Smart Data Stewardship:

Innovating Governance and Quality with AI

Otmane Azeroual

German Centre for Higher Education Research and Science Studies (DZHW), 10117 Berlin, Germany

Keywords: Data Governance, Data Quality, Artificial Intelligence (AI), AI-Powered Framework, Data Integration,

Quality Assurance, Data Protection Monitoring, Compliance Management, Digital Transformation,

Organizational Efficiency, Case Studies, Practical Applications.

Abstract: In the modern digital landscape, data plays a crucial role in the competitiveness and efficiency of

organizations. Data governance, which involves managing and ensuring data quality, faces increasing

challenges due to the growing volumes and complexities of data. This paper examines how artificial

intelligence (AI) offers innovative solutions for optimizing data governance and data quality. We present an

AI-powered framework that includes components such as data integration, quality assurance, data protection

monitoring, and compliance management. Through case studies and practical examples, we demonstrate how

this framework can be implemented in real-world environments and the benefits it offers.

1 INTRODUCTION

In today's digitalized world, data has become an

essential asset that forms the basis for business

decisions, innovations, and strategic planning

(Schildt, 2020), (Kolasani, 2023). Data governance

refers to the comprehensive management of the

availability, usability, integrity, and security of data

within an organization (Solà-Morales et al., 2023). It

is an overarching concept that defines the policies,

processes, roles, standards, and metrics necessary to

ensure that data can be used effectively and

efficiently (Hatanaka et al., 2022). The importance of

data governance cannot be overstated, as it helps to

minimize risks, ensure compliance with legal and

regulatory requirements, and guarantee data quality

(Mahanti, 2021).

Traditional approaches to data governance face

significant challenges, which are exacerbated by the

exponentially growing volumes of data and the

increasing complexity of data landscapes (Caparini &

Gogolewska, 2021). One of the main difficulties lies

in the manual management of data, which is prone to

errors and time-consuming. Additionally, traditional

data governance models are often not flexible enough

to quickly adapt to changes in the data landscape or

new regulatory requirements (Gong et al., 2020). The

fragmentation of data across various systems and

silos makes it difficult to ensure consistent data

quality and to implement a holistic data governance

strategy (Strengholt, 2020), (Janssen et al., 2020).

Moreover, many organizations are confronted with

limited resources and expertise, which further

complicates the effective implementation and

maintenance of data governance programs (Plotkin,

2020).

Artificial intelligence (AI) offers innovative

solutions to address the challenges of traditional data

governance (Janssen et al., 2020). By leveraging AI

technologies such as machine learning (ML), natural

language processing (NLP), and robotic process

automation (RPA), many of the manual and time-

consuming processes can be automated, significantly

improving the efficiency and accuracy of Data

Governance. AI-powered systems can analyze large

volumes of data in real-time, identify patterns and

anomalies, and take proactive measures to ensure data

quality and security (Yandrapalli, 2024).

Additionally, AI enables dynamic adaptation to

changing regulatory requirements and business

environments, ensuring flexible and future-proof data

governance (Stanciu et al., 2021). These technologies

can also help integrate and harmonize data from

various sources, reducing data fragmentation and

enabling a holistic view of the data landscape.

This paper aims to present a comprehensive

framework that demonstrates how AI can be used to

Azeroual, O.

Smart Data Stewardship: Innovating Governance and Quality with AI.

DOI: 10.5220/0012918200003838

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3: KMIS, pages 187-196

ISBN: 978-989-758-716-0; ISSN: 2184-3228

187

optimize data governance and data quality in

organizations. The following sections will first

explain the theoretical framework for data

governance and the relevance of AI technologies.

Next, the various components of the proposed AI-

driven framework will be described in detail,

including data integration, data quality assurance,

data privacy monitoring, and compliance

management. The paper will also discuss the steps for

implementing the framework, as well as the

associated technological and organizational

requirements and challenges. Additionally, case

studies and practical examples will be presented to

illustrate the practical applicability of the framework

and analyze the benefits achieved. Finally, there will

be a discussion of the effectiveness of the framework

compared to traditional approaches, followed by a

summary of the key findings and recommendations

for practice.

