Data Analytics Tool Usage and Impact

This assignment delves into the experiences of organizations utilizing IBM Watson Analytics for data analysis. It presents a questionnaire survey focusing on tool usage, benefits, enhancements to business processes, and future plans. Participants share their insights on how Watson Analytics impacts their data analytics departments and overall business operations.

Running Head: FINAL PROJECT REPORT
Final Project Report: Business Improvement Plan/Data Analytics
Name of the Student
Name of the University

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Executive Summary
Data analytics is an important aspect of any business that wants significant progress in today’s
market. Initially, the analytics operations were done manually and as a result, there were
numerous errors in the calculations. However, after the arrival of the softwares, this process has
now become much more easier. The data analytics softwares can now efficiently carry out any
analytics operations within a matter of microseconds. The software data analytics tools must be
implemented by the business organizations in order to progress in modern market. There are a
number of software tools available for data analytics including IBM Watson Analytics, Pentaho,
Clic Data, Tap Clicks and others. Most business organizations utilize these analytics tools for
business operations that are related to the use of numerous pieces of data and information. This
report is based on the research conducted on data analytics in business with special emphasis on
IBM Watson Analytics tool.

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Table of Contents
1.0 Introduction................................................................................................................................4
2.0 Problem Statement.....................................................................................................................5
2.1 Definition of the Problems.....................................................................................................5
2.2 Identification of the Stakeholders..........................................................................................5
3.0 Project Objectives......................................................................................................................6
4.0 Related Academic and Commercial Research...........................................................................6
5.0 Relevant Theories and Frameworks..........................................................................................8
6.0 Methodology..............................................................................................................................8
7.0 Data Collection and Analysis..................................................................................................10
7.1 Data Collection....................................................................................................................10
7.2 Data Analysis.......................................................................................................................10
8.0 Discussion of the Artefact (IBM Watson Analytics)...............................................................16
9.0 Summary of Findings, Limitations, Recommendations..........................................................18
9.1 Summary of Findings..........................................................................................................18
9.2 Limitations...........................................................................................................................20
9.3 Recommendations................................................................................................................21
References......................................................................................................................................23
Appendix........................................................................................................................................27

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1.0 Introduction
The client for this particular project is Vista Concept Dimensions who are looking to
implement a data analytics tool in their system. They are currently facing problems due to their
manual data analytics process and hence, they require a new automated analytics system. The
main objective of this particular project is to identify only one data analytics tool that is the best
option for all the business organizations. Hence, in order to conduct the project, questionnaire
survey method was used. Collection of secondary data from the organizational records of the
analytics tools was rejected due to one particular reason: biasing. Developers of each of these
tools always claim their product to be the best in the market. Hence, the target in this research
was to capture actual user experience data to identify the best analytics tool available. The
decided method to undertake the survey was by using questionnaires where 15 questions were
asked. The target audiences of the survey were different organizational leaders for small scale
organizations and organizational management staff for the large scale ones. The interview was
conducted in form of direct interviews (if possible), video calls and online distribution of
questionnaire copy.
This report is based on the project conducted on data analytics in business with special
emphasis on IBM Watson Analytics tool. The survey data has been analyzed in order to identify
the specific feedbacks on the IBM Watson Analytics tool as it is the main point of focus in this
particular research. In addition, other analytics tools were also analyzed in order to determine
whether IBM Watson is better than them in terms of services and features or not.

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2.0 Problem Statement
2.1 Definition of the Problems
The main problems that need to be addressed in course of the project are as follows.
P1: The existing data analytics system has very low efficiency as it is mostly handled
manually.
P2: Currently, Microsoft Excel is used to store business data that has large number of
limitations.
P3: In the client’s existing manual analytics system, due to lack of sufficient data
analytics results, the designs cannot be produced of the best quality.
2.2 Identification of the Stakeholders
The stakeholders of the project have been identified as follows.
Project Manager – To manage the different aspects of the project
Project Supervisor – To supervise and monitor the progress of the project
Financial Support – To provide sufficient financial support for the project
Sponsor – To provide the overall budget for the project
Software Developer – To develop the chosen analytics tool in the system
Software Tester – To test the developed software for bugs and glitches

