2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)
2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) | 978-1-6654-2733-3/21/$31.00 ©2021 IEEE | DOI: 10.1109/ICIMCIS53775.2021.9699131
Data Integration Readiness Analysis in Merged
Telecommunication Company
1st Nugroho wibisono
Master of IT Departement
Swiss German University
Tanggerang, Indonesia
nugroho.wibisono@student.sgu.ac.id
2nd Mohammad Amin Soetomo
Master of IT Departement
Swiss German University
Tanggerang, Indonesia
Mohammad.soetomo@.sgu.ac.id
4th Marastika Wicaksono Aji Bawono
Master of IT Departement
Swiss German University
Tanggerang, Indonesia
Maraskita.bawono@student.sgu.ac.id
5th Eka Budiarto
Master of IT Departement
Swiss German University
Tanggerang, Indonesia
Eka.budiarto@.sgu.ac.id
Abstract— This study conducted analysis of the readiness of
data integration in merged telecommunication company, using
the Data Management Body of Knowledge (DAMA-DMBOK)
framework and evaluating it with COBIT. Three readiness
parameters are evaluated, namely Data Architecture, Data
Governance, and Data Quality. The analysis process uses a case
in the calculation of revenue and billing collection, which has an
impact on the achievement of cash and net income in the
company, which is the KPI for the Business Performance of the
company. From the Data Architecture Analysis, it was found
that some of the business processes were still run manually and
had not been integrated. From the Analysis of Data Governance
and Data Quality, it is found that there are differences in the
interpretation of these two parameters in the company's
internal, namely in the expert group of recording revenue
compared to the non-expert one, as well as differences in
company base employees from companies M and P. Also, it is
reviewed from the Data Quality is readier for data integration
than when viewed from Data Governance, therefore it requires
a lot of improvement.
This study aims to uncover that
companies should take further systematic and strategic steps
before the data integration process is carried out. The DAMADMBOK method obtained that Company M had the applicable
percentage of 52,98% while Company P had the applicable
percentage of 95.00%. Due to the large gap between the
perception, further investigation needs to be conducted in
possible future studies.
Keywords— COBIT 5, DAMA-DMBOK, COBIT 2019,
ISO/IEC 15504, Data Integration
I. INTRODUCTION
This research was conducted in a newly merged company.
The company is a satellite telecommunication company that
combines satellite service, ground service and network service.
This company will carry out the data integration process so it
requires an analysis of data integration readiness based on the
applicable framework, namely the DAMA-DMBOK
Framework [5].
There is a study that examines the "Challenges of
Data Integration and Interoperability in Big Data" where the
growth of large data volumes and conducts a study of the
system to be integrated is considered. [11].
In this research, we do not look at the growth of data
volume, but rather study the readiness of data integration in
the three companies that have merged which was reviewed
3rd Heru Purmono Ipung
Master of IT Departement
Swiss German University
Tanggerang, Indonesia
Heru.ipung@.sgu.ac.id
using the DAMA-DMBOK Framework. This research will
obtain information on how this data integration is carried out
in the company and will be related to the results of the analysis
of the current data integration readiness study.
II. RELATED WORK
There is a study titled Designing A data governance
structure based on the data management body of knowledge
DAMA - DMBOK Framework: A case study on Indonesia
deposit insurance corporation (IDIC), where data governance
was designed using DAMABOK. This serves as basis for the
research to review governance data using DAMABOK [1].
There is a study at BMKG to measure data quality using the
DAMA-DMBOK framework [17].
In the DAMA-DMBOK framework book, it explains how
to carry out data integration, how to process data governance
and how to assess data quality [14]. This study uses binary
pass fail and COBIT level measurement calculations in
conducting assessments such as in the Manage Assurance for
Continuous Improvement Strategy in Rural Internet Service
Case Study [13].
III. METHODOLOGY
This research is done by using qualitative and quantitative
methods. For the qualitative methods, Gap Analysis and
Binary pass-fail are used. The framework used, in Gap
Analysis and Binary Pass-Fail, is DAMA-DMBOK, and
analysis of agreement level with the one applied in the
company is done by using COBIT and ISO 15504 as
references for its measurement [16]. For the quantitative
method, a Likert questionnaire is used with a certain scale to
search the data from the related respondents.