2 THEORETICAL FRAMEWORK

Data governance refers to the set of measures, rules,

processes, and technologies that ensure data is

managed efficiently, effectively, and securely within

an organization (Azeroual et al., 2023). The main goal

of data governance is to ensure data quality and

integrity, protect data privacy and security, and

comply with legal and regulatory requirements

(Brous et al., 2020). This also includes establishing

clear responsibilities and accountabilities for data

management and defining policies and standards for

data handling.

The essential objectives of data governance

include (Georgiadis & Poels, 2021), (Duggineni,

2023), (Ren, 2022), (Al-Surmi et al., 2022):

• Ensuring Data Quality: Avoiding data

inconsistencies, duplications, and errors to

provide reliable and accurate data for

business decisions.

• Compliance with Regulations: Ensuring

that data processing and storage comply

with legal and regulatory requirements, such

as GDPR.

• Data Protection: Ensuring the

confidentiality, integrity, and availability of

data to prevent data misuse and breaches.

• Efficient Data Management: Optimizing

processes for data integration, processing,

and utilization to enhance the efficiency and

effectiveness of data management.

• Supporting Strategic Decision-Making:

Providing high-quality and up-to-date data

for strategic planning and operational

decisions.

AI encompasses a variety of technologies that

enable machines to mimic human intelligence and

perform tasks autonomously (Jiang et al., 2022). The

key AI technologies relevant to data governance

include machine learning (ML), natural language

processing (NLP), and robotic process automation

(RPA) (Ansari et al., 2019), (Serey et al., 2021),

(Sarker, 2021), (Sharma et al., 2022), (Rane et al.,

2024):

• ML is a subset of AI that uses algorithms

and statistical models to learn from data and

make predictions or decisions without being

explicitly programmed. ML models can be

used to detect patterns and anomalies in

large datasets to identify and address data

quality issues. For example, ML algorithms

can be used for automatic duplicate

detection, error correction, and data

classification.

• NLP enables machines to understand,

interpret, and generate human language.

This technology can be used to analyze and

process unstructured data such as text

documents, emails, and reports. In data

governance, NLP can be employed to extract

relevant information from unstructured data

sources, categorize data, and generate

metadata, thereby improving data quality

and availability.

• RPA uses software robots or "bots" to

automate repetitive and rule-based tasks.

RPA can be used in data governance to

automate routine tasks such as data cleaning,

validation, and updating. This reduces

manual intervention and minimizes error

rates, thereby increasing the efficiency and

accuracy of data management.

In recent years, numerous studies and approaches

have been developed to integrate AI into data

governance (Janssen et al., 2020), (Wirtz et al., 2020),

(Zuiderwijk et al., 2021), (Taeihagh, 2021), (Khan et

al., 2024). These approaches aim to enhance the

efficiency and effectiveness of data management

through the use of AI technologies.

• Automated Data Quality Monitoring:

Several studies have demonstrated that

using ML algorithms for continuous

monitoring and improvement of data

quality offers significant benefits (Lee &

Shin, 2020). For instance, ML models can

KMIS 2024 - 16th International Conference on Knowledge Management and Information Systems

188

detect data anomalies in real time and

suggest corrections, thereby sustainably

improving data quality.

• NLP-Based Data Analysis: Research has

shown that NLP techniques can be

effectively used to analyze and process

large volumes of unstructured data

(Aladakatti & Senthil Kumar, 2023). This

enables better data categorization and

indexing, enhancing data availability and

usability.

• RPA for Data Management Processes:

The use of RPA to automate data

management tasks has proven successful in

many organizations. Studies have shown

that RPA increases the efficiency of data

management by automating repetitive tasks

and reducing human errors (Radke et al.,

2020).

An example of a successful implementation of an

AI-powered data governance framework is a project

by a large financial services provider that employed

ML and NLP technologies to improve data quality

and security (Li et al., 2021), (Xu, 2022), (Mishra et

al., 2024). By integrating these technologies, the

company was able to significantly enhance data

integrity and compliance while reducing operational

costs.

3 COMPONENTS OF THE

AI-POWERED FRAMEWORK

The AI-powered framework presented for optimizing

data governance and data quality encompasses four

essential components: data integration, data quality

assurance, data protection monitoring, and

compliance management.

