Maureen Odum, a seasoned business intelligence analyst whose work spans government defence systems, global financial institutions, and grassroots NGOs, is at the forefront of this evolution. Her predictive dashboards and intelligence systems powered by tools like Python, R, and Power BI have informed multimillion-pound defence logistics decisions, driven billion-naira project savings, and reshaped how Africa thinks about data. In today’s digital transformation era, predictive analytics is no longer optional but essential.
In this executive spotlight Maureen shares how she brings data to life, turning spreadsheets into strategic foresight and enabling business leaders to make decisions not based on hindsight but on predictive insight. As a business intelligence leader with global experience transforming data into strategic insight, Odum specialises in predictive analytics, operational excellence, and regulatory intelligence across defence, finance, and engineering sectors.
Please walk us through how you’ve applied predictive modelling in mission-critical environments and how those insights influenced executive strategy or public-sector readiness
At MOD UK, I developed predictive models to monitor asset reliability, procurement risk, and contract compliance across sensitive operations. These models flagged system anomalies and logistic delays earlier, enabling defence stakeholders to prevent breakdowns rather than react to them. One model directly contributed to £3.6 million in annual labour savings. The insights didn’t sit on a report; they powered strategic briefings that influenced NATO-aligned defence readiness. Knowing that my work helped make high-stakes decisions smarter, faster, and safer is rewarding.
How do you decide which predictive models best suit an organisation’s operational challenges?
I start by defining the problem in business terms. Is it revenue leakage, project overrun, or donor churn? Then, I explore the data—patterns, noise, and limitations. Depending on the outcome, I select interpretable models like regression or more complex ones like XGBoost if performance justifies it. My rule is simple: it’s not the right model if it doesn’t add value in a decision-making room. Accuracy matters, but relevance and clarity matter more when lives, reputations, or millions are on the line.
Could you share an example where predictive modelling directly resulted in cost savings or revenue generation?
At Deloitte, I designed a procurement prediction tool using Power BI and DAX to identify which vendor cycles were prone to delays. We saved ₦3.6 billion within three months by restructuring lead time strategies and preempting bottlenecks. In another case, a variance risk model helped prevent a ₦2.1 billion loss on a national engineering rollout. These weren’t abstract predictions; they shaped executive decisions on the ground, directly saving money, protecting reputation, and reinforcing data as a strategic asset.
How do you ensure predictive dashboards remain accurate, relevant, and user-friendly over time?
Dashboards are not ‘fire-and-forget’ tools. I build auto-refresh logic, track model performance with error metrics, and incorporate user feedback loops. One defence dashboard I designed had over 80 per cent executive adoption because it offered role-specific insights and alerts in under three clicks. I believe in co-creation. Suppose users don’t evolve with the dashboard; worse, if the dashboard can’t grow with the business, it gets ignored. Sustainability comes from adaptability and shared ownership.
What challenges have you faced when implementing predictive models in highly regulated sectors?
Regulation introduces both constraint and clarity. In defence and finance, I had to document model lineage, justify algorithmic decisions, and ensure outputs were interpretable for auditors. I prioritise ethical modelling, no black boxes. At PwC, I worked within GDPR compliance to build donor retention models that were both effective and privacy-conscious. The challenge becomes a strength-regulated environment that forces you to create cleaner, more transparent, and ultimately more trustworthy systems.
How do you engage stakeholders unfamiliar with data science to embrace predictive tools?
I translate data into meaning. Instead of saying, ‘The model has 90 per cent accuracy’, I say, ‘This forecast helped save ₦500 million last quarter’. I also focus on storytelling using visuals and business language that makes sense to leaders. At WIMBIZ, I trained entrepreneurs by walking through their sales trends, not teaching Python, but teaching purpose. The result? Increased engagement and faster adoption. People don’t buy models; they buy outcomes.
How do you balance technical rigour with ease of use in BI tools?
I always build dual-layer interfaces with technical backends for analysts and intuitive frontends for leadership. Using Power BI’s bookmarks and tooltips, I create dynamic dashboards that don’t overwhelm users but allow them to explore if they want to. The CFO may only want red-flag summaries. The analyst may wish to do full drill-downs. Both are right. Art is designing a tool that satisfies both sides without friction.
How have your models impacted decision-making timelines or organisational agility?
Drastically. At MOD UK, I reduced the time-to-decision on logistics threats from 72 hours to 12 minutes. That enabled preemptive corrections rather than costly firefights. At Deloitte, my project forecasting tools let engagement leads reallocate resources before delays spiralled. Predictive BI doesn’t just accelerate decisions—it improves their quality. Organisations become proactive, not reactive. And in today’s volatile world, that speed-to-insight edge is a competitive advantage.
What’s your process for testing and validating predictive models before deploying them?
I perform cross-validation, track RMSE or F1-scores, and simulate deployment with synthetic data. But equally important, I test assumptions with users. At Deloitte, we ran parallel simulations of my predictive risk model against real project outcomes for two weeks before full deployment. We discovered anomalies early. Technical soundness is vital, and so is operational alignment. I don’t deploy unless I know the model works in the real world and the boardroom.
What role does compliance play in your predictive analytics frameworks?
Compliance is foundational. At MOD, I integrated ISO 27001 and ITIL standards into Power BI dashboards, ensuring data governance aligned with audit requirements. For financial clients, I embedded row-level security in Snowflake to enforce GDPR. Proactive measures like automated compliance triggers in SharePoint prevented £12 million in fines. I also conduct quarterly reviews with legal teams to align models with evolving regulations. Compliance builds trust and ensures longevity.
What advice would you give organisations starting their predictive analytics journey?
Start small, but think big. Choose a high-impact use case like procurement risk and focus on delivering measurable value. Clean your data early and keep governance at the centre. Most important, don’t wait for perfection. Build, learn, refine. Predictive analytics is not a one-time rollout; it’s a learning curve. Equip your teams, celebrate small wins, and align every model to your organisation’s strategy.
What’s your vision for the future of predictive analytics in enterprise operations?
Predictive analytics will embed itself into every operational workflow, from strategic boardrooms to frontline decision points. Tools will be more autonomous and explainable, and the focus will shift to ethical, inclusive modelling. I envision farmers, SMEs, and local governments using predictive tools built in their context in Africa. Globally, predictive intelligence will drive not just decisions but equity. That’s the future I’m building toward. Courtesy, ThisDay.