A. Methodology Diagram
Fig. 1. Methodology Diagram
The methodology of this research as can be seen in Figure
1, in the process, is started with a literature review. The result
of the literature review will be followed by the process of
interview with the expert taking care of the job. Like being
illustrated in the Table 1. The result of the interview will be
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317
2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)
written in the Table 1. below. The main consideration of using
the parameter of data governance and data architecture is that
according to the framework reference of DAMA-DMBOK
[14]. Data Governance is the foundation of the whole process
in data management, to govern the transformation rules and
message structure and data architecture are for the designing
solutions in data integration processes [14]. Data Governance
need to Analysis and review about activity [2]. Data Quality
is needed in data integration because it is directly related to
Data Governance in the DAMA-DMBOK framework. And
one of the objectives of this research is to prepare the system
to produce effective business performance monitoring [4].
data integration. The results of the interview with the expert
users involved in data integration process are seen on Table 1.
where there is the interview column with the expert, the
activity column that explains the interview conducted, and the
result column that includes result of the interview.
From the interview result, it is gathered that the
implementation of business process in the company has not
fully employed system integration. The processes that are not
fully integrated are revenue calculation, billing calculation,
and calculation of company performance management. To
proceed with improving data integration, the company
conducts an interview and questionnaire found on Table 1.
In this research, to carry out the gap process, the analysis
of compliance with DAMA-DMBOK uses best practices
recommended by COBIT 5, namely using ISO / IEC 15504.
COBIT is an IT governance framework that is used as a
reference by IT Management which is sourced from IT
management. governance best practices [13]. IT governance
and management itself is a discipline aimed at ensuring the
implementation of IT or IT-based investment has optimal
value for the organization in terms of benefits, risks, and
utilization of resources. In COBIT 5, there is a standard level
measurement that uses ISO / IEC 15504, which can be used
for various purposes in terms of measuring conformity to other
standards [8]. The COBIT 5 ISO / IEC 15504-based
assessment approach continues to facilitate the following
objectives that have been a key COBIT approach. In COBIT
5 using ISO IEC 15504 approach [16], using 4 levels as
follows:
Not achieved (N) level, where is little or no evidence of
achievement (0 to 15 percent achievement, Partially achieved
(P) level where there is some evidence of an approach to, and
some achievement of, the defined attribute in the assessed
process. Some aspects of the achievement of the attribute may
be unpredictable. (15 to 50 percent achievement), Largely
achieved (L) level, where there is evidence of a systematic
approach to, and a significant achievement of, the defined
attribute in the assessed process. Some weaknesses related to
this attribute may exist in the assessed process. (50 to 85
percent achievement) and lastly, Fully achieved (F) level,
where there is evidence of a complete and systematic approach
to, and full achievement of, the defined attribute in the
assessed process. No significant weaknesses related to this
attribute exist in the assessed process. (85 to 100 percent
achievement). References from the 2019 Cobit regarding the
Quality Criteria of Information are also considered In making
the questionnaire.
After questionnaire has been carried out, validation
process takes place through Focus Group Discussion (FGD).
This process gathers experts in the company to validate
questionnaire results.
IV. RESULTS AND DISCUSSION
In this study, interviews interviews with experts who
work on business processes related to revenue recording and
billing collection are carried out to find out what activities lack
TABLE II.
TABLE I.
Intervieweer
INTERVIEW TABLE
Activity
Result
VP BillCO
Interview Bispro Revenue
Assurance and Billing
workflow
Revenue
Information
AVP CP
Interview Workflow Bispro,
Workflow Business
Perfomance Data
Visualization
Information Data
Visualization
Revenue,Ebitda,Net
Income
Mgr Coms
Interview Workflow Bispro
Revenue
Information Order
until Revenue
Closing
Mgr Col
Interview Workflow Revenue
and Collection
Detailing Order
until Closing
Revenue
AVP Finance
Interview Finance Workflow
Detailing SAP
Process and
Financial Report
The first data is taken by using a questioner to produce
binary pass-fail data. In the questioner, there are 24 questions
consisting of 12 questions related to Data Quality and the
others related to Data Governance. The questions are based on
DAMA-DMBOK agreement criteria as in the enclosed table
[14]. The questioner is divided into two parts: the first one is
searching for data from the expert user to get absolute
information on whether quality and governance data are really
applied in the running system or application. After having the
data, it is continued with the process of taking the next data,
that is, searching qualitative and quantitative data related to
the achievement of agreement illustrated in Likert scale
ranging from 1 to 5. Number 1 indicates very
ineffective/inconsistent with score 20%, number 2 = less
effective/consistent
40%,
number
3
=
quite
effective/consistent 60%, number 4 = effective/consistent
80%, number 5 = very effective/consistent 100%. Score
translation is needed because in COBIT 5, and there are four
scales for maturity level measurement: scale below 15% is
“not achieved”, 15% until 50% is "partially," above 50% until
85% is "largely," and above 85% is" fully. The results of the
questionnaire for the data quality category can be seen in the
Table 2.
