The Silent Architects: How Data Governance and Stewardship Are Reshaping the Future of Enterprise Intelligence

- Data governance and data stewardship have a close relationship with each other. In order to start the governance and stewardship program, a better understanding of the concept and key elements is essential. In this paper, the overall of data governance and stewardship is discussed including the definition, goal, elements, focus area, framework, role, and responsibilities, how to implement and also how to measure its maturity state. Thus, this will give an insight into how to establish data governance and data stewardship in an organization.

In the current data‐driven landscape of wildlife conservation, data sovereignty (i.e. governance and security) is fundamental to determining how knowledge is created and applied to pressing biodiversity concerns. Western science increasingly champions open data, which uplifts data stewardship and sharing to make data more accessible. At the same time, Indigenous Peoples stress the importance of Indigenous data sovereignty (IDS) to assert Indigenous rights over Indigenous data. These two philosophies offer distinct approaches to data sovereignty, creating possible tensions in collaborations between Western scientists and Indigenous Peoples. Awareness of this issue in wildlife conservation is slowly growing, and practical guidance is needed for balancing IDS and open data in collaborations. Here, we review current literature to identify challenges in bringing IDS and open data together in collaborative wildlife conservation. We include specific commentary for wildlife sensors (camera traps and autonomous recording units) due to the growing use and demonstrated interest in open data practices for these technologies and their appeal in Indigenous‐led conservation. We describe three nested themes of data sovereignty: collaborative relationships, data governance and data stewardship. Within these themes, we identify nine recommendations to navigate potential IDS and open data tensions in collaborations, stressing the need to focus on relationship building, support Indigenous data governance agendas and to discuss upfront expectations for data stewardship. Additionally, we suggest reflective questions to consider throughout the data life cycle. This provides a framework for understanding the sociocultural implications of wildlife data and supporting IDS throughout the collaborative process, while identifying opportunities to apply open data stewardship practices. Synthesis and applications: Our work demonstrates the need for explicit recognition of IDS principles for wildlife data and proposes general guidance for addressing data sovereignty in collaborative initiatives. We recognize that our recommendations are not exhaustive and that each collaborative context brings unique challenges and opportunities. We invite continued discussion on data governance and stewardship to promote shared learning. Our recommendations can be applied more broadly to facilitate a new approach to data sovereignty in collaborations across the natural sciences. Read the free Plain Language Summary for this article on the Journal blog.

U.S. health policy is engaged in a struggle over access to health information, in particular, the conditions under which information should be accessible for research when appropriate privacy protections and security safeguards are in place. The expanded use of health information-an inevitable step in an information age-is widely considered be essential to health system reform. Models exist for the creation of data-sharing arrangements that promote proper use of information in a safe and secure environment and with attention to ethical standards. Data stewardship is a concept with deep roots in the science and practice of data collection, sharing, and analysis. Reflecting the values of fair information practice, data stewardship denotes an approach to the management of data, particularly data that can identify individuals. The concept of a data steward is intended to convey a fiduciary (or trust) level of responsibility toward the data. Data governance is the process by which responsibilities of stewardship are conceptualized and carried out. As the concept of health information data stewardship advances in a technology-enabled environment, the question is whether legal barriers to data access and use will begin to give way. One possible answer may lie in defining the public interest in certain data uses, tying provider participation in federal health programs to the release of all-payer data to recognized data stewardship entities for aggregation and management, and enabling such entities to foster and enable the creation of knowledge through research.

The Moving Ahead for Progress in the 21st Century Act (MAP-21) established a performance- and outcome-based transportation program for safety, infrastructure condition, congestion reduction, system reliability, freight movement, environment sustainability, and reduced project delivery delays. Transportation data are essential in addressing those challenges. Data are valued assets, but they carry a significant risk–-bad data can lead to ineffective planning and ultimately to poor agency business decisions. An effective transportation data business plan coupled with institutional data governance can mitigate the risk by providing an approach for delivering comprehensive, quality data. Better data provide better information, which in turn results in informed decisions. A data business plan with an established data governance environment can lead to proactive rather than reactive decisions. Many state departments of transportation have embraced such concepts and best practices and are beginning to apply them in overall data governance. However, the terms and application are not mainstreamed and are not assimilated into the transportation agency culture. This paper provides the context for data management, data governance, and data stewardship; a business need for establishing data governance in a transportation agency; key features to be considered for a data business plan; approaches to developing a data governance program; and finally a process for evaluating data program governance.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

Centring equity in data‐driven public health: a call for guiding principles to support the equitable design and outcomes of Australia's data integration systems

Chapter Objectives In this chapter, you will learn to: • identify the key challenges and approaches for data and process integration; • understand the basic mechanisms of searching unstructured data within an organization and across the World Wide Web; • define data quality as a multidimensional concept and understand how master data management (MDM) can contribute to it; • understand different frameworks and standards for data governance; • highlight more recent approaches in data warehousing, data integration, and governance. Opening Scenario Things are going well at Sober. The company has set up a solid data environment based on a solid relational database management system used to support the bulk of its operations. Sober's mobile app development team has been using MongoDB as a scalable NoSQL DBMS to handle the increased workload coming from mobile app users and to provide back-end support for experimental features the team wants to test in new versions of their mobile app. Sober's development and database team is already paying attention to various data quality and governance aspects: the RDBMS is the central source of truth, strongly focusing on solid schema design and regular quality checks being performed on the data. The NoSQL database is an additional support system to handle large query volumes from mobile users in real-time in a scalable manner, but where all data changes are still being propagated to the central RDBMS. This is done in a manual manner, which sometimes leads to the two data sources not being in agreement with each other. Sober's team therefore wants to consider better data quality approaches to implement more robust quality checks on data and make sure that changes to the NoSQL database are propagated to the RDBMS system in a timely and correct manner. Sober also wants to understand how their data flows can be better integrated with their business processes. In this chapter we will look at some managerial and technical aspects of data integration. We will zoom in on data integration techniques, data quality, and data governance. As companies often end up with many information systems and databases over time, the concept of data integration becomes increasingly important to consolidate a company's data to provide one, unified view to applications and users.

