Harmonization and Consolidation of Initial Quality Data to Generate Key Performance Indicators

This presentation discusses the importance of harmonization and consolidation of initial quality data for generating Key Performance Indicators (KPIs) in the automotive market. It covers the concept of Initial Quality World, data harmonization, data consolidation, key performance indicators, and vehicle issues. The presentation also includes research methodology, research philosophy, research approach, research design, and data collection and analysis methods.

Harmonization and Consolidation of Initial
Quality Data to Generate Key Performance
Indicators

Chapter 1:
Introduction
Research aim
• The aim of this paper is to facilitate as well as consolidate,
the process in one structure system.
• This paper aims at avoiding the discrepancy in the IQW
deliverables.
• The research conductor has tried to fulfill the research aim.

Objectives
• To identify the problems in consolidating and
harmonizing the Initial Quality Data
• To consolidate the process in one structured
system for avoiding discrepancies in IQW’s
deliverables
• To develop an efficient tool for predicting the
harmonized and consolidated data for future
years
• To generate Key Performance Indicators for the
automotive market

Research questions
• What are the problems in consolidating and
harmonizing the initial quality data?
• How to develop a structured system for
disengaging the IQW’s deliverables?
• How to develop an efficient tool for
predicting the harmonized and consolidated
data for coming years?
• What are the Key Performance Indicators in
the automotive market?

Definition
• Harmonization of data is considered a
matching process of the new resources
with the existing master record.
• Steering committee tempts the business
idea.
• The new harmonized and consolidated
system users are classified by the
utilization of Quality Management tools.

Initial Quality World
• IQW is approved as an essential process of
measuring the problems of the ownership of
vehicles.
• IQS has been considered software helps in
offering all manufacturer types in the entire
world.
• Automobile companies get assisted by IQW
as it helps in assessing the customers’
review on a regular interval of time.

Key Performance Indicators
• KPIs assist a business organization to
identify the major problems that
consumers are facing
• Reporting an appropriate KPI condition is
considered very crucial for a business
organization.
• Important KPIs need to be tracked by the
automotive industry.

Motivation
• Data consolidation and data harmonization
are necessary for the IQW to implement the
advanced system of Information
Technology.
• Data consolidation is approved as
necessary to drive the customers’
experience.
• Data security is considered important to
reduce to advanced technical problems.

Rationale
• This thesis paper has stressed data
harmonization and data consolidation of IQW
that can help in generating Key Performance
Indicators.
• Numerous problems have been confronted by
the owners of the vehicles have been analyzed
and incorporated in this thesis paper.
• Thus, the future studies will get assisted in
conducting any kind of research in accordance
with this topic of research.

Summary
• This chapter of the research study has
provided the constructive concept of IQW,
along with its importance.
• The concept of data harmonization along
with data consolidation has been also
screened in this chapter of the thesis paper.
• Moreover, this research study has discussed
how IQW helps in generating KPIs.

Chapter 2:
Literature Review
Introduction
• This chapter of the thesis paper is considered
important to conduct the study based on the
provided topic of research.
• This chapter of the thesis paper has provided
secondary data in accordance with the research
topic.
• The information incorporated in this thesis paper
can assist the conductor of research in many ways.

Concept of Initial Quality Data
• The problems of ownership of vehicles
can be measured with the help of IQW.
• This initial quality data can help in
bringing improvement in the products
and services of the companies under the
automobile industry.
• In addition, the consumers’ satisfaction
can be escalated with the help of IQW.

Harmonization of Initial Quality Data
• The usefulness of data of a business
organization can be explained with the
help of data harmonization.
• The process of data transformation can
get easier by data harmonization
(Conway et al. 2014).
• Deployment of new data can be quicker
by this process.

Benefits of data harmonization
• It will improve the organisational process
significantly
• The efficiency of organisational process
can be controlled with the help of data
harmonization.
• It also helps in improving customer base.

Consolidation of data:
Data consolidation is a process where the
several raw data are collected from
various sources and then transformed
into the usable data. The three steps of
data consolidation are as follows.
• Data propagation
• Data replication
• Data federation

Importance of data consolidation:
• The process of data consolidation is utilized to
enhance the level of efficiency of both the
employees as well as organizational
performance.
• This data consolidation method helps in
providing effective and usable data to the users.
• It also helps in the preservation of the previous
records so that in future the data can be utilized
by both any individual and any organization.

Issues of data consolidation:
It is mentioned that during performing the data
consolidation method some organization
experience few problems. These problems
are mentioned as follows.
• Due to the poor network connection, the
methods of data consolidation are hampered.
• Web services are reduced.
• All the data cannot be transferred into the
database due to the problem in the network
connection.