3.1 Data Integration

Data integration refers to the process of combining

data from various sources to provide a consolidated

and unified view. This is particularly important as

organizations often work with a multitude of data

sources and formats. Effective data integration

enables harmonizing data across different systems,

which in turn improves the quality and availability of

data (Rangineni et al., 2023). The significance of data

integration lies in its role as the foundation for reliable

data analysis and decision-making. Without efficient

integration, data can be fragmented and inconsistent,

leading to faulty analyses and suboptimal decisions.

Artificial intelligence can significantly enhance and

automate the data integration process (Aldoseri et al.,

2023). ML and NLP can be used to identify complex

data patterns and automatically transform and

harmonize data from various sources. AI-powered

data integration systems can:

• Automatic Schema Matching and

Mapping: ML algorithms can recognize and

automatically map data fields from different

sources, reducing the time and effort required

for manual data reconciliation.

• Anomaly Detection: AI can detect

anomalies and inconsistencies in data

sources and suggest solutions to address

these issues.

• Real-Time Data Integration: AI-powered

systems can continuously integrate and

update data in real-time, enhancing data

timeliness and accuracy.

3.2 Data Quality Assurance

Data quality assurance refers to the processes and

measures that ensure data is accurate, complete,

consistent, and up-to-date. High data quality is crucial

for the reliability and credibility of data analysis and

decision-making. Poor data quality can lead to

incorrect conclusions and ineffective business

strategies. Therefore, ensuring data quality is a

fundamental aspect of data governance.

AI offers powerful tools to improve data quality

through the use of advanced algorithms:

• Anomaly Detection: ML algorithms can be

used to identify unusual patterns and outliers

in the data that may indicate errors or

inaccuracies.

• Automated Data Cleansing: AI systems

can automatically detect and clean

duplicates, missing values, and

inconsistencies in the data.

• Data Validation: AI can continuously

monitor data quality and apply validation

rules to ensure data meets established

quality standards.

3.3 Data Protection Monitoring

Data protection monitoring encompasses the

measures and technologies used to ensure the

confidentiality, integrity, and availability of data

(Farayola et al., 2024). This is particularly important

given the increasing threats from cyberattacks and

data breaches. An effective data protection

monitoring system helps safeguard sensitive data and

Smart Data Stewardship: Innovating Governance and Quality with AI

189

Figure 1: AI-Powered Framework for Optimizing Data Governance and Data Quality.

ensures that only authorized users have access to it

(Duggineni, 2023).

AI can significantly enhance data protection

monitoring by employing advanced techniques to

detect security vulnerabilities and data breaches:

• Anomaly Detection: ML algorithms can

identify unusual activities and anomalies in

network traffic that may indicate potential

security breaches.

• Threat Analysis: AI can analyze threats in

real-time and proactively adjust security

measures to prevent attacks.

• Behavior Analysis: AI-powered systems

can analyze user behavior and identify

abnormal activities that may indicate

potential insider threats.

3.4 Compliance Management

Compliance management refers to ensuring that an

organization meets all relevant legal and regulatory

requirements (Biggeri et al., 2023). This is

particularly crucial in highly regulated industries such

as healthcare, finance, and insurance. An effective

compliance management system helps minimize

legal risks and build trust with customers and

stakeholders (Olawale et al., 2024).

AI can significantly improve compliance

management by employing advanced techniques to

monitor and ensure adherence to regulations:

• Rule-Based Monitoring: AI-powered

systems can continuously monitor

compliance with regulations and

automatically issue alerts when violations

are detected.

• Automatic Updates: AI can be used to

update compliance rules and regulations in

real-time, ensuring the organization is

always up-to-date with legal requirements.

• Audit Trails: AI can generate detailed logs

and reports for audits and inspections to

demonstrate compliance with regulations.

Summarizing the components in Figure 1, each

plays a crucial role in ensuring effective and efficient

data management. By leveraging AI technologies

such as machine learning, natural language

processing, and robotic process automation,

organizations can overcome the challenges of

traditional data governance and achieve higher data

quality, security, and compliance. This framework

offers a comprehensive approach to modernizing and

enhancing data governance in the digital era.

4 IMPLEMENTATION OF THE AI

FRAMEWORK: STEPS,

REQUIREMENTS, AND

CHALLENGES

To optimize data governance and data quality using

an AI-powered framework, a systematic approach is

required. Here, we detail the essential implementation

steps and discuss the technological and organizational

requirements necessary for organizations to

effectively deploy the framework.

