...
... Debido a que este conocimiento es considerado como uno de los valores intangibles más importante de la empresa, es imperativo la integración de estos dos procesos. Esto garantizará un continuo adquisición del conocimiento (Surbakti, 2015) La gestión del conocimiento en la innovación 3. ¿Existe alguna relación entre la teoría organizacional de gestión del conocimiento e innovación? ...
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El emprendimiento pareciera ser una estrategia para efectos del desarrollo económico de los países, están las esperanzas focalizadas en el emprendimiento empresarial como en el social, ya que implica convertir una idea por inverosímil que parezca en una innovación exitosa. El emprendedor regularmente observa oportunidades donde los demás sólo ven problemas o pocas opciones, esto implica utilizar cualidades, habilidades, creatividad, innovación, tenacidad y tomar el riesgo que ello implica. En algunas ocasiones es necesario contar con conocimiento del área específica para crear un negocio propio. Quedaron atrás los prototipos de la edad, la experiencia y el género, que limitaban la decisión del emprendedor para seleccionar el giro de su negocio.
... creaci?n, compartir, diseminaci?n y aplicaci?n del conocimiento (Surbakti, 2015). Tanto los procesos de inte- ligencia de negocios como de gesti?n del conocimiento son influenciados directamente por el ambiente de la organizaci?n (Wilson, 2002;Paiva, Goncalo, 2008;Herschel y Jones, 2005). ...
... Existen diferentes herramientas o componentes claves de an?lisis de datos que son utilizados por los procesos de inteligencia de negocios, estos son: las base de datos corporativas "data warehouse", miner?a de da- tos o "data mining", OLAP por sus siglas en ingl?s, Online Analytical Processing, ETL por sus siglas en ingl?s Extract, Transform and Load y DSS tambi?n por sus siglas en ingl?s Decision Support System (Langseth y Vivatrat, 2003;Curko, Vuksic y Loncar, 2009;Surbakti, 2015;Muhammad, Ibrtahim, Bhatti, y Waqas, 2014). Estos componentes se encuentran en la Figura 2. ...
... un continuo adquisici?n del cono- cimiento (Surbakti, 2015). ...
Book
El emprendimiento y su comprensión es algo que ha fascinado mucho a los investigadores, puesto que se plantean las preguntas ¿cómo surge el emprendimiento? ¿Qué motiva a emprender? ¿Por qué el emprendedor ve oportunidades donde no son visibles? ¿Por qué el emprendedor no tiene miedo al fracaso? ¿Por qué el éxito es un objetivo de vida? Estas y otras preguntas se han tratado de responder en el transcurso de los años. Como una contribución al análisis del emprendimiento y del emprendedor, se desarrolló este libro denominado Perspectivas del Emprendimiento en América Latina y el Caribe, en el cual se analiza la temática desde diferentes perspectivas. En este libro participan 4 Instituciones de Educación Superior Mexicanas, Universidad Autónoma de Querétaro, Universidad Juárez del Estado de Durango, Instituto Tecnológico de Estudios Superiores de Monterrey y la Universidad Tecnológica de Aguascalientes; 2 de Ecuador, Universidad Técnica Luis Vargas Torres y Universidad Tecnológica Empresarial de Guayaquil; 1 de Colombia, la Universidad EAFIT de Medellín y finalmente la Universidad del Turabo de Puerto Rico. Los países Colombia, Ecuador, Puerto Rico y México, se unen para efectuar un análisis del emprendimiento y el impacto que genera en diversos ámbitos.
... A segunda refere-se ao uso dos dados para geração de informação e conhecimento e a terceira relaciona-se ao uso da informação como subsídio para tomada de decisão. Essas perspectivas podem melhorar a qualidade e a eficiência de serviços públicos (SURBAKTI, 2015) e, de forma mais específica, dos vários campos da gestão do cuidado em saúde (FRAGA et al., 2017). ...
... A base desse planejamento, além das diretrizes do Planejamento Estratégico da SMSA, tem sido o uso das informações disponíveis para subsídio na tomada de decisão e no reconhecimento das oportunidades de negócio. O uso da informação nos processos de gestão é uma prática que mantém relação com a Inteligência de Negócios, com a gestão da informação (GI) e a gestão do conhecimento (GC) (ABEDI, 2013;WAUYO;OMOL;OKUMU, 2017;SURBAKTI, 2015). ...
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A literatura atual aponta que a implantação da tecnologia de Business Intelligence (BI) ou Inteligência de Negócios é um dos principais fatores de aumento do desempenho nas organizações. Essa pesquisa consiste em um estudo de caso cujo objetivo é descrever o processo de implantação da tecnologia de BI na Diretoria Regional de Saúde Norte de Belo Horizonte (DRESN). Em relação à metodologia da pesquisa, foram identificadas como amostra, a partir do universo de todos os processos gerenciais desta diretoria, iniciativas que tiveram investimento da tecnologia de BI ao longo dos últimos anos. A coleta de dados foi realizada por meio de dados secundários não sensíveis. A análise de todo o material foi feita por meio da técnica de análise de conteúdo. Como produto desse trabalho, foi elaborado um roteiro com recomendações para sistematizar o projeto de implantação de tecnologia de BI em processos gerenciais no contexto do Sistema Único de Saúde (SUS). Acredita-se que esse estudo tenha o potencial de incentivar o uso dessa tecnologia na gestão do SUS e fomentar a cultura da análise da situação e tomada de decisão com base em informação e nos processos de gestão colegiada.