Data standardization:
• Data standardization is a method by which
the Initial quality data analysis process is
enabled.
• This process helps in enduring the data
credibility in a scientific manner. It is very
tough to perform this process.
• In this process, the data is brought out into a
standard data format.
• This process is also considered as variable
which helps in the rescaling of the results of
data analysis (Wang, Kung & Byrd, 2018).

Concept of key performance indicators:
The other name of key performance indicators is the
progress indicators which are utilized to measure
the efficiency level of both employees and
organizational performances. The advantages of
the KPI are as follows.
• The timeframe, behaviors, governance,
performance, compliance, and efficiency of
any particular project are tracked by the
KPI.
• The business goals are also measured by
the KPI.

Vehicle issues:
It is seen that the vehicle customers experience
several car related problems while using cars.
These vehicle problems are mentioned below.
• Car cooling and heating problems.
• Car seat related problems.
• Problems regarding driving experience.
• Engine or transmission problems.
• Pro9blems in external and internal features of the
car.
• FCDE related problems in car.
• ACEN

literature Gap:
This proposition paper depends on
information harmonization and
information combination of Initial Quality
Data for producing KPI or Key
Performance Indicators in the car
business. Furthermore, the examination
conductor additionally has talked about
a gainful correlation between the
information harmonization and
information institutionalization.

Summary of the literature review
• Data consideration and Data
harmonization are needed for
generating the KPIs
• Data standardization process should
also be considered

Chapter 3: Research methodology:
Research methodology is an essential part
of the research study. In this section of
the study, the details of all the techniques
and tools are informed and it is
mentioned that these techniques and
tools are used for gathering the authentic
data.

3.1Problem statement:
The actual problem of this thesis is that the
data construction has not been done in a
proper manner thus the key performance
indicators can be generated in a significant
way. Under this problem there are several
subproblems which are mentioned below.
• Customized products marketing solution
has not been constructed.
• Organization becomes unable to handle
the big data.

Problem and Results
• Current Problem
• Discrepancies in the gathered data from
multiple market research has been found
• Delta and multiplication method have
been used as prediction methods for
data consolidation and harmonization
• SteCo list is focused on the USA and
China market only, where IQW and
prediction tool focused on the vehicles
with feedback from all market and
feedback from specific market
significantly
• Result of harmonization and
consolidation
• By applying Delta prediction method
2.9 pph has been identified from the
Steco List
• Waterfall methodology has been
applied
• 4.2 pph has been identified from the
expectation result from IQW
• Therefore, 2.6pph can be predicted by
applying delta method adequately

Waterfall chart of SteCo
• From the above chart
the data from the year
2016 to 2018 is
consolidated
• Hence, gathered data at
the end date is
harmonized
Start
Mar-16
Jun-16
Sep-16
Dec-16
Mar-17
Jun-17
Sep-17
Dec-17
Mar-18
Jun-18
Sep-18
End
0
1
2
3
4
5
6
7
8
9
10
Waterfall Chart of SteCo List
Rise
Fall
Year
PPH

Waterfall chart Expectation (IE)
• Data from 2016-2018 is
selected for
consolidation
• The data of end date
has been harmonized in
this context
Start
Mar-16
Jun-16
Sep-16
Dec-16
Mar-17
Jun-17
Sep-17
Dec-17
Mar-18
Jun-18
Sep-18
End
0
2
4
6
8
10
12
14
16
Waterfall chart Expectation (IE)
Fall
Base
Year
PPH

Waterfall chart (Prediction)
• Data from 2016-2018 is
collected for
consolidation
• The end date has been
considered for data
harmonization
Start
Mar-16
Jun-16
Sep-16
Dec-16
Mar-17
Jun-17
Sep-17
Dec-17
Mar-18
Jun-18
Sep-18
End
0
1
2
3
4
5
6
7
Waterfall chart (Prediction)
Rise
Fall
Year
PPH

Harmonized and consolidated data
• 47 pph has been selected
from the IQW list
• Expected result of the IQW
by applying prediction
method is the major
reason behind selecting
the data SteCo IQW Delta method
0
5
10
15
20
25
30
35
40
45
50
Consolidated data
pph Series2 Series3

3.2 PLANNING:
In this section the utilization of the
appropriate tools and technique
is planned. This section is
performed by the researcher of
the study.

3.21 Research method:
In this part the brief
information of the
research tools and
techniques which are
utilized in this study
is informed.

Research onion:
Research onion is very
important element of the
research study. It is identified
that like onion in research
onion there are several
layers and every layer of the
research onion provides
information about any
specific topic. In the research
onion 6 different layers are
seen (Mayer, 2015).