This session will be an introductory conversation to understand the fundamentals of data governance and stewardship. Attendees will be introduced to the core principles of data governance and the role of data stewards. We will talk about strategies for governance and stewardship at your institution, and how to work within roles and frameworks that may already exist at your institution. We’ll also talk about how to build this framework and external considerations you need to consider.

Data Governance and Data Management need to work hand-in-hand. Data Governance provides the oversight, measurement, and communication while Data Management provides the tactical operations to achieve desired outcomes. It is important to understand how Data Governance and Data Management align in support of larger business goals. It is important to understand how Data Governance and Data Management align in support of larger business goals. A great first step to gaining this understanding is to examine an overall framework of a program and its components parts. They can be broken down into Data Governance, Data Management, Data Stewardship, Business Drivers, Solutions, and Methods. The SAS Data Management Framework breaks down each of these components. Data architecture policies include statements about data models, data movement, data sharing, data integration, data standards, ETL standards, data access, and service level agreements. Data life cycle policies will pertain to the management of data from its creation to its eventual destruction.

Health information, over the years, has been regarded as one of the crucial assets in the landscape of health on the grounds of the myriads of benefits derived from it. It plays a major role in policy planning and implementation, quality improvement, clinical decision-making, care continuity, disease surveillance, etc., making it an integral part of healthcare delivery. This substantiates the pressing need for an appropriate information governance (IG) system, ensuring proper data stewardship, measures to ensure data quality and that preserving the privacy and confidentiality of patient data are in place. As part of conscious efforts to improve health information governance, this study examines the entire information governance structure of healthcare facilities used as study areas, taking into account the effectiveness of data stewardship, data quality management and compliance with regulatory requirements. Employing a cross-sectional study design, data was collected from 432 healthcare professionals emanating from Cape Coast Teaching Hospital (CCTH), Eastern Regional Hospital Koforidua (ERHK) and Ledzokuku-Krowor Municipal Assembly (LEKMA) Hospital, all in Ghana. A semi-structured questionnaire, demarcated into five (5) parts, was used to collect the data. Closed-ended questions were analyzed with Stata 15.0 involving frequencies, percentages, means, standard deviation, standard errors and confident intervals. A chi-square test of independence was deployed to ascertain relationships between variables. Open-ended questions were coded and analyzed with Qualitative Data Analysis Miner 6.0, deploying inductive thematic analysis. The level of understanding of data stewardship (2.87/4.0), familiarity with concepts of data quality (2.73/5.0) and ethical considerations and privacy (3.13/5.0) when dealing with patient health data were found to be varied among the professionals, overall rated as moderate and affirming the need for improvement. The level of knowledge and understanding on these pertinent areas were highly influenced by type of profession (p < 0.001; p = 0.031 and, p < 0.001) respectively and been indicative that some type of health workers is much concerned about data stewardship and health information governance than others. Various methods and efforts were in place to ensure the quality of data; however, hampering these attempts included factors such as poor data collection methods, lack of resources, inadequate competency and some behavioral factors. In addition to these factors is the low level of training on data stewardship (1.86/4.0), which heavily leads to skewness in knowledge. The majority of healthcare professionals were familiar with and advocated for adherence to regulatory requirements such as HIPAA (33.7%), GDPR (21.3%) and HITECH Act (14.4%), in making data governance much robust. Efforts were also identified in ensuring privacy and confidentiality during the storage, processing and transmission of patient data mostly by deploying methods such as authentication (58.9%), use of secured computers and software (71.2%) and using secure electronic platforms (66.0%) respectively indicating a keen attention for data stewardship and governance. About 70% of healthcare professionals attested to the regular conduct of monthly compliance audits, however, the rate of training on data stewardship was discovered to be very low (1.86/4.0). There is an existence of clear policies and procedures to guide data stewardship to enhance overall information governance, however, healthcare professionals' level of knowledge on subjects of information governance ought to be enhanced. There is a need to intensify training sessions on data quality, and regulatory requirements for health information governance to mitigate the gaps in knowledge among professionals.

Chapter 1 - Data Stewardship and Data Governance: How They Fit Together

Data management is important to organisations as it can improve business operations and emerge competitive advantages. Systematic data management may also transform organisations to become data-driven organisations that business decisions are based on insights from the raw data. However, organisations from across industries might face different challenges in establishing an effective data management system. Data governance is an approach to manage data in organisations focusing on the decision-making processes. By practising data governance, stakeholders in organisations are likely to receive awareness and understand their rights, duties, and responsibilities toward the data they create and utilise. With this regard, data governance approach may leverage data management in organisations. This study aims to propose a framework and guideline to apply data governance in higher educational organisations. Particularly in the context of an initiative to transform the organisations to data-driven organisations. This study is qualitative research. Data in this study were gathered from the literature review and the semi-structured interviews with five staff from the faculty as data stewards. The interviews aimed to explore the current state scenario and challenges in applying data governance. As a result, the framework of this study was further designed to provide a guideline to initially practise data governance to manage data in the organisation.

The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

Data-driven government: Cross-case comparison of data stewardship in data ecosystems

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, &amp;#8216;a data steward&amp;#8217;, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity.&amp;#160;The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require&amp;#160; &amp;#8216;human in the loop&amp;#8217; within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.&amp;#160;