Use of Analytics in Knowledge Management

This paper analyzes how business analytics would be applied in knowledge management for a business to assess how knowledge obtained would be useful in decision making. The processes involved in knowledge management include discovery of knowledge, collection of knowledge, capturing, analysis and sharing of knowledge. Figures have been included in assessment in order to give more insights about the assessment and how decision can be generated in a business from big data analysis.

Use of Analytics in Knowledge Management
Given Name Surname
Department
Institution
City, Country
E-mail Address
Abstract- Knowledge management involves
activities concerned with capturing,
discovering, applying and sharing
knowledge for enhancing strategic impact
from knowledge while technologies and
practices employed in knowledge
management to aid in decision making
business in are known as business analytics.
Moreover, the fields for knowledge
management together with intellectual
capital get distinguished precisely between
information, knowledge and data.
Furthermore, as a basic concept in the field,
knowledge is found to sometimes go beyond
simple information or data regarding to idea
on some level of reflection. In addition,
Social media in the current world has
reinvigorated various programs that
implement and develop KM strategy. Today
more organizations exploit business
analytics so as to attain good decision-
making. Notably, this paper is concerned
with analysis of how business analytics
would be applied in knowledge management
for a business to assess how knowledge
obtained would be useful in decision
making. The processes involved in
knowledge management include discovery
of knowledge, collection of knowledge,
capturing, analysis and sharing of
knowledge. On the other hand, figures have
been included in assessment in order to give
more insights about the assessment and how
decision can be generated in a business from

big data analysis. In conclusion, it is notable
that there is a strong connection between
KM and business analytics hence knowledge
management can be achieved more easily
and would help in business decision making.
Keywords: Knowledge Management,
Analytics, Data, Information
I. INTRODUCTION
With advancements and changes on
daily basis, organizations need to be
informed and prepared with potential
applications and trends so as to achieve
competitive advantage. Knowledge
management refers to the set of techniques
that are used in capturing, sharing, codifying
and effectively using information so as to
achieve business set objectives to help in
business decision making due to business
analytics [1]. On the other hand, business
analytics refers to technologies, practices
and skill of bringing and exploring
quantitative data so as to bear a good
decision making [2]. Moreover, effective
knowledge management (KM) has become
more useful as firms do access data from
various sources. In addition, practitioners do
use big data analytics in categorizing data
and to gain useful customer sentiments
knowledge towards their competitor and
firm organizations.
Moreover, study in this report is
concerned with knowledge management
using business analytics. Notably, analysis
that has been done is qualitative as no field
work was involved, thus simple analytics
have been employed. Furthermore, through
creation of knowledge management system
(KMS) for integrating KM and Big Data
technology, business is positioned to extract,
utilize and store knowledge more
effectively. Management of knowledge has
therefore been done sequentially based on
the applications of analytics leading to
generation of useful insights to identify
suitable knowledge to a business operation
[3].

II. DISCOVERY OF
KNOWLEDGE
The initial step involved in the
process is determination of how an
organizational knowledge can be
discovered. There are always numerous
sources of important data for a business thus
the process requires a deep understanding
concerning the flow of knowledge found
within the organization. Moreover, this
would involve determining most important
practices in business where data can be
obtained, discovering within the
organization where most critical knowledge
bases as well as well as determining how
information may be lost or trapped in the
process. In addition, the steps through which
an organization can undergo the discovery
process include identifying current
knowledge in in organizational processes,
conducting interviews for groups or
individuals within the organization, hiring of
new consultants and talents with critical
knowledge from outside the organization
and lastly, utilizing data – mining techniques
and intelligence – gathering obtained from
customer related information, manufacturing
as well as business based practices.
According to [2], such analytics help in
knowledge discovery in an organization as
in figure 1 below.
Fig 1: Showing Summary of
Discovery of Knowledge (Source: [2])
III. COLLECTION OF
KNOWLEDGE

Collection of knowledge is a crucial
process in knowledge management [3]. In
gathering of knowledge, analytical processes
are undertaken to aid in data collection.
Moreover, the methods taken for data
collection depend on the source and type of
data. Notably, it is worth noting that data
collection methods are systematic and
follows procedures that are well structured.
In addition, with data collection procedures,
data extraction tools and techniques are as
well defined. Consequently, most
organizations apply software databases to
conducting this process. To begin with, the
first step in data collection is identification
of information needs. This involves listing
of stakeholders, determining the information
required for and from them and obtaining
information in the organization.
Furthermore, the information
obtained is translated into categories like
knowledge management system. In addition,
questionnaires are designed and used with
objective of gathering various information
from given respondents in consideration to
literacy levels. In addition, data is then
collected with follow – up reminders set.
Moreover data obtained is verified and
filtered to improve its quality so that it can
be processed and stored. Figure 2 below
shows summary of data collection process.
Fig 2: Processes of Data Collection
(Source: [4])
IV. CAPTURING OF
KNOWLEDGE

