Real-Time Decisions in Volatile Settings

Published on: Mon Jun 10 2024 by Ivar Strand

From Guesswork to Guidance: Using Data for Real-Time Decisions in Volatile Settings**

1. The Challenge of Decision-Making in Volatile Environments

Project implementation in fragile and conflict-affected states is characterized by high levels of uncertainty. Managers are tasked with delivering results and maintaining fiduciary responsibility in environments subject to rapid, often exogenous, shocks. A fundamental challenge in these settings is the information vacuum. Decision-makers must frequently commit resources and personnel based on data that is incomplete, outdated, or of questionable reliability.

The typical reporting cycle—often monthly or quarterly—is misaligned with the operational tempo on the ground. A security situation can deteriorate, a supply route can close, or a key political actor can shift allegiance in a matter of days. A project manager who waits for the formal monthly report to learn of such a development is not managing events, but reacting to them. This lag between event and information consistently leads to inefficient resource allocation, increased risk to personnel and assets, and a diminished probability of achieving project objectives.

2. Adaptive Management: A Structured Approach

The solution to this challenge is not to seek perfect information, which is an impossibility, but to implement a system of adaptive management. This is about creating a disciplined, structured process for making the best possible decisions with the data that can be made available in near real-time.

At its core, adaptive management is a simple feedback loop:

• Plan: Define a clear objective and a theory of how to achieve it.
• Act: Implement the planned activities.
• Measure: Continuously collect relevant data on progress and the operating context.
• Adapt: Use this data to learn and make deliberate adjustments to the plan.

This iterative process moves an organization from a rigid, linear implementation model to one that is dynamic and resilient. It replaces guesswork with evidence-based guidance.

3. Key Principles for Effective Real-Time Data Systems

At Abyrint, we have found that successful real-time monitoring systems are not built on complex technology, but on a set of pragmatic principles. Technology is an enabler, but the management process is what determines success.

1. Define ‘Good Enough’ Data. The pursuit of comprehensive, verified data is often the enemy of timely decision-making. The key is to identify the minimum viable information needed to make a sound judgement. The focus should be on data that is directionally correct and available quickly, rather than perfect data that arrives too late to be of use.
2. Focus on Leading, Not Lagging, Indicators. Lagging indicators, such as quarterly output figures, describe the past. Leading indicators, by contrast, help anticipate the future. For a logistics project, a leading indicator is not the number of packages delivered last month (lagging), but the percentage of trucks that departed on schedule this week (leading). A dip in this metric can signal emerging security or maintenance problems that require immediate attention.
3. Integrate Quantitative and Qualitative Inputs. Numbers alone are insufficient to understand a complex environment. A dashboard might show that attendance at a training program has dropped by 50%, but it cannot explain why. This requires structured qualitative input from field staff or community partners who can provide the necessary context—perhaps a new local conflict has made travel unsafe, or a rumour has eroded community trust. The combination of quantitative alerts and qualitative context is essential for sound judgement.

4. A Practical Application: Monitoring Aid Distribution

Consider a project tasked with distributing food aid to several dozen camps for internally displaced persons (IDPs). The environment is insecure and access is intermittent.

• The Conventional Approach: The project relies on paper-based delivery receipts collected from camp managers at the end of each month. By the time the central office reconciles these reports, they might discover that three camps received no food for weeks, but the information is too old to enable an effective response.
• The Adaptive Management Approach: The project implements a simple system where each camp manager sends a single SMS message confirming receipt of goods. This data populates a central dashboard in near real-time. This system could be visualized in a simple chart.

(Placeholder) Exhibit A: Real-Time Delivery Status Dashboard A simple bar chart showing deliveries scheduled vs. deliveries confirmed for the current week, updated daily.

When the dashboard shows a delivery to Camp C is unconfirmed 24 hours after its scheduled arrival, the project manager can act immediately. They can contact the driver, consult field security staff about the route, and make an informed decision to either re-route another delivery or postpone until the situation is clarified. This is a targeted, low-cost intervention that directly mitigates risk and improves performance.

5. Moving Towards Deliberate Adaptation

Operating effectively in volatile settings demands a move away from rigid, long-term plans and towards a culture of deliberate adaptation. Building systems for real-time data collection and analysis is not primarily a technological challenge; it is a management discipline.

By focusing on ‘good enough’ data, tracking leading indicators, and integrating quantitative and qualitative insights, project managers can shift from reactive guesswork to proactive guidance. This approach does not eliminate the inherent risks of working in difficult environments, but it provides a structured framework for navigating them, substantially increasing the likelihood of delivering on objectives and meeting fiduciary responsibilities.