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3.0 Project Objectives
The objectives of the project are as follows.
 To suggest a new system to replace the existing manually handled data analytics
system
 To replace the excel storage technique with some software powered techniques
 To prepare an implementation plan for the new data analytics tool as well as
training the employees regarding the use of this tool
4.0 Related Academic and Commercial Research
Data Analytics is a process by which different pieces of data are analyzed in order to
extract a particular piece of information from it. Before the availability of advanced technology,
data analytics were done manually. However, after the advancement of software technology, data
analytics is now software driven. There are a number of software tools available for data
analytics including IBM Watson Analytics, Pentaho, Clic Data, Tap Clicks and others.
According to Tsoi et al. (2017), data analytics is an important aspect of any business that
wants significant progress in today’s market. Initially, the analytics operations were done
manually and as a result, there were numerous errors in the calculations. However, after the
arrival of the softwares, this process has now become much more easier. The data analytics
softwares can now efficiently carry out any analytics operations within a matter of microseconds.
Hoyt et al. (2016) said that the software data analytics tools must be implemented by the
business organizations in order to progress in modern market. Most business organizations

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utilize these analytics tools for business operations that are related to the use of numerous pieces
of data and information.
Guidi et al. (2016) promoted the use of Pentaho by publishing data sets that it can utilize
Big Data completely and its values are accelerated by NoSQL, Hadoop and other big data
platforms. This tool integrates all the data collected on a specific subject and then classifies it
using its customization analytics tool. It then analyzes the data and provide reliable business
insights and suggestions based on the information contained in the data.
Lak et al. (2016) discussed about other tools like Clic Data and Tap Clicks. According to
them, Clic Data has different and unique features than the other existing ones. Clic Data also acts
as a data visualization and business intelligence tool that also collects, analyzes and cleans the
data according to the requirements set by the user. It can also set up visual indicators in the data
as well as KPIs and other key metrics so that the user can easily share the result data with the
clients and colleagues. They also said that Tap Clicks is designed to provide marketing analysis
services rather than only data analytics i.e. it is specific to marketing data for the analytic
functions. Moreover, it is also used to create a workflow plan and order management plan that
are some of the most essential parts of some business organizations.
Aggarwal and Madhukar (2016) recommended the use of IBM Watson by saying that it is
by far the best tool available in today’s market. According to them, IBM Watson can efficiently
determine a pattern or sequence within a specific set of data without any difficulty. IBM also has
IBMSPSS predictive analytics and data science experience tools that further enhance the features
of the data analytics tool.

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5.0 Relevant Theories and Frameworks
In order to conduct this research, the use of some relevant theories and frameworks were
necessary. The main framework that was utilized in this research was Technology Acceptance
Model (TAM). The use of this particular framework helped to analyze how a data analytics tool
like IBM Watson will benefit the given company under the field of the research.
6.0 Methodology
In order to conduct the research, questionnaire survey method was used. Collection of
secondary data from the organizational records of the analytics tools was rejected due to one
particular reason: biasing. Developers of each of these tools always claim their product to be the
best in the market. Hence, the target in this research was to capture actual user experience data to
identify the best analytics tool available. The decided method to undertake the survey was by
using questionnaires where 15 questions were asked. The target audiences of the survey were
different organizational leaders for small scale organizations and organizational management
staff for the large scale ones. The interview was conducted in form of direct interviews (if
possible), video calls and online distribution of questionnaire copy. Before starting the interview,
the organizational members and leaders were contacted and prior appointments were made. Most
of the organizational leaders actively helped by answering the questions accurately as per the
requirements of the survey.
This survey was conducted among 150 members from different organizations and it has
been found in the result that 70% votes were in favor of IBM Watson Analytics i.e. 70% of the
people said they are currently using IBM Watson Analytics as they find it the most helpful for
their data analytics operations. Moreover, from this survey, it has been found that the following

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reputed organizations are actively using IBM Watson Analytics in their data analytics
department.
a) 1-800-Flowers
b) Macy’s
c) H&R Block
d) Staples
e) Autodesk
f) Chevrolet
g) The North Face
h) TD Ameritrade
i) Rare Carat
j) The Weather Company
It has also been found that the business operations of these companies have been
significantly enhanced by the use of IBM Watson Analytics tool. However, this is not due to
only the use of this tool in the data analytics department. These organizations innovated their
own virtual systems and connected it to the analytics tool that has revolutionalized their
businesses. For instance, Staples provides a red button in their order processing system. By
pressing this button, one simply has to utter “order me some red pens” or “order me some blue
pens”. The IBM Analytics tool will instantly process this voice message and will order for the
pens immediately. Similarly, the IBM tool enhanced the business processes of all the
organizations by linking up the business processes and data analytics operations.

