=Paper=
{{Paper
|id=Vol-3776/short09
|storemode=property
|title=A model for data management and governance practices for everyday business
|pdfUrl=https://ceur-ws.org/Vol-3776/paper09.pdf
|volume=Vol-3776
|authors=Saumya Munasinghage,Heidi Hietala,Nada Elgendy
|dblpUrl=https://dblp.org/rec/conf/tktp/MunasinghageHE24
}}
==A model for data management and governance practices for everyday business==
https://ceur-ws.org/Vol-3776/paper09.pdf
A model for data management and governance practices for
everyday business⋆
Saumya Munasingha1, ∗, Heidi Hietala1,† and Nada Elgendy1,†
1 University of Oulu, Pentti Kaiteran katu 1 90570 Oulu, Finland
Abstract
Managing data in the right way to harness its value and use the latest digital tools creates valuable
opportunities for growing organizations to manage their operations effectively. Data ownership in a
data governance context is a widely discussed topic, and implementing data governance models in
specific organizational environments faces different challenges. In this paper, we add knowledge to the
existing literature by providing a detailed explanation of the steps of implementing a data
organizational structure in a data governance model, specifically focusing on data ownership and
improving its usability. We applied the action design research (ADR) method to create an IT artifact
that offers effective data management solutions and enhances operational efficiency within the
organization. The results show that proposed data governance practices help improve data quality and
lead to improved decision-making, while highlighting the impact of clearly defined data ownership,
roles, and responsibilities on data governance.
Keywords
Data ownership, Data governance, Data quality, Action design research 1
1. Introduction governance practice that clarifies data ownership,
stewardship, and decision-making authority over data
In today's business landscape, organizations consider [7].
their data as one of their most valuable assets, and the High-quality data is essential for maximizing
data provides crucial insights into customer behavior, operational efficiency in businesses [8]. Amidst the
product performance, and operational performance, various discussions on upholding data quality to drive
enabling companies to make informed decisions and business growth and enable effective decision-
achieve better outcomes [1]. Enterprises should making, data governance emerges as a pivotal factor
handle this information responsibly and carefully to in elevating data quality [4]. Strong data ownership
ensure organizational success [2]. A careful planning and accountability for data assets within an
approach is essential for data management in organization can positively impact data governance.
organizations [3]. Also, a dedicated team or group of However, the definition of data ownership and
people capable of making decisions regarding stewardship within an organizational context can
organizational data and data functions, aligning with often be unclear [6,7].
the organizational strategy, is necessary for successful There are questions surrounding the definition of
data management [4]. Consequently, organizations data ownership and stewardship, and how
strive to establish robust data governance responsibilities are assigned to tackle data quality
frameworks and integrate effective data management issues that can affect operational efficiency. The
and governance practices into their daily operations concepts and approaches of data ownership are often
with dedicated personnel. [5]. not clearly defined [4]. Additionally, not every data
Data governance involves meticulous governance framework and the established
organization to ensure data is understood, trusted, of connections between decision-making domains are
high quality, and usable for enterprise purposes [6]. universally applicable to all organizations. Therefore,
Data governance frameworks proposed by various the specific data needs of each organization should be
scholars mention different interconnected decision considered when defining data ownership and
domains or knowledge areas that define the establishing connections between decision domains
functional areas of data management in data [4,7,8]. Furthermore, Abraham et al. (2019) suggest
governance. Organizations can significantly reduce that further research is needed to determine the scope
costs associated with these domains in data and approach of data ownership in relation to
management by implementing an effective data organizational effectiveness. Accordingly, a research
TKTP 2024: Annual Doctoral Symposium of Computer Science, 10.- © 2023 Copyright for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
11.6.2024 Vaasa, Finland
munasinghamsk@yahoo.com (S. Munasingha)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
gap was identified, and we strive to answer the quality refers to the state of organizational data,
following research questions. including its accuracy, completeness, relevance, and
Research Questions (RQ): RQ1: How to build and fitness for purpose [11]. A conceptual architecture for
implement a model that can address data quality data governance, which emphasizes structural
issues that arise with the missing data ownership of governance mechanisms such as reporting lines,
organizational data assets? RQ2: How to improve the governance bodies, and decision-making authorities,
overall efficiency of such a model that can be utilized aligns data asset ownership with data governance
in everyday business? practices [7]. Successful data governance programs
The structural model for data governance within organizations rely on organizational support
organization and its automated solution for and the delineation of key roles, such as data owners,
improving operational efficiency, which are presented stewards, and consumers [7].