... Knowledge management provides the most up-to-date techno-management trend for improving the work process and creating value for organizational operationsas it has the largest influence on competitiveness, strategic development and growth (Surbakti, 2015 Tacit knowledge is regarded as either technical: which encompass precise capabilities and competences applicable to a specific context; or cognitive which are mental models that include ideas, beliefsand schemes that enable individuals find their way in the world and achieve personal goals (Sejdija, 2012).This tacit knowledge although difficult to quantify can only be transferred with appropriate communication between transferor and transferee. Trust is a major factor that determines the transfer of technology (Verberne, 2012). ...
... Study findings have shown the need to capture, store and re-use preciously acquired knowledge by the ageing/experienced but retiring workforce. Surbakti (2015) attests that the outcome of this processcan be beneficial in decision making, for preventing past failures and can also be used as a guideline to treatrecurrent administrative issues. In a bid to achieve this, the first and most crucial step essential to the replication of knowledge has to do with capturing it from its tacit state to an explicit state which can be accessed, decoded and utilised by any succeeding generation of civil service employees regardless of the frequency at which new public administrators are employed to replace the retiring ones.Considerations were also made concerning unstructured knowledge with regards to relationships with Political office holder which is usually subject to individual differences and preferences of individuals involved. ...
... The repository of knowledge requires management processes for effectively capturing, storing, and reutilizing knowledge and ensuring high-quality products and services through the elimination of poor-quality costs (Almomani et al., 2019;Kivrak et al., 2014). According to Surbakti (2015), KM -as a tool and framework for creating and sharing knowledge -is used for solving problems and making decisions. ...
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This study aims at developing a knowledge management (KM) model that construction companies can apply for effective knowledge management implementations in their businesses. In developing the proposed model, a mixed methodology of analysing related literature and identifying the gaps in some existing models intended for the construction industry is applied. In addition, a questionnaire and interviews approaches are conducted to prepare, enhance, strengthen and validate the developed model. The major finding of this study is proposing a new KM model – named as BAN model – that fits potential conditions for construction contracting firms, especially enterprises that are small in size and nonadopting KM. The developed model comprises six main stages: (1) preliminary stage; (2) development of an organizational strategy stage; (3) start-up stage; (4) implementation stage; (5) monitoring and evaluation stage; and (6) derivation of short- and long-term KM values. The proposed model is capable of filling and solving the gaps in existing knowledge management models and defining major success factors in KM implementation. The benefits of the proposed model include the enhancement of the KM implementation process, facilitation of the decision-making process, attainment and maintenance of competitive advantages, improvement of innovation, and continuance of effective KM performance.
... Utilizing software applications, businesses might effectively respond to actual-time trends in digital displays, messaging frameworks and emails. Since this is all actual-time, entrepreneurs are given the opportunity to announce the offers which take advantages of whatever is happening in the intermediate market [4]. Marketing experts can also utilize information to structure creative limited-time fundamentals such as coupon codes. ...
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In the modern business day, enterprises have extensive data that has stimulated data variety in many information silos. These activities for extracting valued data from the sources apply Knowledge Management (KM) and Business Intelligence (BI) technologies and applications. The BI frameworks can effectively empower companies to share, analyse also access knowledge and data. Apart from that, KM fundamentally promotes the usage of knowledge and data that is available in the company. In that case, it might be assumed that KM and BI plays a fundamental role in the development of quantitative and qualitative data value which is essential for decision-making. These concepts can benefit from each other and it can be considered that BI plays a fundamental role in KM projects. For instance, BI techniques are applicable in KM for carrying and creating knowledge. The core rationale of this research is to evaluate the interactions, connections and differences between KM and BI. In this analysis, the essential information was taken and collected from different case studies and various literature sources. This procedure is used to compose this research which helps to access accurate data which is essential to mention enterprise management in the modern age. In general, effective agreement results has been witnessed in other scholastic research which states the clear connection of the two concepts and their interaction with each other.
... Omotayo (2015) investigated the importance of knowledge management process as a tool for organizational management, he argued that in order to have a successful knowledge management process in an organization three dimensions should be considered these dimensions are people, processes and technology. Surbakti (2015) integrated business intelligence processes and knowledge management in order to improve the administration in business organizations, he believed that the current model is quite useful for knowledge classification and analytical applications for prediction of organizational performance. Findikli et al. (2015) investigated the role of knowledge management strategy in organizations and their corresponding knowledge management capacity and showed that strategic methods give the human resources of an organization the ability to predict organizational innovation. ...