Knowledge capturing refers to
techniques by which knowledge of both new
and established organization can be stored.
Moreover, from this step, organization
benefits through expansion of its process of
knowledge management structure. On the
other hand, large amount of useful business
information are mapped and categorized for
organizations to realize most benefits
essential in the knowledge [2]. Additionally,
digital storage is essential means of storage
for complex organizations. Notably, for data
management and easier access, written
information in documents may be scanned
and stored in digital databases. Also, useful
information can be classifieds through use of
the digital system of metadata involving
identifying repeated names, dates, indexing,
along with keywords. Hence, this can help
in searching, retrieving and capturing most
relevant stored knowledge.
V. PROCESSING / ANALYSIS OF
KNOWLEDGE
Notably, after the discovery,
collection and capturing of knowledge, it
would then require to be processed and
analyzed using suitable analytics in order to
improve its utility. Furthermore, at this
stage, analysis is majorly concerned with big
data. Moreover, big data provides a lot of
insights based on the validity, velocity and
volume of the data that leads to effective
knowledge for accurate decision making [4].
It is worth noting that there is a profound
association KM between knowledge, data
and information, thus information and data
can turn into knowledge from reflection.
Since organization has obtained data and
information contributing to the most useful
organization knowledge.
Thus, additional analysis, assessment
and reorganization are required. Moreover,
applying statistical analysis methods like
hypothesis testing and regression analysis,
the analyst would get factors relating to
target variable. Also, a simple regression

analysis would be performed to determine
whether postulations can be made.
Furthermore, various groups are compared
based on different assumptions which are
then tested by hypothesis test. Notably, at
this stage, data is diced, cut and sliced
followed by various comparisons in order to
derive from the data actionable insights [1],
[4].
Furthermore, knowledge synthesis
from the process would assist in determining
how knowledge would be integrated into the
rules and procedures of an organization.
Also, it would also help in establishing a
knowledge culture as well as developing
teams and individuals who would contribute
to great improvements as well as
innovations to an entire organization [5]. For
instance, a big data (Figure 3) from
McKinsey Global Institute (MGI) report,
showing industrial categorizations on
intangible assets levels activity of
competitive intelligence versus
capitalization of market to the assets ratio
has been used in assessing intangible assets.
Fig 3: Relationship between Big data,
Competitive Intelligence and Knowledge
by an Industry (Source: [6])
From the table above, ideas based on
the relationship between knowledge and big
data can be noted as well as differences in
concepts in the information. Also, it is

notable that first three consecutive columns
have been taken directly from MGI report
together with industry definitions and sorted
based on data stored in every firm [6]. The
total data has been estimated with
employees exceeding 1,000, thus the
number has been divided by the firm
numbers so as to obtain figure per firm. In
addition, the report gives an approximation
of “Ease of Capture” as a value potential in
industrial big data. Moreover, the estimate
value is depended on four aspects, which
majorly offers strong relation to probable
knowledge concepts.
To begin with, the insight obtained
from the report in the indicator of talent
from the table reveals that talent would
appear closely related to the human
understanding of capital. Particularly,
human capital which has tacit emphasis
since individual know – how or talent would
not be easy to share. Secondly, it is
noteworthy that IT intensity possesses good
connection to essential capital [7]. Also, the
firm’s IT structure for managing
information, knowledge and data also form
significant part of capital structure. Lastly,
another aspect is that the firm has
appreciable information, data and explicit
knowledge thus making it simpler to share
and leverage. Thirdly, data – driven mindset
potentially connects back to human capital,
more so firm leaders and managers’
knowledge [8]. Since this is likely more of
individual knowledge, it is thus tacit hence
not possible to replicate. Lastly, data
availability as another indicator deals with
information, knowledge precursors and data.
Therefore, using the analytics, useful
knowledge has been obtained for the
industry due to successful processes of
capturing, identifying and leveraging
intangible assets. Moreover, it has been
noticed that higher number above 1.02
averages, shows that knowledge is very
important in industry. Also, from big data