in this paper, were developed through an Action Data stewards should ensure responsible
Design Research (ADR) project [9] conducted by the information sharing [6] and formalize accountabilities
authors. From the organizational side, the project for managing information resources on behalf of, and
team consisted of an enterprise architect, a senior IT in the best interest of, others [10]. The concept of
analyst, a project manager, and senior managers of a business and technical stewardship involves business
medium-sized Finnish manufacturing company. The stewards ensuring data quality within their respective
team from the case organization started to tackle the domains, while technical stewards manage IT
data management issue as part of an enterprise systems. This serves to define the scope of data
architecture (EA) project, within which this research stewardship more clearly [6].
project was conducted. The significance of this DAMA-DMBOK offers another influential
research is building a data governance model to tackle framework, advocating for clear role definitions and
data ownership in data assets for a medium-sized presenting ten knowledge areas crucial for effective
manufacturing company, expecting to assist the data management. These frameworks collectively
company in achieving strategic objectives related to stress the importance of establishing roles and
data quality and operational efficiency. responsibilities, underscoring the criticality of data
We structured this paper as follows: first, we give governance in enhancing operational efficiency and
a brief overview of the data management and addressing data quality concerns [1].
governance principles from the existing literature, The success of data governance initiatives relies
and subsequently, we undertake an assessment of the on structural frameworks, behavioral aspects, and
research gap. Next, we describe ADR as our adapted technological infrastructures. Maintaining data
research methodology and briefly outline the quality through trusted practices emphasizes the
applicability of the research methodology. Then, we critical role of human behavior, enabling
explain the ADR method in a more detailed level, organizations to succeed in data governance [10].
describing the four stages of the ADR project. Common goals of a data governance program
Accordingly, the solution that addresses the research include increased operational efficiency and
problems is presented, along with the learning addressing data quality issues [1]. With proper data
resulting from ADR. Next, in the discussion section, we management and governance, organizations can
present our main findings and discuss the deliver measurable improvements and empower
generalizability of the study, highlighting the organizational data users with decision-making
limitations and future work. Finally, we close with a power [12].
short summary. Data governance is crucial for improving the
value of data and reducing associated costs and risks
2. Background [7]. As companies grow and adopt new technologies,
establishing good data practices becomes even more
Data governance can be described as the exercise of important. Maintaining data quality and encouraging
authority and control over the management of data user compliance are critical factors for success in data
[7]. Data governance is exercised through policies, governance [10]. As businesses grow and expand into
standardizing data to ensure data stewardship and new geographic areas, there is a greater need for
data quality [10]. It refers to what decisions must be standardization. Data governance plays a crucial role
made and who makes those decisions, defining the in enabling medium-sized enterprises to expand
actions taken to maintain data integrity, and globally through data harmonization [12]. This
encompassing how data reliability, security, underscores the strategic significance of data
availability, and usability are managed [1,5]. Data governance in driving organizational growth and
competitiveness. As companies expand, the involves creating, intervening, and evaluating an
organizational hierarchical structures often become artifact that not only embodies the researchers'
more complex, necessitating robust solutions when theoretical foundations and intentions but also
introducing data governance roles [10]. incorporates feedback from users and ongoing usage
Previous literature highlights the significance of within a specific context. Furthermore, ADR is a
data governance in data management, particularly in method that can effectively address specific problems
maintaining data quality through governance encountered within an organization by intervening,
practices. It also underscores the success factors that evaluating, and developing an IT artifact to tackle the
help organizations manage their data through data class of problems identified in the given situation.
