Benefits of the Enterprise Data Governance in Industry:

A Qualitative Research

Rodrigo Prado

, Edmir P. V. Prado

, Alexandre Grotta

and Andre Montoia Barata

EPAM – Enterprise Software Development, Design and Conculting , Krakow, Poland

IS Post-graduation Program (PPgSI), University of São Paulo (USP), São Paulo, Brazil

CitiBank, Data Governance Department, São Paulo, Brazil

Keywords: Data Governance, Consulting Services, Case Study.

Abstract: Data governance policies and procedures (DGPP) ensure proactive and efficient data management within the

Enterprise context. Thus, DGPP corporate projects may result in positive impacts – we name benefits – to

these companies. However, we found few studies reporting these benefits in-developing countries. Another

gap is no evidence of a DGPP benefits model. Given this context, we first created a DGPP benefits model

(DGB-M) via a Systematic Literature Review; we planned and conducted case studies at four different

Brazilian industry sectors: agribusiness, fertilizers, automotive, and logistics. As main results, we have: (i)

The DGB-M itself; (ii) evidence that 62% of the processes described by DGB-M were implemented by these

four cases; (iii) evidence that 68% of the DGB-M benefits expected were achieved by these cases; and (iv)

cases lessons learned. These results are highly relevant to forecast the benefits and challenges of future DGPP

projects.

1 INTRODUCTION

Contemporary organizations are fast adopting Data

Governance (DG) policies and procedures (DGPP)

given data is a fundamental company asset, which

supports from daily operations and up to strategy

decisions. Enterprise data is been considered one of

the most critical assets of a company (Yebenes and

Zorrilla, 2019). Data is a vital backbone that may be

scaffolded by the DG and its DGPP, towards

proactive and efficient data management (Dasgupta,

Gill, & Hussain, 2019).

DGPP are implemented via frameworks, in which

the expected results are positive impact – what we

name benefits – to these organizations (Haider &

Haider, 2013). However, were found a gap given that

few studies investigated the benefits right after the

adoption of these DGPP. Even further, we found no

consolidate model to bound DGPP and their expected

benefits.

https://orcid.org/0000-0003-0315-8391

https://orcid.org/0000-0002-3505-6122

https://orcid.org/0000-0003-2549-138X

https://orcid.org/0000-0001-6815-2252

Anyhow, towards the adoption of these

frameworks, companies are expected to face many

challenges, such as partial framework adoptions,

based usually on customers’ priorities and

requirements. As a natural consequence, frameworks

should consider partial adoptions as a possible result,

thus it should also consider what to prioritize the

implementation of DGPP (Otto, 2011a). Within this

context, the main goal of this study is to analyze four

DGPP projects occurred in Brazil as well as its

benefits. To achieve the research's main goals, we

created the DG Benefits Model (DGB-M).

Thus, there are two mains objectives: (1) describe

the DG processes implemented by Brazilian

organizations and the benefits obtained, and (2)

analyze the benefits obtained with partially or fully

implementation of DG processes.

Prado, R., Prado, E., Grotta, A. and Barata, A.

Beneﬁts of the Enterprise Data Governance in Industry: A Qualitative Research.

DOI: 10.5220/0010418606990706

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 699-706

ISBN: 978-989-758-509-8

699

2 THE DATA GOVERNANCE

BENEFITS MODEL (DGB-M)

The DGB-M emerged as a result of a systematic

literature review (SLR). We first describe the SLR

base concepts and the SLR methodology. We then

collected all the DG Frameworks from the SLR

results. Then, we clustered the SLR results into two

main groups: DGPP and DG benefits. DGPP and DG

benefits are detailed in Subsection 2.3.