4.1 Steps for Implementing the

Framework

The implementation of an AI-powered framework for

optimizing data governance and data quality requires

a systematically grounded approach. First, a

comprehensive requirements analysis is essential to

identify the specific needs and goals of the

organization. This step involves evaluating current

data governance practices, identifying weaknesses,

and defining the main objectives and requirements.

The analysis is supported by qualitative methods such

as stakeholder interviews and quantitative methods

such as data analyses. For example, a healthcare

organization might conduct interviews with doctors

and administrative staff to identify requirements for

data quality and data protection.

Following the requirements analysis is the

selection of appropriate AI technologies. This step

involves evaluating ML algorithms, NLP tools, and

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Figure 2: Automation and Efficiency Improvements through AI Technologies.

RPA software that meet the needs of data integration,

data quality assurance, data protection monitoring,

and compliance management. Criteria for selection

include scalability, compatibility with existing IT

infrastructure, user-friendliness, and cost. For

instance, a financial institution might choose specific

ML algorithms developed for fraud detection in

transaction data and evaluate them based on their

ability to process large volumes of data in real time

and reliably detect anomalies.

4.2 Technological and Organizational

Requirements

Implementing an AI-powered framework involves

addressing both the technological and organizational

requirements of the organization.

 Technological Requirements

Powerful servers and network infrastructures are

required to support real-time data processing and the

execution of AI algorithms. Additionally, specialized

AI platforms and tools are necessary to implement

ML, NLP, and RPA. Robust databases and data

warehouses are also essential for efficiently storing

and managing large volumes of data.

 Organizational Requirements

Training and continuous education of employees

are crucial to ensure they acquire the necessary skills

and knowledge to handle the new AI technologies.

Furthermore, clear roles and responsibilities for

implementing and managing the framework need to

be defined, including appointing data stewards and AI

specialists. A comprehensive change management

plan is also required to promote acceptance and

engagement among employees and to ensure a

smooth introduction of new technologies. Challenges

in implementation include ensuring data quality and

availability, as well as addressing data protection and

ethical concerns through robust data management

strategies and clear data protection policies.

4.3 Challenges and Solutions

Adopting an AI-powered framework for data

governance offers numerous benefits but also

presents significant challenges. One of the greatest

strengths of the framework is the automation and

efficiency improvements enabled by AI technologies.

These advancements lead to a significant

enhancement of data quality, increased security, and

improved compliance (see Figure 2).

• Data Quality and Availability

Challenge: Ensuring that data is complete and

accurate is one of the biggest challenges. Missing or

incomplete data can significantly impair the

effectiveness of AI algorithms.

Solution: Implement robust data management

strategies, including clear guidelines and standards to

ensure data quality. This can be supported by regular

data reviews and cleansing processes.

• Data Privacy and Ethical Concerns

Challenge: The use of AI must comply with data

protection laws and consider ethical standards to

maintain stakeholder trust.

Solution: Develop and implement clear data

privacy policies and practices. Organizations should

ensure that all employees are informed about the

importance of data protection and receive appropriate

training.

• Technological Complexity

Challenge: The implementation and

maintenance of AI systems require specialized

technical knowledge and resources, which are not

always readily available.

Solution: Invest in training and continuing

education for employees to build the necessary

technical skills and knowledge. Collaboration with

specialized technology providers and consultants can

also be beneficial.

Smart Data Stewardship: Innovating Governance and Quality with AI

191

Figure 3: Recommendations for Implementing an AI-Powered Data Governance Framework.

4.4 Recommendations for

Organizations

This section presents key recommendations for

organizations to successfully implement an AI-

powered framework for optimizing data governance

and data quality. By following these guidelines,

organizations can effectively address challenges and

maximize the benefits of AI integration. See Figure 3

for a visual representation of these recommendations.

• Comprehensive Requirement Analysis

Organizations should conduct a thorough

requirement analysis to identify specific needs and

goals. This can be achieved through qualitative and

quantitative methods such as stakeholder interviews,

surveys, and data analyses.

