OF KNOWLEDGE MANAGEMENT IN BUSINESS INTELLIGENCE AND DECISION SUPPORT
Narendra Kumar Sharma1, Subhash Chandra
Maurya2, Anand Vardhan Shukla3
Scholar, Amity University, Lucknow UP – India
Scholar, MGCGV, Chitrakoot UP – India
of Computer Science, Integral University, Lucknow UP – India
‘Knowledge is Power’ and it recognizes
the reality of our everyday working and personal lives. Knowledge has become
the key considered quality for the twenty first century and for every
organization that values knowledge it must invest in developing the best
strategy for identifying, developing and applying the knowledge assets it needs
to succeed. Knowledge Management (KM) is a compliance
that improves the ability of organizations to solve problems better, adapt,
evolve to meet changing business requirements and survive disrupting changes
such. The present paper focuses on the importance of Knowledge Management in Business Intelligence and
Decision Support System. This paper will also provide an introduction to the
increasingly important area of Business Intelligence & data mining and
explain how knowledge is benefited for organization and decision support.
Knowledge Management, Data Mining, Business Intelligence, Decision Support System.
is an important asset to any organization and what an organization does with
this knowledge can be a critical component in their success. Knowledge alone can give one organization a
distinct competitive advantage in the market. Knowledge encompasses far more
than intellectual property such as processes and methods that might be unique to
II. KNOWLEDGE MANAGEMENT
According to Gartner, “Knowledge management is a
discipline that promotes an integrated approach to identifying, capturing,
evaluating, retrieving, and sharing all of an enterprise’s information assets. These
advantages may incorporate databases, reports, strategies, systems, and already
un-caught skill and involvement in singular specialists.
According to Knowledge Management Tools, knowledge typically
falls into one of three categories:
Explicit knowledge – Including
document management, intelligence gathering, and data and text mining.
Tacit knowledge – Including
surveys and questionnaires, information from individual and group interviews,
focus groups, network analysis, and findings from observation.
3. Embedded Knowledge –
Referring to knowledge that’s not immediately obvious or available on the
surface, including analysis from observations, reverse engineering, modeling
tools to identify knowledge that may be stored within procedures and the like.
III. KNOWLEDGE MANAGEMENT ASPECT
management is a
collection of systematic approaches to help information and knowledge flow to
and between the right people at the right time. KM is essentially about getting
the right knowledge to the right person at the right
time. KM may also include new knowledge creation, or it may solely focus
on knowledge sharing, storage, and refinement. Implementing knowledge management thus has
several dimensions including:
1: Dimensions of KM
1. Strategy: KM
strategy must be dependent on corporate strategy. The objective is to manage,
share and create relevant knowledge
assets that will help meet tactical and strategic requirements.
Culture: The organizational culture influences the way
people interact, the context within which knowledge is created, the resistance
they will have towards certain changes, and ultimately the way they share or
the way they do not share knowledge.
Processes: The right processes, environments, and systems
that enable KM to be implemented in the organization.
& Leadership: KM requires competent and
experienced leadership at all levels. There are a wide variety of KM related
roles that an organization may or may not need to implement.
5. Technology: The
systems, tools and technologies that fit the organization’s requirements properly
designed and implemented.
6. Politics: The
long-term support to implement and sustain initiatives that involve virtually
all organizational functions, which may be costly to implement (both from the
perspective of time and money).
III. BUSINESS INTELLIGENCE
to Gartner, Business Intelligence (BI) as a set of all technologies that gather
and analyze data to improve decision making. In business intelligence,
intelligence is often defined as the discovery and explanation of hidden,
inherent, and decision-relevant contexts in large amounts of business and
IV. STRUCTURE OF BI
BI is formed by a set of various
software technologies as Olszak & Batko, 2012: Data warehouse (DW), data
marts, data mining, online analytical processing (OLAP), extraction transform
load (ETL) and other reporting applications.