implications, businesses significantly use
data [9]. Thus reflection from big data
provides insights (explicit knowledge) for
business decision making.
VI. SHARING AND BENEFIT OF
KNOWLEDGE
The aspect of sharing knowledge
among individuals within an organization
explains the reason why business adopts
business analytics strategies on knowledge
management [10]. To begin with, it is
important to determine individual who
would mostly benefit from organizational
knowledge thereby devising means of
accessing it easily and quickly [11].
Moreover, managers may need that
individuals undergoing training to apply
knowledge for business practices as well as
understanding what an organization benefits
from in order to realize better results for
knowledge management efforts.
Furthermore, knowledge analytics would be
useful in business since it would enable
managers in decision making [12]. Also, it
may assist KM team of sharing knowledge
and also would allow organizations seizing
business opportunities concerning
competition on knowledge [3], [6].
VII. CONCLUSION
In conclusion, it is notable that a
strong connection occurs between KM,
business analytics and big data. Moreover,
all of such aspects deal with intangible
assets like information, data intelligence and
knowledge. In addition, when focus is put
on strategic aspects of protecting and
developing knowledge, a good conception
concerning how knowledge assets can be of
benefit to an organization can be obtained.
Furthermore, through review of variables
like nature of knowledge, organizations and
businesses are able to identify suitable
knowledge required. Thus in conclusion,
business analytics provide potential
knowledge and insights suitable for business
operations.

REFERENCES
[1] I. Becerra-Fernandez and R.
Sabherwal, Knowledge management:
Systems and processes, 2nd ed. Routledge,
2014.
[2] F. Acito and V. Khatri, "Business
analytics: Why now and what
next?", Business Horizons, vol. 57, no. 5,
pp. 565-570, 2014. Available:
10.1016/j.bushor.2014.06.001 [Accessed 23
May 2019].
[3] C. Holsapple, A. Lee-Post and R. Pakath,
"A unified foundation for business
analytics", Decision Support Systems, vol.
64, pp. 130-141, 2014. Available:
10.1016/j.dss.2014.05.013 [Accessed 24
May 2019].
[4] L. Duan and Y. Xiong, "Big data
analytics and business analytics", Journal of
Management Analytics, vol. 2, no. 1, pp. 1-
21, 2015. Available:
10.1080/23270012.2015.1020891 [Accessed
24 May 2019].
[5]Z. Khan and T. Vorley, "Big data text
analytics: an enabler of knowledge
management", Journal of Knowledge
Management, vol. 21, no. 1, pp. 18-34,
2017. Available: 10.1108/jkm-06-2015-0238
[Accessed 24 May 2019].
[6]S. Erickson and H. Rothberg, "Big Data
and Knowledge Management: Establishing a
Conceptual Foundation”", The Electronic
Journal of Knowledge Management, vol. 12,
no. 2, pp. 108-114, 2014. Available:
http://www.ejkm.com. [Accessed 24 May
2019].
[7]M. Najafabadi, F. Villanustre, T.
Khoshgoftaar, N. Seliya, R. Wald and E.
Muharemagic, "Deep learning applications

and challenges in big data
analytics", Journal of Big Data, vol. 2, no.
1, 2015. Available: 10.1186/s40537-014-
0007-7 [Accessed 24 May 2019].
[8] H. Rothberg and G. Erickson, "Big data
systems: knowledge transfer or intelligence
insights?” Journal of Knowledge
Management, vol. 21, no. 1, pp. 92-112,
2017. Available: 10.1108/jkm-07-2015-0300
[Accessed 24 May 2019].
[9] H. Özköse, E. Arı and C. Gencer,
"Yesterday, Today and Tomorrow of Big
Data", Procedia - Social and Behavioral
Sciences, vol. 195, pp. 1042-1050, 2015.
Available: 10.1016/j.sbspro.2015.06.147
[Accessed 24 May 2019].
[10] P. Ritala, H. Olander, S. Michailova
and K. Husted, "Knowledge sharing,
knowledge leaking and relative innovation
performance: An empirical
study", Technovation, vol. 35, pp. 22-31,
2015. Available:
10.1016/j.technovation.2014.07.011
[Accessed 24 May 2019].
[11] R. Grossman and K. Siegel,
"Organizational Models for Big Data and
Analytics", Journal of Organization Design,
vol. 3, no. 1, pp. 20-25, 2014. Available:
https://ssrn.com/abstract=2458909.
[Accessed 24 May 2019].
[12]S. Erevelles, N. Fukawa and L. Swayne,
"Big Data consumer analytics and the
transformation of marketing", Journal of
Business Research, vol. 69, no. 2, pp. 897-
904, 2016. Available:
10.1016/j.jbusres.2015.07.001 [Accessed 24
May 2019].