• Selection of Appropriate Technologies

The selection of the right AI technologies should

be based on a detailed evaluation of technical

requirements and available solutions. Organizations

should consider factors such as scalability,

compatibility, user-friendliness, and cost.

• Training and Education

Organizations should invest in the training and

education of their employees to ensure they acquire

the necessary skills and knowledge to handle new AI

technologies. This can be facilitated through training

sessions, workshops, and continuing education

programs.

• Development of Robust Data Management

Strategies

Organizations should develop and implement

clear data guidelines and standards to ensure data

quality and availability. This includes the

establishment of processes for continuous monitoring

and improvement of data quality.

• Data Privacy and Ethical Considerations

Organizations should ensure that their AI-

powered systems comply with data protection laws

and consider ethical standards. This requires the

development and implementation of clear data

privacy policies and practices.

• Collaboration with Experts

Organizations should consider collaborating

with specialized technology providers and

consultants to support the implementation and

maintenance of AI systems. This can facilitate access

to expertise and resources, thereby enhancing the

efficiency of the implementation process.

By following these structured recommendations,

organizations can maximize the benefits of an AI-

powered framework for optimizing data governance

and data quality, while effectively addressing the

associated challenges.

5 CASE STUDIES AND

PRACTICAL EXAMPLES

To illustrate the practical applicability of the AI-

powered framework for optimizing data governance

and data quality, detailed case studies and practical

examples are presented below. These examples

demonstrate how the framework has been

successfully implemented in various organizations

and the resulting benefits.

5.1 Case Study 1: Healthcare

Organization

Challenge: A large healthcare organization faced the

challenge of significantly improving the quality and

security of patient data. Due to the variety and volume

of data, errors and inconsistencies frequently

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occurred, impacting medical care. Manual data

reviews and corrections were time-consuming and

inefficient, leading to delays and increased error rates.

Solution: To address these challenges, the

organization implemented an AI-powered system for

automatic data cleansing and anomaly detection.

Machine learning (ML) was used to systematically

identify and correct data errors. The AI system

continuously analyzed incoming patient data for

inconsistencies and deviations from established

standards. Once an error was detected, the system

could automatically initiate corrective actions or

notify the responsible staff.

Approach: Data Integration: All relevant

patient data from various sources were first integrated

into a central database. ML algorithms helped

harmonize and structure different data formats.

Anomaly Detection: An advanced ML model

was trained to detect anomalies and inconsistencies in

the data. These algorithms were capable of

identifying both simple errors like missing values and

complex anomalies like unusual diagnoses.

Automatic Data Cleansing: After anomaly

detection, the system performed automatic data

cleansing processes, removing duplicates and

correcting faulty entries. For more complex issues,

the staff was notified to conduct manual reviews.

Outcome: Implementing the AI system

significantly improved data quality and security.

Errors and inconsistencies were drastically reduced,

leading to higher accuracy of medical data. This not

only increased the efficiency of medical care but also

improved patient satisfaction, as treatment decisions

were based on more reliable data. Overall, the

organization reduced the error rate in patient data by

60% and shortened data processing time by 40%.

5.2 Case Study 2: Financial Institution

Challenge: A large financial institution faced the

challenge of early detection and prevention of

fraudulent activities in transaction data. Traditional

methods for fraud detection were inefficient and often

resulted in false alarms or delayed responses, causing

financial losses and erosion of customer trust.

Solution: The institution opted to implement an

AI-powered system for real-time detection of

fraudulent activities. ML algorithms and natural

language processing (NLP) tools were used to

analyze transaction patterns and identify anomalies.

The system was designed to continuously monitor

transaction data and immediately respond to

suspicious activities.

Approach: Data Collection and Integration:

Transaction data from various sources were collected

and integrated into a central system. Historical data

were also included to train the ML models.

Model Development: Various ML models,

including supervised and unsupervised learning, were

developed to detect fraudulent patterns. These models

were continuously trained and refined with new data.

NLP Analysis: In addition to the ML models,

NLP tools were used to analyze text data from

transaction descriptions and identify semantic

patterns indicative of fraudulent activities.

Real-Time Monitoring: The system

continuously monitored all transactions in real-time.

Upon detecting an anomaly or potential fraud,

immediate actions were taken, such as freezing the

affected account and notifying the customer and the

security team.