governance as they grow. The existing literature lacks Because ADR aims to design a problem-solving
discussions on how data stewardship and ownership artifact through iterative evaluation and learning [15],
align with organizational structures and the impact of we followed the four stages of the ADR cycle as our
governance practices on data quality and research methodology. These include: 1) Problem
organizational changes. Additionally, there is a gap in Formulation, 2) Building, Intervention, and
the literature regarding how organizations with Evaluation (BIE), 2) Reflection and Learning, and 4)
different strategies and resources can adapt data Formalization of Learning [9]. Our four-stage ADR
stewardship practices. process is exhibited in Table 1.
While our study was conducted in a specific It briefly explains the research task in each stage.
organization, it explores how roles, responsibilities, Also, we introduce the corresponding research
and core data concepts can be tailored to suit various principles and their consequences for the research.
organizations with similar contexts in data
management. The literature also falls short in 4. ADR for data governance
outlining common roles adaptable to different data
governance organizations and their implementation
metamodel development
to enhance data quality in an organizational setting. The research opportunity: Our case company is a
In contrast to many existing data governance medium-sized tech-based manufacturing company
frameworks and studies, our study has implemented with around 300 employees and a moderate global
a data governance program, evaluated the outcomes, market presence. They have a strong market presence
and proposed solutions to enhance the program's in the Nordic countries, and their expectation is to
overall effectiveness. grow in new geographic markets while expanding
their core business. With their current growth over
3. Methodology the last 2-3 years, a strong data culture is in demand
because of the organization's high-tech production
Our research project focuses on addressing an environment. Also, for market and customer
organizational problem by developing a solution segmentation and identifying new leads, they desired
tailored to the organizational context and to have a suitable analytics platform. Hence,
incorporating user feedback throughout the process. maintaining the data quality, accessibility and data
ADR guides the development of IT artifacts within discoverability becomes crucial for data management
organizational settings [9]. Our goal is to create an IT in the case organization. The case company is
artifact that offers effective data management equipped with various information systems, such as
solutions and enhances operational efficiency within an on-premise Enterprise Resource Planning (ERP)
the organization. Continuous user feedback and system, a cloud-based Customer Relation
iterative development are essential for the successful Management (CRM) system, and a Product Lifecycle
implementation of data governance solutions in Management (PLM) system. In addition, the
organizations [1]. Additionally, we aim to enhance the organization has a centralized cloud data warehouse
overall efficiency of the proposed IT artifact in daily and data lake support for reporting with Power BI. All
operations and user involvement. Our solution is a of these information systems generate a large volume
result of both design and practical application, of data points, and the organization’s main concern is
following an iterative development and evaluation to manage this data properly, maintain the data
approach with diverse stakeholders within the quality, and help business users locate data sets,
organization [14]. identify the contact person for data access, and
ADR is used to develop and evaluate a set of IT generate reports
artifacts within an organizational setting to generate
prescriptive design knowledge [9]. This process
Table 1
ADR stages, Research tasks, Outcomes and actions based on Sein et al. (2011)
Stage Research tasks Outcomes and actions
Problem Conceptualize the research The need for the IT artifact of the case organization to
formulation opportunity. improve the discoverability and accountability of the
data assets was the initial trigger of this research.
Formulate the research Two research questions are formulated with reference
questions to the class of the problem. RQ1: How to build and
implement a model that can address data quality
issues that arise with the missing data ownership of
organizational data assets? RQ2: How to improve the
overall efficiency of such a model that can be utilized
in everyday business?
Cast the problem as a class of Achieving data quality and operational efficiency
problem through data governance.