DATA QUALITY CATEGORY QUESTIONNAIRE
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2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)
No
1
2
3
4
5
6
7
8
9
10
11
12
Objectives
Data Criticality about
important data for
consumers and the
company
Data Lifecycle
Management about data
management cycle
Data Prevention about
data error prevention
process
Data Root Cause
Remeditation about
data error causes
Data Standard Driven
about data quality
commitment
Data Source about
quality of data
resources
Master Data about
company's master data
Data Standard Driven
about having data
quality commitment
Data Objective
Measurement and
Transparency about
data quality
measurement
Data Embedded in
Bussiness Process
about data quality in
every workflow of
business process
Data Systematically
Enforced about
systematically
producing good data
quality requirements
consistency
Data Connected to
service levels about
quality data report that
integrated with Service
Level Agreement of
Company and
Consumers
Applicable
Scale
Applicable
Percentage
Achievement
COBIT 5
Classification for
Applicable
Achievement
Consistency
Scale
Consistency
Percentage
Achievement
COBIT 5
Classification for
Consistency
Achievement
8
66,67%
Largely
1540
64,17%
Largely
9
75,00%
Largely
1360
56,67%
Largely
9
75,00%
Largely
1460
60,83%
Largely
9
75,00%
Largely
1500
62,50%
Largely
12
100,00%
Fully
1980
82,50%
Largely
11
91,67%
Fully
1860
77,50%
Largely
9
75,00%
Largely
1640
68,33%
Largely
11
91,67%
Fully
1620
67,50%
Largely
7
58,33%
Largely
1040
43,33%
11
91,67%
Fully
1680
70,00%
Largely
10
83,33%
Largely
1620
67,50%
Largely
7
58,33%
Largely
1100
45,83%
Partially
Partially
The results of the questionnaire for the data governance category can be seen in the Table 3.
TABLE III.
No
1
2
3
4
Objectives
Data Strategy about
company's
communication of data
governance strategy
importanity
Data Policy about
policies for data
management in
company’s system
information
Data Oversight about
review, correction and
audit process
Data Standard and
Quality about data
quality commitment
DATA GOVERNANCE CATEGORY QUESTIONNAIRE
Applicabl
e Scale
Applicable
Percentage
Achievement
COBIT 5
Classification for
Applicable
Achievement
Consistency
Scale
Consistency
Percentage
Achievement
COBIT 5
Classification for
Consistency
Achievement
9
75,00%
Largely
1480
61,67%
Largely
10
83,33%
Largely
1700
70,83%
Largely
6
50,00%
Partially
1409
58,71%
Largely
9
75,00%
Largely
1720
71,67%
Largely
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319
2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)
Objectives
No
5
6
7
8
9
10
11
12
Data Standard and
Quality about company's
data architecture
Data Standard and
Quality about data
architecture commitment
Data Oversight about
company's data
governance committee
Data Policy about
approval in information
system
Data Issue Management
about company’s data
glossary
Data Strategy about
information system to
company's policies /
business process
Data Compliance about
compliance fulfillment
of information system's
data
Data Asset Valuation
about company’s
business value to
information system's
data
TABLE IV.
Applicabl
e Scale
Applicable
Percentage
Achievement
COBIT 5
Classification for
Applicable
Achievement
Consistency
Scale
Consistency
Percentage
Achievement
COBIT 5
Classification for
Consistency
Achievement
7
58,33%
Largely
1360
56,67%
Largely
6
50,00%
Partially
1140
47,50%
Partially
6
50,00%
Partially
1340
55,83%
Largely
9
75,00%
Largely
1760
73,33%
Largely
5
41,67%
Partially
960
40,00%
Partially
9
75,00%
Largely
1360
56,67%
Largely
6
50,00%
Partially
1040
43,33%
Partially
8
66,67%
Largely
1160
48,33%
Partially
QUESTIONNAIRE VALIDATION WITH COBIT 2019
Variable Information
Reference Model COBIT 2019
Objectivity
Completeness
Availability
Accuracy
Restricted Access
Interpretability
R6 as IT Developer, R7 as Expert Asset Management, R8 as
Expert Revenue System, R9 as Expert Billing 1, R10 as
Expert Collection, R11 as Expert Accounting, and R12 as
Expert Business Performance. Table 3 represents a summary
of all the processes of GAP Analysis that have been done
during the research by classifying the COBIT levels into 4, in
which Level 3 is further broken down into two separate
categories. The next part of this paper will explain the analysis
results in depth. The first analysis process was observing
results from our questionnaire, which revealed two different
categories of respondents, which were experts involved in
revenue calculation and experts involved in company base.