2.1 SLR Base Concepts and Method

A base concept is the DG itself, which might be

defined as a system of accountabilities and decision

rights for processes related to information within an

organization (DGI, 2015). Two other base concepts

used by this SLR are Data Governance Office

(DGO) and data stewardship. DGO is defined as a

group of people that provide general support to

various fields of information and data-driven tasks

within the corporate operations. Data stewards is a

person that specializes in the data fields. He/she

follows after data policies, practice oversight, and

process (Thammaboosadee and Dumthanasarn,

2019; Rosenbaum 2010).

DG is the main subject of the Data Management

Association (DAMA, 2009), an organization that

promotes best practices of information and data

management across the Globe. DAMA is most

known by its framework, the Data Management

Body of Knowledge (DMBoK). The DMBoK

provides an overview of data management through

the definition of standards, terminologies, and best

practices.

Based on these concepts, this SLR was carried

out as described by Kitchenham et al. (2009). The

study addressed the following research questions:

(1) Which DG frameworks are available in the

scientific literature? (2) What are the benefits of DG

processes and practices? In sum, we consolidate DG

processes and practices and their expected benefits

into a model we named DGB-M as seen in Figure 1.

This SLR included English researches only

published between 2005 and 2019 found at the IEE,

ACM, and SCOPUS databases. We limited

researches to academic journals and conferences in

the information systems (IS) field only that we

peer/expert reviewed. The SLR was finished on Feb.

2020. Our final list of 31 articles found the

frameworks described as follows.

2.2 DG Frameworks

In addition to the DMBOK framework, other DG

frameworks to meet specific contexts have been

reported. Some of them are enhancements of DMBoK

such as (Aisyah & Ruldeviyani, 2018). The SLR

identified sixteen studies reporting DG frameworks.

We grouped them by context as follows: Big Data,

Cloud, Public Sector, and Other Sectors.

2.2.1 Big Data Governance Frameworks

A big data governance framework for healthcare data

was reported regarding health information yet useful

for industries non-healthcare industries. For instance,

it can be used to analyze the risk factors in advance,

preventing prevent issues and problems, and actuate

at these frameworks' limitations level (Al-Badi,

Tarhini and Khan, 2018; Li et al., 2019).

2.2.2 Cloud Data Governance Frameworks

Monolithic Enterprise Applications have been

transformed integrated, thus they are been connect

into what has been named Enterprise Service

Ecosystems. These ecosystems modularize business

rules and expose these rules as services. These

services at then hosted via Cloud Infrastructure, and

event Internet of Things devices should be thus

subjected to a Cloud Data Governance Framework. A

framework proposed by (Shrivastava and Pal, 2017)

spans across various Data Centers and Cloud

Infrastructure resources for instance. A common

agreement among these studies is that the gap

regarding Cloud Data Governance Framework still is

an open question.

2.2.3 Data Governance Framework in the

Public Sector

In in-development countries, personal data privacy

is the role and responsibility of government

agencies. As the first sample, there is a legal gap in

Thailand government data (Thammaboosadee and

Dumthanasarn, 2019) proposed a new framework

for Thailand open government data. Another

emerging problem in in-developing countries Public

Sector is the large data amount. Aisyah and

Ruldeviyani (2018) reported that Indonesia

Insurance Institute requires much data from their

users. They also reported data governance and

management structure based on the guidance of

DMBoK to accommodate these challenges.

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Figure 1: The Data Governance Benefits Model (DGB-M).

2.2.4 Other Frameworks

There have been reported other frameworks for DG

general context, such as: DG maturity (Haider &

Haider, 2013); DG frameworks unification (Liaw, et

al., 2014); Framework for data governance which can

be used to focus on DG relevant issues, (Khatri, and

Brown, 2010); and literature and practice topics

complementation (Otto, 2011a).

There have been reported also DG-specific

context frameworks, such as elderly citizens

(Dahlberg, 2014), new-generations platforms

(Yebenes & Zorrilla, 2019), and more. Finally, it

has been reported frameworks intended to

implement the Enterprise Information Management

maturity model, such as Gartner® DG (Newman &

Logan, 2008).