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7.0 Data Collection and Analysis
7.1 Data Collection
As discussed in the methodology section, the main data collection has been done from the
survey interviews of different organizational members and leaders. However, before proceeding
with the survey, an in-depth analysis of literature has been conducted. During this phase, the
works of researchers based on the data analytics and the influence of software in data analytics
have been studied. When sufficient data had been collected, the survey was conducted to capture
data regarding user experiences on using software driven data analytics tools. Conducting both
the data collection methods helped to reduce biasing of opinions as well as verification of the
hypotheses that were proposed before the start of the research.
7.2 Data Analysis
The data collected included the processes of manual data analytics, software driven data
analytics, different analytic tools available in the market and the types of services and advantages
they provide. In order to collect sufficient data for literature survey, online databases like google
scholar and other online libraries. After the data collection for literature survey was complete, the
survey was started. The interview was conducted in form of direct interviews (if possible), video
calls and online distribution of questionnaire copy. Before starting the interview, the
organizational members and leaders were contacted and prior appointments were made. Most of
the organizational leaders actively helped by answering the questions accurately as per the
requirements of the survey.
When all the data was collected from literature review and the survey, analysis was
conducted using all the data. Firstly, a comparison was made between the literature data and

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survey data for verification of the hypotheses proposed before the starting of the project. After
that, the survey data was analyzed in order to identify the specific feedbacks on the IBM Watson
Analytics tool as it is the main point of focus in this particular research. In addition, other
analytics tools were also analyzed in order to determine whether IBM Watson is better than them
in terms of services and features or not. The data analysis results are as follows.
From the data analysis process, it has been seen there are a number of data analytics tool
currently available in the market. Some of the most popular ones are discussed as follows.
IBM Watson Analytics – This has been found to be the most popular of all the data
analytics tools available in the market. This tool was invented by IBM and has the features of
data analysis and visualization within a cloud based virtual interface. IBM Watson analytics can
refine, explore, predict and assemble the data. IBM Watson can efficiently determine a pattern or
sequence within a specific set of data without any difficulty. It also provides complex cloud
based services along with guidance and data prediction. IBM also has IBMSPSS predictive
analytics and data science experience tools that further enhance the features of the data analytics
tool. It also has cognos analytics in which it provides services as hosted solution via IBM cloud.
In addition to achieving vista concept dimensions’ business client through the free choice,
Watson Analytics additionally guarantees availability through representation and self-benefit
usefulness.

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Figure 1: Use of IBM Watson for Data Analysis
(Source: Watson, 2014)
Pentaho – Pentaho is another business data analytics tool that has been designed by
Hitachi. Pentaho is slightly different from IBM Watson in terms of services and features.
Pentaho mainly utilizes Big Data and its values are accelerated by NoSQL, Hadoop and other big
data. This tool integrates all the data collected on a specific subject and then classifies it using its
customization analytics tool. It then analyzes the data and provide reliable business insights and
suggestions based on the information contained in the data.

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Figure 2: Data Analytics Dashboard of Pentaho
(Source: Watson, 2014)
Clic Data – This is another data analytics tool that some different and unique features
than the other existing ones. Clic Data also acts as a data visualization and business intelligence
tool that also collects, analyzes and cleans the data according to the requirements set by the user.
It can also set up visual indicators in the data as well as KPIs and other key metrics so that the
user can easily share the result data with the clients and colleagues.

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Figure 3: Data Analytics with Clic Data
(Source: Watson, 2014)
Tap Clicks – The final data analytics tool featured in this research is tap clicks that is
specially designed to provide marketing analysis services rather than only data analytics i.e. it is
specific to marketing data for the analytic functions. Moreover, it is also used to create a
workflow plan and order management plan that are some of the most essential parts of some
business organizations.