Research Philosophy:
Mainly four types of research
philosophies are used in the
research studies (Mebius,
Kennedy& Howick, 2016). These
research philosophies are
mentioned below.
• Positivism research philosophy.
• Pragmatism research philosophy.
• Interpretivism research
philosophy.
• Realism research philosophy.
RESEARCH
PHILOSOP
HY
Posi
tivis
m
Prag
mati
sm
Inter
preti
vism
Reali
sm

Research approach:
This is a very essential section of the study, as based
on the research approach the data collection and data
analysis process is constructed (Page, 2016). The
research approach is categorized into three main
types which are mentioned below.
• Deductive research approach.
• Inductive research approach
• Abduction research approach.
RESEARCH
APPROACH
Deductiv
e
Inductive
Abductio
n

Research design:
This part of the research study helps
the researcher to construct the
research study in a proper manner.
IN the research design all the
strategies are mentioned which are
require for constructing the research
study (Marczyk, DeMatteo &
Festinger, 2017). The research
design is categorized into three
types which are mentioned below.
• Exploratory research design
• Explanatory research design
• Descriptive research design

Scope of Primary and Secondary Data
• Harmonization of data
• Consolidation of data
• Authenticity

Data collection method
Primary Data collection
• Questionnaire
Secondary data collection
• Content analysis
prima
ry
data
collec
tion
secon
dary
data
collec
tion
data
collec
tion
metho
d

Data analysis
• Primary quantitative data has been
analysed
• Secondary qualitative data has been
evaluated for this thesis

Primary Data analysis
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .938
Bartlett's Test of
Sphericity
Approx. Chi-Square 2127.455
df 45
Sig. .000

Primary Data analysis (CONTD..)
Total Variance Explained
Compone
nt
Initial Eigen values Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
1 9.427 94.271 94.271 9.427 94.271 94.271
2 .246 2.457 96.729
3 .123 1.226 97.954
4 .076 .760 98.714
5 .040 .401 99.115
6 .035 .355 99.470
7 .020 .201 99.670
8 .015 .155 99.825
9 .012 .115 99.940
10 .006 .060 100.000
Extraction Method: Principal Component Analysis.

Primary Data analysis (CONTD..)
Rotated
Component
Matrixa
a. Only one
component
was extracted.
The solution
cannot be
rotated.

Primary Data analysis (CONTD..)
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .914
Bartlett's Test of
Sphericity
Approx. Chi-Square 1100.065
df 15
Sig. .000

Primary Data analysis (CONTD..)
Correlations
HVAC engine ACENsystem FCDsystem exteriorsystem carinterior
HVAC
Pearson Correlation 1 .906** .919** .881** .859** .820**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 81 81 81 81 81 81
engine
Pearson Correlation .906** 1 .990** .986** .966** .918**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 81 81 81 81 81 81
ACENsystem
Pearson Correlation .919** .990** 1 .976** .961** .914**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 81 81 81 81 81 81
FCDsystem
Pearson Correlation .881** .986** .976** 1 .962** .933**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 81 81 81 81 81 81
exteriorsystem
Pearson Correlation .859** .966** .961** .962** 1 .894**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 81 81 81 81 81 81
carinterior
Pearson Correlation .820** .918** .914** .933** .894** 1
Sig. (2-tailed) .000 .000 .000 .000 .000
N 81 81 81 81 81 81
**. Correlation is significant at the 0.01 level (2-tailed).

Secondary Analysis
Key findings
• The premium brands are not better than
the non-premium brands
• Domestic cars are better than imported
cars
• Korean brands are better than the other
car brands
• The automobile industry becomes able to
improve PP100 by 6% over last year (JD
Power, 2016)

Discussion of the findings
• IQS score of premium brands are
inferior to the non-premium brands
• Premium-brands offer the airbags
blind-spot monitor, traction control,
electronic stability control, and many
more facilities to the vehicle
customers
• IQS score of the import brands have
been reported is 99 PP100

Recommendations
• The automobile companies should
focus on hiring professionals for better
service
• Companies are suggested to consider
updated technologies for improving
the vehicle service
• Improvised materials should be
provided at the time of delivery

References
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Management, 31(1), 1-22.
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costs of key low-carbon energy technologies: Harmonization and aggregation
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dependence, 141, 153-158.
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stats>
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To be conti…..
• Parmenter, D. (2015). Key performance indicators: developing, implementing, and using winning KPIs.
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• Varasteh, A., & Goudarzi, M. (2017). Server consolidation techniques in virtualized data centers: A
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• Yan, R., Bundy, K., Law, D. R., Bershady, M. A., Andrews, B., Cherinka, B., ... & Thomas, D. (2016). SDSS-IV
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