2.3 DGPP and DG Benefits

As seen, we identified and classified the DG

frameworks. At the next SLR step, we found that the

10 key processes advocated by the DMBOK

(numbered them from 1 to 10, on Figure 1 first box)

and also the DGPP process number 11 was added

based on Mosley et al. (2009) report. We also found

28 DGPP as described in Figure 1 second box.

3 CASE STUDY METHOD

The case study is an exploratory and qualitative

research method (Yin, 2015). The four case studies at

Brazilian organizations described the DGPP and

analyse the resulting benefits via the DGB-M. The

research design and case protocol are described as

follows. The case data were collected in the 2

semester of 2020.

3.1 Research Design

This research design was: First (1) we performed the

SLR; then (2) we then defined the research method

and conducted the field research (presented in this

section); finally we (3) presented the results in

Sections 4 (single cases) and 5 (cross-cases),

followed by discussions, as seen on Figure 2.

3.2 Cases Selection Criteria

The case selection criteria were based on Yin (2015):

First, the research could have comprehensive access

to the entire project, including its processes,

participants, and documentation. Second, selected

cases that refer to different periods in time. Third,

cases that came from different industries.

(1.1) Standards linked to business need

(1.2) Well-defined and structured DG policies

(1.3) Improvement in the control of business processes

(2.1) Improvement in performance of data analysis

(2.2) Data elements with the same semantics

(3.1) Improvement in performance of data maintenance

(3.2) Guarantee of integrity, security, usability and

maintainability

(3.3) Consolidated data model

(4.1) Data preserved and effectively archived

(4.2) Database performance optimization

(5.1) Reliability in data security and privacy

(5.2) Access to data with security and integrity

(5.3) Data and information risks mitigation

(6.1) Control of costs related to data problems

(6.2) Centralized access to organization’s master data

(7.1) Improvement in performance and efficiency of reports

(7.2) Improvement in strategic decision-making

(8.1) Increased productivity in the use of data

(8.2) Standardization, storage and use of well-defined data

(8.3) Control of costs related to document storage

(9.1) Effective data interpretation and use of information

(9.2) Common understanding of data elements

(10.1) Data with accuracy, timeliness, consistency

completeness and integrity

(10.2) Reduced cost of data duplication

(10.3) Increased client satisfaction with the use of data

(11.1) Adherence to legal rules

(11.2) Risk mitigation of fines and lawsuits

(11.3) Data fraud control

DG benefits

Benefits described in literature

DG benefits

(1) Data Governance

(2) Data Architecture Management

(3) Data Development

(4) Data Operations Management

DG benefits

(5) Data Security Management

(6) Reference and Master Data Management

(7) Data Warehousing and BI Management

(8) Document and Content Management

DG benefits

(9) Data Quality Management

(10) Metadata Management

(11) Data Auditing, and Compliance

DG processes and practices

Beneﬁts of the Enterprise Data Governance in Industry: A Qualitative Research

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Figure 2: Research phases.

We finally select four organizations that met the

criteria as shown in Table 1. The first organization

comes from the agribusiness industry, thus we shall

name it AGRO, and it has implemented DGPP on:

master data for customers, suppliers, and bill of

materials.

Table 1: Enterprise characteristics.

Characteristics Enterprise

AGRO AUTO LOGI FERT

Employees 7,500 10,000 5,000 17,000

Annual revenue

(US$ millions)

700 2,100 600 3,000

IT team 25 50 42 100

The second organization is the automotive

industry, thus from now on refer as AUTO, has

implemented the GDPP to its bill of material master

data. The third organization is a logistics industry,

thus from now on refer as LOGI, has implemented

DGPP to on the following data: customers, suppliers,

and bill of materials. Finally, a fertilizer company,

thus from now on refer as FERT, has implemented the

DGPP to its bill of material master data.

