Value and Importance of Knowledge Management in Organizations

This document discusses the value and importance of knowledge management in organizations. It explores how knowledge management can be used with various methodologies and technologies to improve business efficiency and generate revenue. The document also highlights the role of knowledge management in promoting data and knowledge sharing within an organization.

University
Semester
Business Intelligence
Student ID
Student Name
Submission Date
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Table of Contents
Question 1.............................................................................................................................................3
Question 2.............................................................................................................................................4
Question 3.............................................................................................................................................5
Question 4...........................................................................................................................................10
References...........................................................................................................................................12
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Question 1
We shall be discussing the value and importance of knowledge management and how this
can be used with the assistance of various methodologies and technologies in an organization.
This shall be our topic of discussion for this question.
Used daily in every organization and business firm, Knowledge Management plays
the important factor of knowledge sharing, which is vital and substantial for its intellectual
capital value. This process occurs at every level and stage of an organization structure with
the only intention to pass the relevant data and information. Keeping in mind its relevance to
the organization, it is vital to identify the right information to be shared and transferred,
especially with the relevance that this will be utilized by the high management levels. Same
time, it encourages the freedom of data and knowledge sharing within the various sections of
the organization. The importance here is given to “Knowledge Management” in the business
environment, as ultimately the goal of every business is to generate revenue and make profit.
By isolating the long procedure of approvals and data passing from one area of the
organization to another, knowledge management opts for the shortest route of data and
information distribution. Another point to observe is, that how with this exceptional tool, we
can efficiently and effectively improve the relationship within the organization by bringing in
all the different departments together and thus forming a bigger stronger knit business which
shall be more competitive and has greater value in the business community. (Colombi, 2019).
Unlike the data innovations which is more related to the learning part, Knowledge
management is involved more into preparation of meaningful data. This is directly assisting
the board members and the top level executives in their decision making abilities and basis
for information innovation. The various factors that influence these innovation for the
executives and for the man-made consciousness includes,
 Used in learning security
 Frameworks for executive’s data and information.
 Frameworks based on case thinking
 Video conferencing
 Emotionally supportive network option
 Data archives
 Web Technology and innovation support (Tlu.ee, 2019).
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Question 2
We shall be studying, discussing and evaluating about the differences between a
Database and a Data Warehouse. The points that shall be taken as topics for discussion shall
include,
 Advantages for end users for the use of data warehouse over data storage and
analysis.
 In Business Intelligence, the role played by data warehouse.
Below is the discussion and analysis for the differentiation between a database and a data
ware house (Pediaa.Com, 2019).
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Bringing to a central hub location, these centralized data storage units called as data ware
houses lets the organizations and businesses to combine all the data from all the various
sources, sectors and applications. Once all these data and information is collected at one
place, it than becomes easier for the next stage of processing i.e for data analysis, evaluation,
data sorting, filtering out, decision making etc.
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The primary reason why data warehouses are used so commonly in businesses and
organizations is their ability to bring in all the data and information to a central hub thus,
making it easier for modifications and conversions, from the very complicated and
unstructured data when collected. (Techadvisory.org, 2019) / (Stupakevich, 2019).
Below are some of the advantages of making use of the data warehouse,
 Business intelligence is improved.
 Access to data on timely basis
 Increased efficiency for query and system
 Improvement of data quality & consistency
 Best Investment returns
There is always an option which influences the procedure and the actions on the information
driven certainties dependency and by the data pertinent upheld by the association collected
after some time, while in business knowledge, the role of the data warehouse is to keep the
options data fragment depended.
Thus, the different processes of money linked administration, systematic deals, market
classification and stock administration can be legally validated and linked to more business
expansion and fulfilling of more profit based revenues. (GlowTouch, 2019).
Question 3
In this question, we are use the Weka software to do J48 classifier analysis on
Vote.arff file. After, we need to prepare the report based on classification results. The J48 is
one of the Decision tree algorithms on Weka. The J48 decision tree is used for building a
decision trees for Vote data set by using the J48 classifiers on weka. It has various parameters
by the default visualization only it displays like confidence value, minimum number of
instances in the two popular branches. By applying a J48 decision tree on the vote data set
which is used to predict the target variable of the data. The J48 decision tree is the
implementation of algorithm ID3 developed by weka team. The decision trees can support the
regression and classification problems. It is referred as classification and regression tree. The
decision tree is used to evaluate the provided data instance, start at the tree root and moving
down to roots until a prediction can be made. The tree is constructed which is pruned in order
to improve the models ability to generalize the new data (Brownlee, 2019).
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To do J48 decision tree, first we need to download and install the Weka software.
Once installation is completed, then we need to open Weka software which is illustrated as
below (Kaluza, 2013).
After, click Explorer to do classification on Vote.arff file which is illustrated as
below.
Then, upload the Vote.arff file to view the pre-processing tab to click the open file
and browse Vote.arff file and at last open the Vote.arff file which is illustrated as below. The
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pre -processing is used to evaluate the performance of the training data because it leads to
unrealistically optimistic performance estimates.