2: Structure of BI
1. DW: It
is an integrated collection of the summarized and historic data, which is
collected from internal and external data sources Radonic, 2007. It is the
significant component of BI, and subject oriented and integrated. It supports
the spread of data by handling the numerous enterprise records for sintegration,
cleansing, aggregation and query tasks.
2. Data Marts: These
are small sized DWs, usually created by individual departments Khan, 2012.
These are a collection of subject areas organized for decision support based on
the needs of a given department. These help business experts for the analysis
of past trends and experiences Inmon, 1999.
3. Data Mining: It
is a method of finding patterns, correlation, generalizations, regularities and
rules in data resources and trends by modifying through the large amount of
data, which is stored in the warehouse. Muhammad
& Ibrahim, 2014.
4. OLAP: It
is the technology that enables the user to interact, analyze, report and
present the data in the DW. It represents a form of a multidimensional and
summarized business data analysis, and is used for reporting, analysis,
modeling and planning for optimizing the business Panian & Klepac, 2003.
It refers to the way in which business users can slice and dice their way by
using complicated tools that allow for the improvement of business Ranjan,
5. ETL: It
is a set of actions by which data is extracted from numerous databases, applications
and systems, transformed as capture, and is loaded into target database. It is responsible
for data transfer from operational or transaction systems to DW Gadu & El-
V. RELATIONSHIP BETWEEN BI AND KM
relationship between BI and KM perform similar activities in collecting data,
organizing the data, analyzing data, aggregating data and applying data to
generate solutions to help make business decisions. However KM includes two other activities that
BI lacks. These activities are the
creation of new knowledge and the dispersion of knowledge throughout an
organization. This is where knowledge
management encompasses the activities of business intelligence. The below table
shows the similar and different activities performed by KM and BI.
Create new knowledge
No equivalent action!
No equivalent action!
1: Activities between KM vs BI
DECISION SUPPORT SYSTEM
A decision support system (DSS) is a computerized
information system used to support decision-making in an organization or a business.
A DSS lets users sift through and analyze massive reams of data and compile
information that can be used to solve problems and make better decisions. The
benefits of decision support systems include more informed decision-making,
timely problem solving and improved efficiency for dealing with problems with
rapidly changing variables.
3: BI and KM for DSS
A properly designed DSS is an
interactive software-based system intended to help decision makers compile
useful information from a combination of raw data, documents, personal
knowledge or business models to identify and solve problems and make decisions.
Typical information that a decision support application might gather and
Inventories of all of your current
information assets (including legacy and relational data sources, cubes, data
warehouses, and data marts),
Comparative sales figures between one
week and the next,
Projected revenue figures based on new
product sales assumptions.
INTEGRATION OF KM AND BI FOR DSS
and KM provides real technological support for Strategic Management (Albescu et
al., 2008). This integration will not only facilitate the capturing and coding
of knowledge but also enhances the retrieval and sharing of knowledge across
the organization to gain strategic advantage and also to sustain it in
competitive market (Khan and Quadri, 2012).
4: Technological Integration b/w BI and KM
BI and KM is the scope of activities involved in each area. Business intelligence focuses solely on
capturing data, manipulating the data and analyzing the data. Whereas knowledge management would perform
business intelligence activities while also pursuing the creation of new
VIII. IMPORTANCE OF KM IN DECISION
are several reasons why KM is important to implement:
It ensures all relevant information and
resources can be access by employees when they need it
Important knowledge is kept within the
business even after employees move on from the business
It avoids duplicated efforts
Take advantage of existing expertise
Standardized processes and procedures
for knowledge management
Every organization needs the proper knowledge
management strategy for the development of the organization. Both
KM & BI are deeply influenced by the culture of the organization,
especially leadership, groups and opinion leaders, as well as organizational
values. In the last decades the
business environment has changed and recently it becomes more dynamic and more
complex. At present KM is valuable not only for individuals, and organizations,
but also for global humanity.
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