3.3 Data Acquisition

We adopted individual interviews as an instrument

for data collection due to the qualitative nature of the

research (Yin, 2015). We adopted the semi-structured

interview (Selltiz, Wrigthman & Cook, 1987), given

it has a pre-establish script that makes it easier to

compare information among participants. The

interview script had both open and closed questions.

The answer regarded projects that were already

closed between 2016 and 2019.

We interviewed different groups of people with

different perspectives: staff, project managers, and

data analysts. First, we conducted interviews with

eight professionals from a multinational consulting

company that operates in Brazil. These eight staffs

were members of the DG implementation projects at

the mentioned companies. We then interviewed two

project-managers that were in charge of these project

implementations. And finally, a data analyst who has

supported DG processes and practices. We also

collected project data.

3.4 Data Processing

Data treatment and analysis were performed using the

semantic content analysis technique (Neuendorf,

2001), according to the following steps: (1) the

interviews were recorded based on participant

consent; (2) we generated the transcribed; (3) the data

was classified according to the previously explained

categories; and (4) The final list of benefits was then

double-checked back with the participants.

3.5 Research Limitations

The main research limitations are as follows:

(1) Data analysis technique. The data collected in

the interviews were analyzed using the content

analysis technique. The interpretation of this

data was made by the author, which attributes

subjectivity to the results.

(2) Results generalization. All interviewees

belong to a single consulting service company,

which does not allow generalization of the

research results to other companies and

contexts.

4 WITHIN-CASE ANALYSIS

The final within-case analysis is as shown in Table 2.

Light-gray lines are special cases as follows. Zeroed

lines: the process was not implemented by the current

project. Line with 100(%): both processes 4 and 7 are

were already implemented by earlier projects.

Literature review

Research method

Results and

discussion

Phases

•

Systematic literature review

• Multiple case study

• Data acquisition: - through interviews and documents

- two professional per case

• Data processing: - SLR for literature articles

- content analysis for interviews

• Descriptive statistic

• Triangulation of data

Techniques

• DG processes and frameworks

• Benefits of DG processes

• Four cases

• Two interviews per case

• Project’s documents

• Comparation between benefits

described and achieve

Outcomes

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Table 2: The DGPP per Sector.

Processes Enterprise (%)

AGRO AUTO LOGI FERT

1 Data governance 75 65 75 75

2 Data architecture 88 75 68 50

3 Data development 50 7 73 65

4 Data operations 100 100 100 100

5 Data security 50 75 50 0

6 Reference and master

data management

100 58 100 100

7 Data warehousing and

BI management

100 100 100 100

8 Document and content

management

63 0 58 50

9 Data quality

management

0 0 0 0

10 Metadata management 100 78 68 85

11 Data auditing 63 65 20 20

Total implemented

processes (%)

74 54 64 56

4.1 AGRO Case

The AGRO DG project finished by the fall of 2015

after a 22 months project. The project budget was

circa US$ 700,000 distributed as follows: 20% to

executives - partners, directors, and managers; 20%

to senior consultants; and 60% to junior consultants

and trainees. The project was implemented in the

headquarter office and it was sponsored by the Supply

Department. The departments involved in the project

were engineering, accounting, supplies, and

Information Technology (IT). The project team had

five employees from the interviewed consulting

company. The PMBOK and the SCRUM

methodologies supported the project management

methodologies, while the DMBOK was used as a

reference for DGPP.

4.2 AUTO Case

The AUTO DG project finished by the fall of 2016

after 17 months. The project team had five

employees. The project budget was circa US$

875,000 distributed as follows: 37% to executives -

partners, directors, and managers; 30% to senior

consultants; and 33% to junior consultants and

trainees. The project was implemented in a branch

office and it was sponsored by the Accounting

Department. The departments involved in the project

were accounting, supplies, and IT. The project

management was supported by the SCRUM

methodology, and DMBOK was used as a reference

for DG processes. The AUTO company implemented

54% only of DGPP, due to the specific

characteristics, such as the one company with the

lowest productivity rate.