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Figure 4: TapClics Data Analytics
(Source: Watson, 2014)
The main points are compared as follows.
IBM Watson Pentaho Clic Data Tap Clics
It utilizes a cloud based
interface
It uses the functions of
big data tools like
MySQL, Hadoop, etc.
It uses normal online
interface features
It uses normal online
interface features
It identifies the patterns
within the data and
processes it accordingly
It classifies the data into
different categories and
then processes it
It analyzes the data only
based on user
requirements
It analyzes only
marketing data

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It can classify any kind
of business data and
hosts it in IBM Cloud
It utilizes various tools
to analyze and store the
data
It requires user input for
classifying, analyzing
and hosting the data
It hosts the marketing
data on its own cloud
interface
8.0 Discussion of the Artefact (IBM Watson Analytics)
It is the next generation, using software powered user experience; the automated data
pattern detection system can be created. This will also accept the natural language query as it can
handle both IT related data or business user content data. The main design pattern of the
automated data analytics systems are such that the users can understand the working and features
of the application easily. It provides strong analytic techniques that massively enhance the
quality of the business operations of any organization. IBM also has IBMSPSS predictive
analytics and data science experience tools that further enhance the features of the data analytics
tool.

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Figure 5: Use of IBM Watson Analytics for Predictions of Churn
(Source: Watson, 2014)
Of all these data analytics tools, it has been found that IBM Watson is the most popular
tool and is used extensively by business organizations worldwide. There are several reasons as to
why it is the most popular whereas the other tools discussed in the research also have their own
unique features as well as intelligence. The several aspects for the reason of popularity of IBM
Watson are listed as follows.
Analytics Intelligence – IBM Watson is one of the most intelligent tools in the market
today. It helps the users to identify different patterns in a particular data set by automatic
classification and reading of the data. Moreover, IBM Watson has support for multiple languages
and hence, based on the entered user data, this tool can read through any language and perform
data analytics processes. However, some researchers have suggested including more
personalized data analytics feature so that the user himself can personalize the analytics criteria
to be performed on a specific piece of data.
Recommender System (RS) – This is a unique feature of IBM Watson that provides
personalized recommendations to the user after analysis of the input data. The bases of this
recommender system are prior usages and analytics history set by the user in the system.
Recently, a more advanced recommender system has been developed namely, Context Aware
Recommender System where the tool will analyze the context of the input at a particular time
and provide suitable personalized recommendations to the user.
Database System – Every analytics tool requires a suitable database to store the data it
deals with everyday. IBM Watson has dedicated logging database powered by ElasticSearch.

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This ElasticSearch provides a browser based user interface namely, Kibana for viewing logs and
exploring real time events. This feature is very useful for the business users who can easily
extract data from the events that have been inputted into the database of the analytics tool.
9.0 Summary of Findings, Limitations, Recommendations
9.1 Summary of Findings
The findings of the research are summarized as follows.
There are several data analytics tools available in the current market that include IBM
Watson Analytics, Pentaho, Clic Data, Tap Clicks and others. Each one of them has its own
unique and advantageous features that are utilized by different business organizations worldwide.
IBM Watson can efficiently determine a pattern or sequence within a specific set of data without
any difficulty. IBM also has IBMSPSS predictive analytics and data science experience tools that
further enhance the features of the data analytics tool. It also has cognos analytics in which it
provides services as hosted solution via IBM cloud. It also provides complex cloud based
services along with guidance and data prediction. IBM Watson analytics can refine, explore,
predict and assemble the data. Pentaho is another business data analytics tool that has been
designed by Hitachi. Pentaho is slightly different from IBM Watson in terms of services and
features. Pentaho mainly utilizes Big Data and its values are accelerated by NoSQL, Hadoop and
other big data. This tool integrates all the data collected on a specific subject and then classifies it
using its customization analytics tool. Clic Data acts as a data visualization and business
intelligence tool that also collects, analyzes and cleans the data according to the requirements set
by the user. It can also set up visual indicators in the data as well as KPIs and other key metrics
so that the user can easily share the result data with the clients and colleagues. Tap clicks is

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specially designed to provide marketing analysis services rather than only data analytics i.e. it is
specific to marketing data for the analytic functions. Moreover, it is also used to create a
workflow plan and order management plan that are some of the most essential parts of some
business organizations. Of all these data analytics tools, it has been found that IBM Watson is the
most popular tool and is used extensively by business organizations worldwide. IBM Watson is
one of the most intelligent tools in the market today. It helps the users to identify different
patterns in a particular data set by automatic classification and reading of the data. Moreover,
IBM Watson has support for multiple languages and hence, based on the entered user data, this
tool can read through any language and perform data analytics processes. IBM Watson has
dedicated logging database powered by ElasticSearch. This ElasticSearch provides a browser
based user interface namely, Kibana for viewing logs and exploring real time events. This feature
is very useful for the business users who can easily extract data from the events that have been
inputted into the database of the analytics tool.
Based on the findings, the main problem statements can be addressed as follows.
S1: The main answer to the first problem statement is a software driven data analytics
system. Among the many data analytics softwares available in today’s market, IBM Watson
Analytics is the recommended for the purpose.
S2: The use of IBM Watson will solve this problem as well as it has its own database
hosted in cloud server and hence, there will be no need for further use of excel sheets for data
storage.
S3: Again, IBM Watson is the best possible solution. IBM Watson can efficiently
determine a pattern or sequence within a specific set of data without any difficulty. IBM also has

