Integrating Multiple Data Sources in Risk Assessment

Flood resilience is not just an infrastructure challenge. It is a data coordination challenge. We have been working with Ordnance Survey on an Intelligent Flood Readiness Model to explore how existing datasets can better inform national and local decision-making. With England experiencing well-above-average rainfall in early 2026, including record levels in some regions, the limitations of static planning cycles are becoming increasingly visible. Using Snowflake as the intelligence layer, we brought together building-level data, deprivation indices, and flood risk policy datasets to create a more integrated view of exposure and vulnerability. The findings highlight important considerations for policy: ➡️ Up to 1.2 million buildings may sit outside current flood defenses ➡️ 68% are in the most deprived communities, raising questions of equity and resilience ➡️ 85% are exposed to surface water flooding, which remains underrepresented in planning discussions ➡️ 84% were built before flood risk was systematically embedded into planning policy. This is not about identifying gaps in any single dataset. It is about what becomes visible when data held across institutions is connected and analyzed collectively. There is a clear opportunity to complement existing Flood Risk Management Plans with more dynamic, data-driven approaches. This can support better prioritization of interventions, more targeted investment, and improved long-term resilience. The same principle applies beyond flooding. Many complex policy challenges depend on fragmented datasets owned by different organizations. Connecting them can materially improve decision-making. Data sources used in the solution include: 1️⃣ Buildings Data from Ordnance Survey 2️⃣ Indices of Multiple Deprivation from the Ministry of Housing, Communities and Local Government 3️⃣ Flood Risk Management Policy Documents 2021-2027 from the Environment Agency 4️⃣ Flood Defenses from the Department for Environment, Food and Rural Affairs Further details: https://lnkd.in/e6UYfFAZ Rebecca O'Connor | Camilla Dowson | Daniel Reeves | Tim Chilton | Abs Gandhi | Katherine James |

Most asset failures are avoidable when risks are systematically identified and managed. After years of working with industrial facilities, I've found that effective risk management requires mastering five complementary frameworks: 1) HAZOP/HAZID: The foundation of process safety • HAZID provides early, broad-brush hazard identification • HAZOP deliversa systematic analysis of process deviations • Digital transformation now allows these assessments to feed directly into maintenance systems 2) FMEA (Failure Modes and Effects Analysis) • The comprehensive failure analysis framework • Now enhanced through digital twins that can simulate thousands of potential scenarios • Predictive models identify vulnerabilities that would be impossible to spot manually 3) CRA (Corrosion Risk Assessment) • Specialized analysis for material degradation mechanisms • Modern distributed sensing networks detect moisture ingress and corrosion in real-time • Early detection means addressing issues months before traditional methods would find them 4) RBI (Risk-Based Inspection) • The intelligence layer that optimizes inspection resources • AI algorithms now continuously recalculate priorities as conditions change • No more relying on outdated static schedules or calendar-based inspections 5) IOW (Integrity Operating Windows) • Defines the safe operational limits for process variables • Real-time monitoring ensures operations stay within these boundaries • Automatic alerts when parameters approach critical thresholds The power comes from integration. One refinery I worked with linked all five frameworks through a unified digital platform. Their system automatically flags when operating conditions might trigger corrosion mechanisms identified in their CRA, then updates inspection priorities in real-time. Is your organization still managing these as separate activities, or have you begun integrating them into a cohesive digital risk management strategy? *** P.S.: Looking for more in-depth industrial insights? Follow me for more on Industry 4.0, Predictive Maintenance, and the future of Corrosion Monitoring.

I am currently modeling annualized loss expectancy for supply chain breaches to meet NIS 2 compliance requirements. This shift empowers chief information security officers to demonstrate the real return on investment for security spending. It transforms compliance from a necessary cost into a strategic protector of value. Because NIS 2 mandates proportionate measures, quantifying risk ensures capital flows to the most critical vulnerabilities. Relying on qualitative criteria and static scoring for vendor segmentation is a dangerous waste of time. These biased methods fail to capture dependencies and offer zero protection against negligence claims. In a regulatory audit, a subjective "high risk" label crumbles without data to back it up. We must move beyond indefensible guesswork to rigorous, quantifiable models that withstand legal scrutiny. Static questionnaires and qualitative heat-maps collapse under scrutiny: they miss hidden dependencies, ignore Nth-party concentration risk, and produce rankings that change dramatically depending on who fills them out. When the inevitable breach happens through an overlooked subcontractor, that spreadsheet becomes exhibit A in the negligence claim against you and the board. I prefer using unsupervised machine learning with K-Means clustering to segment vendors dynamically based on real-time risk data. This method automates the detection of outlier vendors that manual assessments miss. I often remind colleagues and students that risk extends far beyond direct suppliers. We utilize graph theory and centrality metrics to map Nth-party dependencies. This reveals systemic concentration risks deep in the supply chain. By detecting bridge nodes or subcontractors serving multiple critical vendors, you can preempt cascading failures that traditional audits ignore. Proficiency in network analysis is now a critical competency for compliance roles. We must also operationalize Software Bills of Materials beyond NIS2 compliance boxes. They are strategic tools for rapid vulnerability management and zero-day response. Integrating analysis into the procurement lifecycle allows organizations to shift security left and vet product integrity before contracts are signed. Experts who bridge legal procurement and technical vulnerability management will lead Security by Design initiatives in major technology firms. Finally, consider the personal liability NIS 2 places on top management. You need a robust governance framework that documents due diligence through regular reporting and signed accountability statements. This translates technical supply chain risks into business continuity impacts the Board understands and accepts. Switch to algorithmic clustering on annualized loss expectancy, dependency centrality, incident history, and SBOM entropy to develop a segmentation model that survives daylight. Anything else is theater. #RiskManagement #NIS2 #SupplyChainSecurity #QuantitativeRisk #CISO