Theoretical bases and prior The DAMA-DMBOK framework [1] and the conceptual
technology advances framework provided by Abraham et al., (2019) were
used as the main theoretical base for the governance
framework.
Define scope Initially focusing on operation data assets
Setup roles and responsibilities The team was structured into three groups. The
research team, the EA team, and an IT analyst from the
case organization.
Building, Initial design of the target The data governance organization structure and
intervention automated workflow.
and
evaluation
Customized BIE form The artifact design and evaluation are done with the
ADR team as a combined approach from researchers
and practitioners.
Evaluation, repeat Based on three criteria explained in the ‘Results'
section, continuous evaluations and improvements
were applied to the artifact. Agile scrum framework
followed.
Reflection Reflect upon design Continuous intervention and evaluations, and the
and learning introduction of design principles. The verification of
the reality of the solution.
Analyze intervention Compare the newly generated knowledge from the
continuous intervention with the problem class.
Formalization Share outcomes The alpha-version of the artifact was evaluated during
of learning the BIE cycle, and the beta-version was expected to be
introduced to the end-users in the case organization.
Articulate the design principles We discuss the learning relating to the existing
and generalize the outcome literature in the ‘Discussion’ and generalized design
principles applicable to the class of the problem.
The case organization faces challenges in organizational structure mapping to data
managing high data volume and streamlining data organization following Ladley, J. (2020) and Plotkin,
operations toward achieving operational efficiency. D. (2021).
As a result, business users face difficulties with Scope: Incorporating the proposed meta model
accessing data, especially when generating reports. and data governance organization structure into the
Often, they have to spend a considerable amount of comprehensive EA was our primary objective.
time finding the correct owners and requesting access Initially, our efforts were concentrated within the
to particular data. This has become a significant operations department, encompassing purchasing,
concern for the organization, and the research team manufacturing, business excellence, and customer
identified missing data governance, in particular, the service teams. Furthermore, in alignment with the
missing ownership of data assets, as one of the organizational strategy and requirements at the time,
reasons for this issue. Also, different departments in the primary emphasis within the proposed model was
the case organization generate inconsistent statistics on data quality [4,10] and structural data governance,
over the same topic with different data sets. This which dictates reporting structures and
raises a critical data quality issue requiring immediate accountabilities, including roles, responsibilities, and
action as it may lead to incorrect organizational decision-making authority allocation [7].
statistics. Roles and responsibilities: The EA project team of
Hence, the need for a meaningful data the case organization and research team are the
governance organizational structure was needed to participants of the research project. The team was
improve the ownership of data assets. In addition, the structured into three groups. The research team
case organization maintains an old Excel sheet as the represents the theoretical aspects. The EA team
system register even though it is equipped with represents the practical aspects, and analytics from
modern data architecture. Because of the lack of the case organization provide the technical aspects.
collaboration, advancement with new technologies, Building Intervention and Evaluation: During the
and tracking issues such as unnoticed errors, the case second stage of the ADR approach, ‘Building,
organization needed a new overall solution that could Intervention and Evaluation’, the artifact is designed,
address all of these issues. Inspired by this iteratively refined, and evaluated. As Sein et al. (2011)
knowledge-creation opportunity and practical- suggested, we created the initial design of our IT
inspired research problem in an organizational artifact using the problem framing and theoretical
domain, we formed our two research questions. RQ1: background presented in stage one. We carried out
How to build and implement a model that can address our BIE stage with the iterative processes to build the
data quality issues that arise with the missing data artifact, perform the intervention, and analyze the
ownership of organizational data assets? RQ2: How to BIE. In relevance to our problem formation, our
improve the overall efficiency of such a model that can research focused on IT Dominant BIE. We built the IT
be utilized in everyday business? artifact design as a solution to the organizational
Class of problem: In this research, we aim to problem described in Stage 1.
