Using Data Analytics for Risk Management in Strategy

Here's my cheat sheet for a first-pass quantitative risk assessment. Use this as your “day-one” playbook when leadership says: “Just give us a first pass. How bad could this get?” 1. Frame the business decision - Write one sentence that links the decision to money or mission. Example: “Should we spend $X to prevent a ransomware-driven hospital shutdown?” 2. Break the decision into a risk statement - Identify the chain: Threat → Asset → Effect → Consequence. Capture each link in a short phrase. Example: “Cyber criminal group → business email → data locked → widespread outage” 3. Harvest outside evidence for frequency and magnitude - Where has this, or something close, already happened? Examples: Industry base rates, previous incidents and near misses from your incident response team, analogous incidents in other sectors 4. Fill the gaps with calibrated experts - Run a quick elicitation for frequency and magnitude (5th, 50th, and 95th percentiles). - Weight experts by calibration scores if you have them; use a simple average if you don’t. 5. Assemble priors and simulate - Feed frequencies and losses into a Monte Carlo simulation. Use Excel, Python, R, whatever’s handy. 6. Stress-test the story - Host a 30-minute premortem: “It’s a year from now. The worst happened. What did we miss?” - Adjust inputs or add/modify scenarios, then re-run the analysis. 7. Deliver the first-cut answer - Provide leadership with executive-ready extracts. Examples: Range: “10% chance annual losses exceed $50M.” Sensitivity drivers: Highlight the inputs that most affect tail loss Value of information: Which dataset would shrink uncertainty fastest. Done. You now have a defensible, numbers-based initial assessment. Good enough for a go/no-go decision and a clear roadmap for deeper analysis. This fits on a sticky note. #riskassessment #RiskManagement #cyberrisk

If you think data visualization and statistics don’t apply to FP&A -- consider just how much valuable information is hidden away in those financial processes. For instance, understanding not only the average days payable but also the variance around those payables can shed light on potential risks or opportunities. The same approach can be applied to other metrics, such as sales forecasts or overhead expenses: analyzing forecast accuracy, identifying anomalies, or even spotting correlations between different expense lines can significantly enhance strategic decision-making. Of course, transforming raw spreadsheets and disparate systems into a structured, analysis-ready format requires effort, but it pays off once those cleansed datasets are in place. With the right data visualization and statistical techniques, these metrics become more than just numbers on a page -- they become actionable insights that drive better decisions. FP&A actually benefits substantially from this kind of analysis, and those who overlook its potential may be missing out on valuable guidance. Embracing data analytics and visualization can help surface insights that might otherwise remain buried and give organizations a more comprehensive view of their financial health and future direction.

Data and tariffs – what can data leaders do? You might assume there's little data leaders can do in the short term to help deal with the impacts of changing tariff policies in the US. That is absolutely 𝐧𝐨𝐭 the case. There are plenty of things they can do - or arguably - should have already done.  We learned many of these lessons in the global pandemic – as there were drastic impacts to the demand and supply sides of our businesses that could have been significantly mitigated with better data (and data management). The impacts to supply chains then, are not unlike what we're seeing unfold now. So, if you are in a position of influence in a data and analytics function, I recommend you quickly work to more deeply understand: 1.  Your customer relationships & behaviors 2.  Any dependencies or risks in your supply chains 3.  All product and material / ingredient related data What do all these things have in common? They are require a focus on 𝐦𝐚𝐬𝐭𝐞𝐫 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – as it's literally the data running the most critical aspects of your business. Unfortunately, most companies I’ve worked with struggle with creating ‘360’ views of these critical data domains, thanks to data silos that are running amuck. In a time of global disruption, this lack of visibility on your customers, products, and supply chain is creating massive risks for your business. If you’re one of these companies, then figuring out how to solve your MDM problem – quickly – must be a top priority.  Some things to consider: ✅ 70% of a customer/supplier master is better than none.  Perfection is the enemy. ✅ Forget that data cleanup, and forget doing a physical system consolidation. You don't have time, and besides, they aren't a hard requirement to create actionable insights. ✅ Use third party data to help accelerate your efforts. ✅ Forget that elaborate data governance framework or operating model.  You don’t have time for either. Develop a maniacal focus on using an analytical MDM to provide more accurate and complete product, customer, supplier, and material related data. ✅ Don't bother with that expensive maturity assessment from a top tier consultant. It won't help you much, and spoiler alert - I can probably already guess you're a maturity of 2 - 2.5 (out of 5) on most data management capabilities. ✅ Data catalogs are great, but you don't need one to solve your problem. Chances are, you already know where the most relevant master data is within your ecosystem. ✅ Yes, you optimally need business engagement on MDM - but for many, the risks here are existential. Bold CDOs should be ready to move quickly and confidently - and seek forgiveness later. Analytical styles of MDM can be deployed in weeks, not years - if you do things the right way. During Covid, too many companies were caught flat-footed with a lack of master data insights. Will that be you this time around? #cdo #masterdata #mdm

Agentic AI is completely changing the risk workflow. Here are my recommendations for setting your team up for success: Risk management is undergoing a fundamental transformation. It's the lethal combination of more and more data with fewer and fewer insights. Teams are swamped. They're toggling between systems, manually correlating information, and spending more time gathering data than making decisions 👎 I've spent years watching analysts open multiple tabs, run the same Google searches, and manually piece together risk narratives. The thing is, analysts actually learn some things from this, but it's all stuck in tribal knowledge. They need to get this knowledge into an agent, fast. 🔥 My tips: 1. DATA SYNTHESIS, NOT DATA GATHERING Your risk agents should deliver the "net net" - key findings, risk indicators, and mitigating factors, not raw data dumps requiring manual analysis. 🧠 2. PROACTIVE MONITORING INSTEAD OF REACTIVE ALERTS "Can you research if there are any lawsuits against this merchant?" should be a question your agent has already answered before you ask. ⏱️ 3. CUSTOMIZED RISK NARRATIVES Different businesses have different risk profiles. Towing companies typically have low online ratings - your agent should understand industry-specific context when flagging risk. 🎯 4. GUIDED INVESTIGATION PATHS Junior analysts should have the benefit of embedded expertise: "A senior analyst would check X next because of Y" - turning every team member into a risk expert. 🧭 5. AUTONOMOUS RESEARCH CAPABILITIES "Find all similar merchants in our portfolio with this risk pattern" should be a simple request, not a complex SQL project. 🤖 The most valuable risk teams are shifting from data gathering to strategic decision-making. If you want to put yours on that path, let's chat 👀