Data Governance Essentials Handbook
Definitions of Data Governance are numerous. However, they agree on a key principle - that it’s dedicated to the organisation of people, processes, and technology to enable effective data management.
Inherent Risk vs Residual Risk: Are They Still Relevant in Today’s Volatile Business Environment?
Inherent and residual risks remain vital in enterprise risk management, even amid today’s volatile business environment. Inherent risk reflects exposure before controls, while residual risk is what remains post-mitigation. Adapting these frameworks through dynamic assessments, analytics, and resilience-building ensures organisations effectively address evolving threats and thrive in uncertainty.
Development of Narrative Intelligence within Risk Management Teams
Training narrative intelligence within risk management teams involves educating them on narrative concepts, leveraging AI tools, fostering cross-departmental collaboration, and developing storytelling skills. Practical steps include workshops, technology investment, and continuous learning. Overcoming challenges like resistance to change and data complexity ensures teams proactively manage risks and craft impactful counter-narratives.
Narrative Intelligence for Risk Professionals – Moving closer to True Risk
Narrative intelligence empowers risk professionals to identify, analyse, and respond to emerging narratives that influence organisational reputation and risk exposure. By leveraging AI, sentiment analysis, and stakeholder mapping, it enhances decision-making, mitigates reputational risks, and builds resilience. Proactively integrating narrative intelligence into risk frameworks is essential for navigating today’s complex, interconnected world.
Navigating the Risk Revolution: Trailblazers Shaping 2025’s Uncharted Terrain
The risk management landscape of 2025 is shaped by AI-driven insights, climate risk integration, and collaborative approaches to tackle complex challenges. Key players, including AI specialists, cybersecurity experts, and ESG analysts, are transforming risks into opportunities. Organisations embracing innovation and resilience will thrive in this dynamic, interconnected world of emerging risks.
Navigating Complexity: The Role of Risk Maturity Models in Modern Business
In an era marked by unprecedented uncertainty and interconnected risks, organisations are increasingly turning to risk maturity models as a strategic tool to bolster their resilience and competitiveness. Effective risk management is crucial for survival and success, and this is where risk maturity models come into play.
Data Governance and Data Policies at the European Commission
Data governance sets out a framework with clear roles, and the responsibilities and interdependencies of those roles.
Data policies introduce common principles, guidance and working practices in the areas of data management, data interoperability
and standards, and data quality. Equally important are the areas of data protection, information security and intellectual property. However, these are not the focus of this document. Corporate data policies do not specify detailed processes. This allows Directorates-General (DGs)/services to organise themselves in the way that best suits their internal organisation,
while ensuring coordination and alignment across the Commission, including its executive agencies.
Data Governance: A conceptual Framework, Structured Review, and Research Agenda
Data governance refers to the exercise of authority and control over the management of data. The purpose of data governance is to increase the value of data and minimize data-related cost and risk. Despite data governance gaining in importance in recent years, a holistic view on data governance, which could guide both practitioners and researchers, is missing. In this review paper, we aim to close this gap and develop a conceptual framework for data governance, synthesize the literature, and provide a research agenda.
Data Governance – Providing Assurance regarding Data Risk Management
While the concept of risk related to data ethics is relatively new, Chief Audit Executives (CAEs) predict that its relevance will grow rapidly over the next five years. Complexity in the collection, analysis, and use of data is expanding rapidly, complicated by artificial intelligence.
Going Digital: Guide to Data Governance Policy Making
The Going Digital Guide to Data Governance Policy Making (hereafter the
Guide) aims to advance the development, revision and implementation of policies for data governance, by helping to overcome key related policy tensions. Addressing the complexities arising from the nature of data as an intangible infrastructural resource of global strategic importance, the Guide helps policy makers design effective, technology-neutral, forward-looking and coherent data governance policies across sectors, policy domains and jurisdictions. It proposes a set of questions and highlights promising policy approaches based on three policy tensions and objectives that characterise
data governance policy making: 1) Balancing data openness and control while maximising trust; 2) Managing overlapping and potentially conflicting interests and regulations related to data governance; and 3) Incentivising investments in data and their effective re-use.