create a general structure to achieve data quality and The project was conducted for five months
operational efficiency through data governance and within the case organization working in scrum cycles
building and proposing a relationship between as part of an EA development project. The two-week
business processes and information systems sprints were planned, and we presented the progress
(metamodel). of the initial model during the scrum meeting. The
Theoretical base: We built a metamodel using a evaluation was conducted after each meeting, and the
data management tool to identify data assets and feedback was taken as notes. The duration of the
create relationships among the data assets. The meetings was approximately 120 minutes. In addition
organization aims to use this model to help business to the continuous meetings, we had three more
users find specific data sets, identify the users who meetings, including the kick-off, mid-way evaluation
own them, and define responsibilities to maintain the meeting and the final evaluation meeting. We
data quality. Our research has enabled them to evaluated our artifact and implemented necessary
establish a relationship with data assets by building interventions based on the recommendations
the metamodel on the tool and assigning data collected from the meetings.
ownership to each data asset. To determine First, we built the initial metamodel (Figure 1),
ownership and data stewardship, we utilized the data aligning with the current organizational structure and
governance framework developed by Abraham et al. data structure. The meta model builds the connections
(2019) with a structural governance framework and between the main components in the organization
[1,10]. Furthermore, we adapted the concepts responsible and accountable for several data assets.
Abraham et al. (2019) described for a conceptual data Data owners communicate broad data requirements
governance model that organizations can adapt and and risks [6]. They also own and make decisions about
modify based on their data governance program the data that the business process produces [6]. Data
objectives. stewards are key representatives in the business
Then we followed the procedural governance functions that belong to business processes. The data
mechanisms [7] and the principle of top-level design assets binding with the business functions do not
[10] to build a data governance organizational belong to the appointed data stewards, but they work
structure (Figure 2) that aligns with our proposed closely with the data and understand the business
metamodel. This data governance structure helps to data [6]. We added another role to the structure, data
build authority over data assets and enhance the expert, to support the technical aspects of the data
transparency within the case organization, being able requirements. The experts support maintaining the
to answer these questions: WHO owns WHAT? WHO data quality in the technical aspect, playing a role as a
is responsible for WHAT? WHOM should I contact? technical data steward. The data management tool
and WHO has access to WHAT? was used to scan the available data in the case
The proposed metamodel illustrates the organization, and we assigned the roles and people
hierarchy of data ownership and decision-making according to the metamodel and data governance
authority within our IT artifact. Data owners expected organization structure.
to be selected for each main business process of the
organization and data stewards were selected to be in
each business function managed under each business
process. The relationship between a business process
and business function was a one-to-many
relationship.
Figure 2: Data Governance Organizational Structure
Furthermore, we present detailed information on the
roles and responsibilities of the data governance
organization structure we proposed for the case
organization in Table 2. We adapted the descriptions
from Abraham et al. (2019), and the responsibilities
Figure 1: Metamodel from Ladley, J. (2020) and Plotkin, D. (2021). To
address our second research question, we introduced
As an example, during our artifact-building phase, the an automated cloud-based solution that registers the
artifact was introduced to the operations department data owners whenever a new data asset is created to
in the case organization. The head of operations was a the system register. We took the initiative by
C-level executive officer, and the Chief Operations introducing a solution as a collection of data in a cloud
Officer (COO) was the Executive sponsor of our space to replace the legacy system register at the case
governance model. The operations department is organization. As described in Stage 1, the case
involved with different business processes, such as organization used an Excel sheet as their system
logistics, customer service, and sourcing. These main register and was unable to maintain it properly.
business processes were handled by senior managers
(e.g., Logistics manager, Customer service manager, Our cloud application served as the easy maintenance
Sourcing manager, etc.), and we decided to assign system for the system register.
them as the data owners, since they were technically
Table 2
Data governance organization – roles and responsibilities based on Abrahm et al. (2019), Ladley, L. (2020) and
Plotkin. (2021)
Role Description Responsibility Assign to
Executive One of the highest-level Provide strategic direction to data COO
sponsor executives in the organization governance and management. Business
who has the authority to prioritization Funding for data
change the organization and management initiatives
support the program
enterprise-wide.