After, do J48 decision tree techniques by click the classify tab > Click Choose > Trees
> J48 which is illustrated as below.
After, conform the Test options as use training set and click the start to do J48
classification on Vote.arff file which is provide the following output.
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The J48 output is presented as below.
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Mean absolute error 0.0519
Root mean squared error 0.1506
Relative absolute error 10.9481 %
Root relative squared error 30.9353 %
Total Number of Instances 435
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area
Class
0.978 0.036 0.978 0.978 0.978 0.942 0.986 0.987 democrat
0.964 0.022 0.964 0.964 0.964 0.942 0.986 0.970 republican
Weighted Avg. 0.972 0.031 0.972 0.972 0.972 0.942 0.986 0.981
=== Confusion Matrix ===
a b <-- classified as
261 6 | a = democrat
6 162 | b = republican
The visualize tree is demonstrated as below.
Based on J48 classification results, it has
 Correctly Classified Instances 423 97.2414 %
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 Incorrectly Classified Instances 12 2.7586 %
The correctly classified instances are used to shows the accuracy of the model on the
vote data used for testing. In vote data model, it has high accurate because it has 97.2414 %
of classified instances. It is used to provide the effective classification tree. It also provides
the easy understanding of the decision tree. It is easily predicted the vote dataset which is
used to provide the similarities and difference between the attributes of the vote data.
In Confusion matrix, the Confusion Matrix is illustrated as below.
Confusion Matrix
a b <-- classified as
261 6 a = democrat
6 162 b = republican
 The decision tree J48 has classified 261 democrat objects as democrat and 6
republican objects as republican, leading in 6 misclassifications
 The decision tree J48 has classified 6 democrat objects as democrat and 162
republican objects as republican, leading in 6 misclassifications.
Question 4
Here, we are creating the business performance dashboard on the internet by using the
Vote.arff dataset (Stephen-few.com, 2019). The business dashboard is a one of the
information management tool which is used to track the key performance indicators, and
other key data points relevant the business processes. The dashboard is used to visualize the
data and simplify the large data set to provide the users with at a glance awareness of the
current performance. The dashboard is used to create the link between the data and business
performance by harnessing the metrics, information and insights on business because these
are most valuable key aspects of the business. It is used to provide the understanding how to
take meaningful actions from the data and also ensure the business remains like competitive,
robust and resilient. The dashboard is used to sort the information about the business process
such as (BI Blog | Data Visualization & Analytics Blog | datapine, 2019),
 Key performance indicators
 Metrics
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 Key data points
These are used to keep tract the various data source to provide the central location for
businesses which is used to analyze and monitor the performance. It reduces the hours of
analyzing and long line of the communications. Basically, dash board is an interface which
integrated the data from the multiple source to provide the unified the display of the
information. In visual views or applications, it is extend the dashboard functionality with
concurrent development support and it has capability to deliver the real time information on
demand (Data and Few, 2019).
The dashboard is requires the qualitative measures because it improve the situational
awareness by provide the business task tracking, prioritizing the capabilities, presenting the
insights on the workload, providing the customer views to help manage the work load while
performing the developments tasks and listing the individual actions on business processes
(Krohe, 2019). The qualitative metrics are also used to complete reporting in business
processes. The dashboard for provided data set is illustrated as below (Klipfolio.com, 2019).
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References
BI Blog | Data Visualization & Analytics Blog | datapine. (2019). Business Performance
Dashboard Examples & Templates You Need. [online] Available at:
https://www.datapine.com/blog/performance-dashboard/ [Accessed 8 Jun. 2019].
Brownlee, J. (2019). How To Use Classification Machine Learning Algorithms in Weka.
[online] Machine Learning Mastery. Available at: https://machinelearningmastery.com/use-
classification-machine-learning-algorithms-weka/ [Accessed 8 Jun. 2019].
Data and Few, S. (2019). Information Dashboard Design. [online] Goodreads.com. Available
at: https://www.goodreads.com/book/show/336258.Information_Dashboard_Design
[Accessed 8 Jun. 2019].
Kaluza, B. (2013). Instant weka how-to. [Place of publication not identified]: Packt
Publishing Limited.
Klipfolio.com. (2019). Data Dashboards. Definition, Design Ideas plus 3 examples. [online]
Available at: https://www.klipfolio.com/resources/articles/what-is-data-dashboard [Accessed
8 Jun. 2019].
Krohe, B. (2019). Data Is Almost Everything: Using Qualitative Metrics For Reporting.
[online] PowerPost. Available at: https://www.powerpost.digital/insights/data-is-almost-
everything-using-qualitative-metrics-to-complete-your-reporting/ [Accessed 8 Jun. 2019].
Stephen-few.com. (2019). Stephen Few – Information Dashboard Design. [online] Available
at: http://www.stephen-few.com/idd.php [Accessed 8 Jun. 2019].
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