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IBMSPSS predictive analytics and data science experience tools that further enhance the features
of the data analytics tool. It also has cognos analytics in which it provides services as hosted
solution via IBM cloud. It also provides complex cloud based services along with guidance and
data prediction. IBM Watson analytics can refine, explore, predict and assemble the data. In
addition to achieving vista concept dimensions’ business client through the free choice, Watson
Analytics additionally guarantees availability through representation and self-benefit usefulness.
The analysis results based on the use of TAM were as follows.
Perceived Usefulness – It has been determined that the IBM Watson will be extremely
useful in enhancing the business operations of the company as it will replace the manual
analytics with faster, efficient and automatic solution.
Perceived Ease of Use – It has also been found that IBM Watson is very easy to
understand and operate. Any person with sufficient training can expertly handle the different
features of the analytics tool.
9.2 Limitations
There are several limitations of this research that are discussed as follows.
Biasing – In spite of taking of several steps to ensure there is no opinion biasing for the
review of IBM Watson Analytics, there are several deliberate opinion biases that are still existing
within the research. This is because most of the managers who were interviewed for the personal
usage experience of analytics tool, provided biases reviews on IBM Watson. From the research,
it has been found that IBM Watson does have some unique features that help the users in
business operations. However, they did not mention that these features are also available in other
analytics tools as well. The main reasons for the preference of IBM Watson are brand value, low

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cost, guaranteed service in case of software failure and others. However, these are not
highlighted by the interviewees and they all emphasized on the fact that they chose it because it
is the most suitable and efficient analytics tool that they estimated to benefit their existing
business processes.
Low Focus on Secondary Data – As discussed earlier, lesser focus has been given on
secondary data that are collected from the different organizational data on their own analytic
tools developments. Instead, the research was conducted based on actual user experiences. As a
result, although sufficient data has been captured from the survey, real specifications of the
softwares from their own developers have not been analyzed during the course of this research.
Lack of Focus on Other Analytics Tools – The final limitation of this research is the lack
of sufficient focus on other analytics tools. It has been revealed in the research itself that there
are many other tools that have their own unique features and advantages on business systems.
However, since the main focus of this research is on IBM Watson, other tools have been mostly
ignored except summarized discussions of some of them. Hence, there is a lack of sufficient
comparison between IBM Watson and the other analytics tools like Tap Clicks, Pentaho, etc.
9.3 Recommendations
Finally, based on the overall analysis and research, the following recommendations can
be provided for the future research activities.
More Focus on Competitive Comparison – Future researchers should focus more on
competitive comparison of any analytic tool instead of focusing particularly one in order to find
more about the competitive advantage of the chosen analytics tool. This will also further explain
why one tool becomes more popular than the others at a certain period of time.

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Preparation of Broader Hypothesis for Research – Again, another thing the future
researchers should focus is the preparation of a broader hypothesis for research. This particular
research is only narrowed down to some specific aspects as it is based on a case study and the
problem statements were limited for the research.

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References
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Appendix
The questions that were used for the questionnaire survey were as follows.
1. Do you use any data analytics tool in your organization?
2. If yes, which tool do you use?
3. Please explain in short your experience about this particular tool.
4. Have you gained any benefit from this tool?
5. Please explain how this tool enhanced your existing business process.
6. Have you used any other tool before this one?
7. If yes, what was it/were they?
8. Again, if yes, why did you replace them?
9. What extra advantage are you getting from this tool than the previous ones?
10. How did you manage your organization’s data analytics department before you
started using this tool?
11. How has this experience been enhanced?
12. Do you plan to continue this tool for at least the next 5 years?
13. If yes, why? Please explain?
14. If not, why not?
15. Again, if not, which tool are you considering implementing?