Chapter
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The chapter describes a concept of knowledge carriers management. It exploits the assumptions of an activity-based knowledge management without enforcing an individual to record knowledge artifacts and store them in an IT system but rather assuming he/she obtained the knowledge needed to complete the activity and storing this as a kind of organizational metadata. Then, the knowledge sharing process can be triggered in the future if needed.
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This paper proposes an integrated modelling framework for the analysis of manufacturing systems that can increase the capacity of modelling tools for rapidly creating a structured database with multiple detail levels and thus obtain key performance indicators (KPIs) that highlight possible areas for improvement. The method combines five important concepts: hierarchical structure, quantitative/ qualitative analysis, data modelling, manufacturing database and performance indicators. It enables methods to build a full information model of the manufacturing system, from the shopfloor functional structure to the basic production activities (operations, transport, inspection, etc.). The proposed method is based on a modified IDEF model that stores all kind of quantitative and qualitative information. A computer-based support tool has been developed to connect with the IDEF model, creating automatically a relational database through a set of algorithms. This manufacturing datawarehouse is oriented towards obtaining a rapid global vision of the system through multiple indicators. The developed tool has been provided with different scorecard panels to make use of KPIs to decide the best actions for continuous improvement. To demonstrate and validate both the proposed method and the developed tools, a case study has been carried out for a complex manufacturing system. (c) 2006 Elsevier Ltd. All rights reserved.
Book
Case Studies in Knowledge Management provides rich, case-based lessons learned from several examples of actual applications of knowledge management in a variety of organizational and global settings. A variety of KM issues are explored, including issues associated with building a KMS, organizational culture and its effect on knowledge capture, sharing, re-use, strategy, and implementation of KM initiatives and a KMS. The benefit of focusing on case and action research is that this research provides an extensive and in-depth background and analysis on the subjects, providing readers with greater insight into the issues discussed.
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Tacit knowledge and informal learning are claimed to be essential in creating and maintaining competitive advantage and innovation capability in organizations. Therefore acquisition and transfer of tacit knowledge is necessary for organization to survive and difficult because of low level of articulation. Is informal learning as a collective activity a precondition for tacit knowledge to be transferred and shared unarticulatedi The main question of the article is "How tacit knowledge is related with informal learningi" The aim of the article is to analyze the liaisons between tacit knowledge and informal learning in organization. The article does not contain empirical research and rather seeks to clarify key concepts and relationships between them. The paper commences with definitions of the concepts of tacit knowledge and informal learning clarifying inaccuracies and identifying the place for the concepts and the level of analysis. The paper is finalized by the description of the liaisons between tacit knowledge and informal learning - inaccuracies are identified and possible interconnections between them are visualized in the schemes. © Raimonda Alonderien?, Asta Pundzieni, Kistutis Krišiiinas, 2006.
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Business Intelligence (BI): It's not just a technology. It's not just a methodology. It's a powerful new management approach that - when done right - can deliver knowledge, efficiency, better decisions, and profit to almost any organization that uses it. When BI first came on the scene, it promised a lot but often failed to deliver. The missing element was the business-centric focus explained in The Profit Impact of Business Intelligence. Written by BI gurus Steve Williams and Nancy Williams, The Profit Impact of Business Intelligence shows step by step how you can achieve the promise of BI by connecting it to your organization's strategic goals, culture, and strengths while correcting your BI weaknesses. Features: * Provides a practical, process-oriented guide to achieve the full promise of BI. * Shows how world-class companies used BI to become leaders in their industries. * Helps senior business and IT executives understand the strategic impact of BI and how they can ensure a strong payoff from their BI investments. * Identifies the most common mistakes organizations make in implementing BI. * Includes a helpful glossary of BI terms. * Includes a BI readiness assessment for your organization. * Includes Web links and extensive references for more information. Steve Williams, President and founder of DecisionPath Consulting, is a leading advocate of business-driven BI design and development. His consulting firm is one of the largest in the specialized field of Business Intelligence and Data Warehousing. Steve has over 23 years experience in information systems and systems engineering and has co-authored a training course on the BI Pathway Method. Nancy Williams is Vice President of DecisionPath Consulting. With over 21 years of business and technical experience, she provides technical and strategic leadership on business intelligence as well as hands-on guidance for client engagements. * A practical, process-oriented book that will help organizations realize the promise of BI * Written by Nancy and Steve Williams, veteran consultants and instructors with hands-on, "in the trenches" experience in government and corporate business intelligence applications * Will help senior business and IT executives understand the strategic impact of BI and how they can help ensure a strong payoff on BI investments.
Conference Paper
As business competition intensifies and the market environment becomes increasingly complex, more and more enterprises learn to make use of Knowledge Management (KM) and Business Intelligence (BI) in order to improve corporate decision-making capacity and efficiency. However, there is still not a unified view for the concept of KM and BI and the relationship between the two in academia and the business world, which may bring about confusion and errors in theory study and application. this paper is trying to provide a thorough analysis of the differences between BI and KM and to establish a framework for relating one field to the other. And it comes to a conclusion that in business management and decision-making process, both BI and KM must be effectively integrated to give full play to their complementary functions.