4.3 LOGI Case

The LOGI DG finished by the fall of 2016 after a 12

months project. The project team had six employees.

The project budget was circa US$ 500,000 distributed

as follows: 7% with executives - partners, directors,

and managers; 44% to senior consultants; and 49% to

junior consultants and trainees. The project was

implemented in both a headquarter office and branch

offices. The departments involved in the project

billing, warehousing, supplies, and IT. The project

used SCRUM as the project management

methodology, while the DMBOK was used as a

reference for DGPP. As seen in Table 2, circa 62% of

DGPP identified by

DGB-M were implement. Given

these projects was the first one of this kind, supported

by a consulting company, the results were considered

satisfactory.

4.4 FERT Case

The FERT DG project finished in 2019 after a 9

months project. The project budget was circa US$

350,000, distributed as follows: 10% with executives

partners, directors, and managers; 40% to senior

consultants; and 50% to junior consultants and

trainees. The project was implemented in the head

office and was sponsored by the Finance Department.

The departments involved in the project were in the

areas of finance, and IT. The project used SCRUM as

the project management methodology, while the

DMBOK was used as a reference for DGPP.

5 CROSS-CASE ANALYSIS

This section analyses the interrelation among cases.

5.1 DGPP Costs

As a relevant outcome, the cost to implement 1% of

DGPP per sector was, from lowest to higher: FERT:

US$ 6,292; LOGI: US$ 7,813 AGRO: US$ US$ 9,507;

AUTO: US$ 16,355. The higher AUTO project is

indeed a relevant subject to analyze. This project had

high management costs. While in the other companies

the projects spent an average of 12.3% with it, AUTO

spent 37.0%. Even a longer project (AGRO) and the

same size team project (FERT) had much lower costs.

Except for AUTO, other projects had a US$ 7,871

Beneﬁts of the Enterprise Data Governance in Industry: A Qualitative Research

703

average cost for the implementation of 1% DGPP,

which was in line with the 2015-2019 consulting

company historical database.

5.2 DGPP Benefits

Table 3 presents the DGPP benefits according to the

DGB-M model. This information was acquired with

the project team members, from both the consulting

company and the customer side. The final benefits

and percentages were agreed upon among the

participants.

For instance, The Metadata Management process

at AGRO was fully implemented (100%) but it

resulted in 38% only of the benefits expected benefits.

On the opposite, 7% only of the Data Development

process in AUTO generated a gain of 33% of the

expected benefits. Indeed, AUTO was the case that

had the highest relation (1.25 as seen in Table 3)

DGPP vs benefits, yet it had the highest costs as seem.

Table 3: DGPP projects Benefits.

Processes Enterprise

AGRO AUTO LOGI FERT

1 Data governance 70 88 88 70

2 Data architecture 100 100 83 50

3 Data development 50 33 88 60

5 Data security 50 75 100 0

6 Reference and master

data management

100 75 83 90

8 Document and content

management

100 0 67 50

10 Metadata management 38 83 83 80

11 Data auditing 83 83 33 15

Average

74 67 78 53

Benefit / proc. implemented

1,01 1,25 1,22 0,95

To make it simpler to cross analyze cases, we have

created Figure 3 where it is possible to identify a

directly proportional relationship between the DGPP

and benefits. In fact, 78% of the processes (i.e., 25/32

of the implemented DGPP) are in the #4 quadrant.

This quadrant has a higher implementation degree

versus higher benefits. As expected, there was a

certain exception for this rule. AGRO processes 8 and

10 and for LOGI Process 5, detailed as follows:

AGRO GDPP two exceptions: processes 8 and 10.

In the first process, the perception of benefits was

total (100%), even without the implementation of all

processes described in the literature. However,

AGRO intended to improve this process after project

implementation. In process 10, the benefits achieved

were low (below 50%), this is because sometimes the

benefits related to data quality are perceived only

later after the implementation of the project.