Data A senior executive who is Communicate broad data requirements Customer Service
owner accountable for one or more and risks. Owns and makes decisions manager,
data sets, business lines, and on the data of the business processes. Logistics Manager,
business assets. Select data stewards Sourcing Manager,
Production Manager
Data The key representative in a Responsible for quality, use and Customer service
steward specific business area who meaning of the specific business data. executive,
takes care of data assets that Make recommendations about the data. Sourcing executive,
do not belong to themselves Communicate data requirements. Production Supervisor
but work closely with that Execute the policies and standards
data. The business leaders or agreed upon to maintain the data
subject matter experts. quality and operation efficiency.
Maintain agreed-upon data definitions
and formats. Identify data quality
issues and ensure that business users
adhere to specified data standards.
Collaborate with other data governance
team members to uphold data
consistency and data quality metrics.
Create, update, and delete data assets
in the asset register.
Data The designated enterprise Control data access of the assigned ERP specialist, CRM
expert application owners in the applications. Collaborate with data specialist
case organization. stewards who work with the
applications, and fix data quality or
integrity issues. Communicate
technical overview and requirements
of the applications. Provide support to
maintain the overall data quality for
data stewards.
During the evaluation meetings, the project team was The initial model was refined with the iterative BIE
satisfied with the functionality of the application, and cycles. According to the feedback, we needed to decide
we improved the process by automating the the granularity of the data asset scan by the data
ownership assignment with the data management management tool and assign the data owners and
tool. For the automation, we used a Python script. It experts first to the metamodel.
was tested appropriately, and the results were Evaluate the artifact: As explained in the BIE
verified before being presented during the evaluation. cycle, we evaluated the IT artifact based on the formal
feedback sessions of the project team stakeholders. was implemented in the case organization, allowing it
We used 3 evaluation criteria to understand the to be used by other users. Due to the time limitations,
feedback of the stakeholders and do the interventions we couldn’t collect feedback from the end-users
during the iterations. regarding the artifact. However, some of the
1. Understandability of the artifact concepts to practitioners were representing the end users’
the business users. perspectives and acted as end users for some systems.
2. Usability of the IT artifact to the business users. Hence, we believe the feedback could be accepted as a
3. Practicality of the artifact to the organization general evaluation of the artifact.
culture. Reflection and Learning: This study may
Most of the received feedback was positive. Some of motivate and help other researchers interested in
the project members did not agree with having extra implementing data governance practices, particularly
work added to their teams, such as data stewards within organizations with growth potential and an
playing a role in supporting the data quality. However, interest in data-driven decision-making. While
everyone agreed with the practicality of the artifact previous studies provide conceptual frameworks and
and its usability. Concept-wise, everyone agreed and models for implementing structural data governance
understood the concepts and the reason for to improve the overall quality of such governance
implementing such a system to overcome the programs, the proposed IT artifact is a practical
identified issues in the case organization. example of such a model.
During the alpha-version, practitioners were .
provided with positive feedback. The beta-version
Table 3
Design principles
Implementor, aim and user Context Mechanisms Rationale
For IT domain experts (Technical In report Designate and make Because doing so
user) (implementers), to reduce generating systems visible the correct improves efficiency in
the amount of data access contact points data discovery
requests received (aim), from
business users in the organization
(users)
For business domain experts Accessing data as Improve data Because by doing so,
(implementer), to improve the inputs for different accessibility the organization can
data accessibility(aim), from processes improve data sharing.
internal and external users
(users)
For senior executives In Report Ensure accuracy of Because data accuracy
(implementer), to improve the generating systems the statistics of the increases the quality of
report data quality(aim), from the reports the decisions.
report generators (Users)
For data users(implementer), to In data governance Automate data Because by doing so,
follow the governance structures ownership data governance
structure(aim), of data assignment and practices are easily
governance practitioners (Users) ensure usability followed.
of the process
During the early interventions of the IT artifact, which about the additional steps introduced into existing
assigned roles with responsibilities and decision- processes. Based on this feedback, we incorporated a
making authority over data within the organizational detailed role description (Table 2), which clarified
structure, many practitioners expressed concerns responsibilities and improved data stewardship.