Better accounting for uncertainty in flood-risk assessments can change scientific conclusions. However, many flood-risk assessments overlook key uncertainties due to the lack of tools integrating relevant sources of information. My colleagues (James Doss-Gollin, Vivek Srikrishnan, and Klaus Keller) and I developed a new software package called UNSAFE (UNcertain Structure and Fragility Ensembles) that fills this gap for a large proportion of structures exposed to flooding. UNSAFE allows a technical user base to obtain more robust flood-risk estimates and identify the influence of uncertainties on risk estimates. As an open-source code base, the community of UNSAFE contributors can continue to add functionality that makes the framework more useful to analysts and decision-makers. Our new publication in the Journal of Open Source Software (https://lnkd.in/gwHhi-45) provides more information on the science underpinning UNSAFE. We've used UNSAFE for policy analysis (https://lnkd.in/ghYeW6sH), capturing flood-risk dynamics (https://lnkd.in/gDmgEzme), and isolating the effect of structure inventory errors on damage estimates (https://lnkd.in/gRB69PEZ). Hopefully it is something that can be useful for you as well. We would love your thoughts for improvement and contributions! (Here's the repo: https://lnkd.in/gEnGQjAz)

Data-Driven Risk Assessment (DDRA) Unlike traditional risk assessments, Data-Driven Risk Assessment (DDRA) relies on data analytics, predictive modeling, and real-time information to make risk management more proactive and precise. Elements of Data-Driven Risk Assessment: 1. Data Aggregation: DDRA starts with the collection and aggregation of data from various sources within an organization. This data can encompass financial records, operational data, cybersecurity logs, and more. 2. Data Analysis: The collected data undergoes rigorous analysis using statistical and machine learning techniques. This analysis identifies patterns, trends, and potential risk indicators that might be hidden within the data. 3. Predictive Modeling: DDRA often employs predictive models to forecast potential risks. These models take historical data and use it to predict future risk scenarios, enabling proactive risk mitigation. 4. Real-Time Monitoring: Unlike traditional risk assessments, DDRA doesn't stop at a single evaluation. It involves continuous, real-time monitoring of data streams to promptly detect and respond to emerging risks. 5. Scalability: DDRA can scale according to the organization's needs. It can handle vast datasets and adapt to different types of risks, from financial and operational to cybersecurity and compliance. Advantages of DDRA 1. Early Risk Detection: DDRA excels in identifying risks before they escalate into significant issues. This early detection allows organizations to take preventive actions. 2. Customized Risk Mitigation: By pinpointing specific risk factors through data analysis, DDRA enables organizations to tailor risk mitigation strategies to address their unique challenges. 3. Efficiency Gains: With automation and real-time monitoring, DDRA streamlines the risk assessment process, saving time and resources. 4. Data-Informed Decisions: DDRA empowers decision-makers with data-backed insights, facilitating informed choices that enhance risk management. 5. Competitive Advantage: Organizations that embrace DDRA gain a competitive edge by staying ahead of potential risks and optimizing their operations. Implementing Data-Driven Risk Assessment Successfully: 1. Data Quality Assurance: Ensure that the data collected and analyzed is accurate, up-to-date, and reliable to make informed decisions. 2. Cross-Functional Collaboration: Collaborate across departments to gather relevant data and insights, as risks often span multiple areas within an organization. 3. Technology Adoption: Invest in data analytics tools and platforms that support DDRA, including machine learning algorithms and real-time monitoring systems. 4. Regular Training: Train employees to understand DDRA concepts and use data-driven insights effectively in their roles. 5. Continuous Improvement: DDRA is an evolving process. Regularly review and update your risk models and data sources to enhance effectiveness.

Continuous Risk Assessment (CRA) -Objective Continuous Risk Assessment (CRA) transforms risk management from periodic, backward-looking assessments into real-time, predictive, and strategy-aligned risk intelligence. -How the Model Works Real-Time Risk Signals Live data from ERP, cyber logs, GRC systems, cloud platforms, market and third-party sources. Dynamic Risk Scoring Continuous recalculation of risk based on impact, likelihood, control effectiveness, and risk velocity. Predictive Risk Indicators Analytics identify trends, anomalies, and emerging risks before they materialize. ERM & Strategy Alignment Risk insights mapped to enterprise objectives, risk appetite, and board priorities. -Governance & Assurance Continuous audit validation of data, scoring logic, and models Clear thresholds and escalation aligned to board-approved risk appetite Defensible, regulator-ready risk reporting