LOGI exception: Process 5. With only 50% of the

implementation of this process, the client had a

perception of having obtained 100% of the benefits.

This happened because LOGI had a deficient DG

maturity level, and with the implementation of part of

the DG processes, the benefits obtained are

expressive and generate significant value for the

organization.

Finally, it is possible to state that the average

GDPP was 62% and the average benefits were 68%.

These values were considered in line with the other

projects managed by the same consulting company.

6 DISCUSSION

In complement to the cross-analysis section, this

section discusses the customer motivations,

challenges and lessons learned behind these DG

projects.

6.1 Motivation for DG Projects

Cost-saving and process standardization were the

main motivation behind these four cases.

AGRO specific motivation was to improve their

decision-making process as well as enhance their data

quality. AUTO specific motivation was to avoid

losing tax benefits due to their inconsistent data. In

fact, Brazil has one of the most complex tax systems

in the world, which thus elevates costs, the number of

employees their overheads. LOGI specific motivation

was their Enterprise Resource Planning System new

release, which was intended to centralize and

organize their resources and data.

6.2 Challenges to Carry Out the

Projects

As expected, all research participants reported

challenges. Resistance to changes is a common issue

among the four cases, such as the difficulty of

obtaining knowledge from people who owned it.

Stakeholders also expected maximum return with

minimum changes, which indeed is most probably not

prone to occur. Bellow, we present specific

challenges for each sector:

AGRO. Employees’ turnover made it difficult to

obtain stakeholders’ involvement. This required more

effort from the consulting service company

professionals in managing change and gaining

business knowledge.

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Figure 3: The degree of DGPP implementation and its related benefits.

AUTO. The enterprise size results in a higher

number of processes and challenges across the

enterprise to the addressed. The problem to obtain

knowledge when considering many departments

highly increase these challenges.

LOGI. Managing stakeholders. There was

reported a lack of alignment and interest among

stakeholders, especially between the project sponsors

and other stakeholders. Besides, there were

difficulties to identify the data owners.

6.3 Lessons Learned

According to the research participants, these were the

main lesson learned:

(1) Clear Identification of Project

Stakeholders, such as a formal project board

with decision making power, and a formal and

empowered sponsor. Additionally, it is

mandatory to have well-prepared staff in the

data registration processes. If the company has

local branches, make a physical visit to each

of them to identify both stakeholders and data

owners.

(2) Change Management Process. Relevante

tasks are formalized the changes will occur

and thus create DG policy, especially for large

organizations, and implement data

standardization process whenever when

required.

(3) Make Technologies and Infrastructure

Available to the Project, given there are

fundamental assets on which these projects

rely, such as the ERP for LOGI project.

7 CONCLUSIONS

This was exploratory and qualitative research, that

proposed a model, named

DGB-M, for investigating the

DGPP benefits at four different sectors: agribusiness,

fertilizers, automotive, and logistics. As result, we

found that on average 62% of the process was

implemented and 68% of the benefits were achieved.

Even these processes were implemented

partially they added value to these organizations,

given many in-developing countries still lack DGPP

projects, this research is highly relevant to identify the

key-process DGPP, in which we created the DGB-M

model. We also validated the DGB-M process and

benefits, thus contributing to enhancing the success

rate of future similar projects. Additional material

such as DGPP costs, benefits, challenges, and other

lessons learned we also collect from this research. For

0 10 20 30 40 50 60 70 80 90 100

100

Percentage of DG processes implemented

Percentage of benefits achieved

- AGRO - AUTO - LOGI - FERT

# - number of DG process:

Average

Far form

midline

Beneﬁts of the Enterprise Data Governance in Industry: A Qualitative Research

705

future researches, the DGB-M can be extended to

contexts, such as companies that actuate at these

reported sectors and in in-developing countries.

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