Total
9
2
2
8
1
2
Table 4 analyzes the suitability of the variables in the
questionnaire with the effects adjusted to the 2019 COBIT
Information Reference Model standard.
TABLE VI.
A. Respondent GAP Analysis
TABLE V.
RESPONDENTS RESULT (CB= COMPANY BASE, RV =
INVOLVED IN REVENUE PROCESS, BPF = BINARY PASS FAIL, LS = LIKERT
SCALE)
No
Position
1
2
3
4
5
6
7
8
9
10
11
12
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
BPF
Score
24
24
15
21
11
16
10
21
6
24
21
10
LS Score
CB
RV
1760
2240
740
1400
1100
1209
580
1680
280
1960
1160
880
P
P
M
M
M
M
M
P
M
P
P
M
Yes
No
No
No
No
No
No
Yes
Yes
Yes
No
No
RESUME RESULT GAP ANALYSIS AND CLASSIFICATION
BASED ON COBIT 5 – ISO 15504
Categ
ory
Ran
ge
(%)
COBIT 5
Classifica
tion
1
0-15
2
1650
5185
86100
Not
Achieve
Partially
3
4
Largely
Fully
COBIT 5
Applicable
Number
COBIT 5
Consistency
Number
Data
Qual
ity
Data
Governa
nce
Data
Qual
ity
Data
Governa
nce
0
0
0
0
0
5
2
4
8
7
10
8
4
0
0
0
In table 6 we can see that the results of COBIT 5 show a
comparison of the gap analysis between Data Quality and
Data Governance.
From Table 5, it is gathered that respondents are presented
as follows; R1 as Expert Order Management, R2 as Expert
Fulfillment Delivery 1, R3 as Expert Fulfillment Delivery 2,
R4 as Expert System Provisioning, R5 as IT Enterprise Expert,
In the next step of our GAP analysis, parameters based on
the DAMA-DMBOK framework will be analyzed. [13] The
first one is data quality; the second is data governance; both
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320
2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)
affect the data integration process that will take place. Then,
the effectiveness of the data management system applied will
be analyzed based on the questionnaire results. There are two
questionnaires, and the second questionnaire applies to those
who answered ‘yes’ during the first questionnaire. After the
second questionnaire, an interview will be validated, which
will be validated using Focus Group Discussion (FGD), along
with related expertise.
Person
Applicable
Number
Applicable AVG
Applicable
Percentage (%)
Consistency
Number
Consistency AVG
Consistency
Percentage (%)
QUESTIONNAIRE RESULT AND OBSERVATION
Cases
TABLE VII.
effectiveness of data lifecycle management, which is
included in the Availability category of COBIT 2019 so that
the integration process becomes more mature.
2) Data Governance Analysis: From the results of the
governance data analysis, it appears that the readiness of data
integration in terms of Data Governance data has not yet met
the DAMA-DMBOK framework standard[1]. From the total
classification results, there are four variables that still have
to be adjusted to the standard DAMA-DMBOK framework.
These four variables are categories of Objectivity,
Interpretability, Availability, and Completeness [9]. This
means that the company should make a lot of improvements
in terms of Data Governance before the data integration
process is carried out. Judging from the effectiveness of the
implementation of Data Governance in the company, it still
does not meet the DAMA-DMBOK framework standards,
especially on the 4 variables, which are the categories of
Objectivity, Interpretability, and Accuracy in COBIT 2019,
and the lowest effectiveness is Interpretability [9].