Despite the organization already having privacy and has facilitated data access for users without
security controls in place technically, the clarification unnecessary communication. The implementation of
of responsibilities and reporting structure ensured automation, such as streamlined processes, has
proper adherence to data quality, security, and reduced manual workload and ensured compliance
privacy controls. with data governance policies. Moreover, assigning
Moreover, we received positive feedback from data ownership to the data assets, particularly for
senior managers to IT experts when the workflows dashboards, has improved data accessibility
were automated compared to the early iterations effectively, thus significantly enhancing productivity.
when only the structural model was introduced. Regular feedback iterations have further facilitated
Design Principles: The anatomy of the design user adaptation to the new practices, creating an
principles [16] is structured in the following table, environment of continuous improvement and
Table 3. We adopted multiple design principles that ensuring that the implemented solutions are user-
can be used for artifact refinement [16]. The four friendly and effective.
design principles outlined below were developed to Limitation and Future Research: Due to time and
meet the objectives of the data governance program other organizational constraints, we could not
and effectively address the research questions evaluate the beta-version of the artifact with the case
organization end-users nor evaluate the alpha version
5. Discussion of the artifact quantitatively. We would like to
propose studying the correlations between data
Strong ownership over data in a data governance governance organization and overall organizational
framework would benefit the whole data governance efficiency quantitatively.
program [7]. Even though it requires careful planning,
data management provides the authority and control
over data [7] and having a decision-making structure
6. Conclusion
with data governance provides support to expand The research is focused on addressing the key
medium-sized companies to grow [12]. At first, considerations for designing and implementing a data
adapting data governance practices to daily governance model in an organization with strong data
operations would be hectic. However, people, ownership and stewardship. Our study explores how
processes and technologies are bonded together for such a model can enhance data quality,
any successful IS implementation. Hence, introducing discoverability, and sharing while adapting to
automation tools to governance practices would evolving data governance practices. We emphasize
motivate users to adopt the new workflows and the process of structuring a data governance
improve their effectiveness. Being able to know whom framework for a growing manufacturing organization
to contact to access specific data makes a favorable and enhancing its effectiveness. By understanding the
impact on data discoverability, accessibility, and organizational needs and context, IS implementors
effective report generation. can integrate governance practices that support
Each business process within the operations continued growth through digital technologies and
department has a designated data steward from the fully utilize data within the organization. Additionally,
corresponding business function. A governance model the fundamental principles of data stewardship and
provides guidance for those overseeing data quality the methodologies identified in this study, along with
and security. the design principles of ADR, are applicable across
Main Findings: The ADR project's findings various organizational contexts beyond
underscore the profound impact of clearly defined manufacturing. These insights offer a framework that
data ownership, roles, and responsibilities on data can assist different industries in refining their data
governance. The implementation of an automated governance practices, ensuring effective and
solution can greatly enhance overall efficiency and the sustainable utilization of data assets across
user-friendliness of governance practices. These organizations of diverse sizes and sectors.
strategies have not only improved operational
efficiency and data quality, but also fostered a culture Acknowledgements
of accountability and adaptability among users.
Defining specific responsibilities for data The authors thank the case organization EA project
stewards in each business function has been observed team for their valuable input for the project, great
to enhance accountability and data quality. This has cooperation, comments, and feedback during all the
resulted in more accurate and timely data entries and stages of the project.
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