Outcome: The implementation led to a

significant reduction in fraud cases. Real-time

monitoring and analysis of transaction data increased

the security of financial transactions and enabled

quick responses to suspicious activities. The number

of fraud cases was reduced by 70%, and the accuracy

of fraud detection increased to over 90%.

Additionally, customer trust in the security of their

transactions improved significantly, leading to

stronger customer retention.

5.3 Analysis of Benefits and Lessons

Learned

The analysis of the benefits shows that the adoption

of the AI-powered framework led to significant

efficiency gains. Routine tasks were automated,

reducing manual effort and improving data

processing accuracy. The improvement in data

quality was achieved through AI algorithms capable

of identifying and correcting data errors in real-time.

Moreover, data security was enhanced by proactive

detection and prevention of security breaches.

Lessons Learned: Importance of High-Quality

Data: High-quality and complete data are essential

for the success of AI systems. Organizations must

invest in robust data management strategies to ensure

data integrity and quality.

Training and Education: Successful

implementation of AI systems requires well-trained

and educated employees. Organizations should

continuously invest in training their staff to equip

them with the necessary technical skills and

knowledge.

Flexible IT Infrastructure: A flexible and

adaptable IT infrastructure is crucial to meet changing

Smart Data Stewardship: Innovating Governance and Quality with AI

193

requirements. A scalable infrastructure facilitates the

implementation and use of AI technologies, enabling

organizations to respond quickly to new challenges.

These case studies highlight the diverse

applications and practical benefits of the framework

in various contexts. By applying the lessons learned,

organizations can maximize the advantages of AI

technologies while effectively addressing the

challenges.

6 DISCUSSION

Although a direct evaluation was not conducted in

this article, the assumed benefits of the AI-powered

framework are based on a comprehensive analysis of

theoretical and practical case studies from the

literature. Implementing such a framework has the

potential to achieve significant improvements in data

governance. By automating routine tasks and

continuously monitoring data quality, the efficiency

and accuracy of data processing can be enhanced.

Specifically, the use of ML algorithms for data

cleansing and anomaly detection offers the possibility

to identify and correct data errors in real-time, leading

to improved data quality. Additionally, proactive

monitoring and detection of security breaches can

significantly enhance data security.

Compared to traditional approaches, the AI-

powered framework offers several key advantages.

Traditional data governance methods often rely on

manual processes that are time-consuming and prone

to errors. The AI-powered framework automates

many of these processes, thereby increasing

efficiency and minimizing human errors. Real-time

analysis and monitoring of data enable faster

responses to anomalies and security threats, which is

often not possible to the same extent with traditional

approaches. Furthermore, the ability to process large

volumes of data in real-time and recognize patterns

provides a distinct advantage over traditional

methods.

Despite the theoretical and practical advantages,

there are still areas for improvement and future

research. A central area is the continuous

development and refinement of algorithms to further

enhance their accuracy and efficiency. Moreover,

integrating AI technologies into existing IT

infrastructures is often complex and requires further

research to optimize these processes. Another

important research field concerns the ethical and legal

implications of using AI in data governance,

particularly regarding data privacy and data integrity.

Future studies should also focus on developing more

user-friendly AI tools to promote their acceptance and

use in non-technical domains.

7 CONCLUSIONS

The theoretical analysis and case studies indicate that

AI technologies can significantly enhance data

governance. Automation and real-time analysis can

greatly improve data quality and security. The case

studies demonstrated practical applications and the

benefits achieved in various organizational contexts.

AI will play an increasingly important role in the

future of data governance. AI's ability to efficiently

process large volumes of data, recognize patterns, and

proactively respond to anomalies will be crucial in

addressing the challenges of the modern data

landscape. AI technologies will enable organizations

to continuously improve their data governance

practices and meet growing demands.

Deploying an AI-powered framework requires

careful planning and execution. Organizations should

conduct a comprehensive requirements analysis,

select appropriate technologies, and invest in training

and educating their employees. Clear data

management strategies and data privacy policies are

essential to ensure data integrity and security.

Collaboration with experts and continuous

monitoring and optimization of AI systems are also

critical success factors.

By following a structured implementation

approach and considering these recommendations,

organizations can maximize the benefits of an AI-

powered framework for optimizing data governance

and data quality, while effectively addressing the

associated challenges.

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