M CB
P CB
RV
NON RV
7
5
4
8
89
114
75
128
12,71
22,80
18,75
16,00
52,98
95,00
78,13
66,67
6189
8800
5680
9309
884,14
1760,00
1420,00
1163,63
36,84
73,33
59,17
48,48
V. VALIDATION RESULT
From table 7 above, the two classifications from 4
categories based on the analysis are shown. The results of the
questionnaire obtained the results of the analysis, namely the
different interpretations of the respondents on the Governance
Data and Data Quality Parameters. We find that respondents
from experts working in departments directly related to
revenue recording have a score gap analysis of 78.13% (Fully
Level) compared to not working in revenue recording of
66.67%. The BPF Score for RV in almost all positions were
high. However, the R9 is an anomaly because it is low. The
revenue has been audited externally every year and needs to
be reported to the Board and Holding. However, for the non
RV, they observe from the system processes and information
where it isn't running perfectly. Therefore, that is why they
give a low score. The table shows that the GAP score of the
application of governance data and quality data for the expert
of company P is higher until 95,00% (Fully Level) than the
one of company M that is only 52,98% (Largely Level).
Employees in Company P scored higher because, from the
respondents' discussion, they feel that the standard documents
and the specific groups of the company have indeed
represented the requirements of the DAMA-DMBOK
framework. Whereas employees in Company M scored lower
because they were looking for definitive answers, they did not
find the parameters implemented in the company.
From the results of the analysis of several parameters for
the readiness of the integration data above, the next is the
validation process. In this case, the validation process is
carried out using a focus group discussion, or FGD and final
interview, followed by stakeholders who need
Data
Integration , namely Commerce, Billing Collection, Revenue
Assurance, and Delivery Assurance, and Last Interview for
Corporate Performance. The results of the FGD are that all
stakeholders agree on the results of the analysis in the study,
namely related to Data Architecture, Data Governance, Data
Quality, and the findings of case interpretations of Data
Integration parameters and variables. Also, it provides several
research recommendations that will be submitted in the
discussion of recommendations and solutions.
VI. CONCLUSION
From the research, it is found that DAMA-DMBOK
framework is proven to be a reference for analysis of data
integration readiness in a Merged Telecommunication
Company with the number of findings and the results of the
analysis, namely findings related Data Quality, Data
Governance. From the analysis, we know that Company M
has a perception of the implementation of data governance
and data quality. Company M has an applicable percentage of
52.98% and a Consistency percentage of 36.84%. On the
other hand, Company P has an applicable percentage of 95%
and consistency percentage of 73.33%, the Revenue
department has an applicable percentage of 78.13%, while the
Non-Revenue department has an applicable percentage of
66.67%.
B. Data Quality and Data Governance Analysis
1) Data Quality Analysis: From the results of data
quality analysis it appears that in the company, data
integration readiness in terms of data quality has followed the
DAMA-DMBOK framework. There are 8 Largely variables
and 4 Fully variables from the number of classification
results. Reviewed in the 2019 COBIT standard, the Accuracy
data variable is ready for data integration. It still needs
improvement in terms of Objectivity, seeing from the GAP
Analysis, which is still far from conformity, namely the
Objective Measurement and Transparency and Information
variables connected to the Service Level Agreement. This
also applies when viewed from the effectiveness of
implementing data quality in the company; it's just that its
level of effectiveness of Data Quality has not reached Fully,
meaning that it needs improvement in terms of effectiveness.
From the analysis results, it is also necessary to improve the
From the data governance and data quality readiness
analysis, it is evident that the data integration process is ready
to be carried out from the data quality aspect compared to the
data governance, because the Data Quality has been measured
to be on the Largely Achieved level while the Data
Governance is still at the Partially Achieved level.
From the results of the analysis, it is found that the data
governance parameters need to be improved in terms of
compliance with the DAMA-DMBOK framework and the
effectiveness of its implementation by the results of the
analysis to all stakeholders, and data quality requires a little
improvement in terms of effectiveness in its implementation
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and data architecture emphasizes more on the integration of
application systems used.[14]
From the validation results, it can be concluded that the
integration of data and systems is needed by all stakeholders
and is very helpful in increasing the acceleration of business
performance monitoring and having an impact on support
expanding the company's net income [4].
VII. RECOMMENDATIONS
There needs to be further research conducted towards the
data integration process that takes a more in-depth approach
to the system's data modeling and design. This will be done
by paying more attention to the technical aspects of Data
Integration and Interoperability [14].
After improving the process in Data Governance and
Data Quality, it is necessary to carry out a further review
analysis involving staff and not focusing on the experts.
Bottleneck process towards data architecture revenue that
has been observed during this research may be continued
with another research regarding the improvement of system
automation processes [12].
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