Dedicated, integrated Operational Resilience (OR) contingencies have become critical to operational integrity as businesses grapple with persistent economic uncertainty and rising compliance requirements.

While governance processes, stakeholder training, and tooling strategies are common OR measures, resultant best practices are often siloed within business functions and grow ineffective as enterprises scale in size and complexity.

Tailored Management Information (MI) capabilities provide a single platform for continuous oversight and optimization of standardized resilience practices. MI models enable all metrics to be captured and presented in a single location, driving decision-making for the entire OR function.


How MI models work

MI models supply the insights decision-makers need to formulate OR and Disaster Recovery Plan (DRP) contingencies, conduct risk analyses of Business As Usual (BAU) resilience, evaluate compliance, and forecast operational impacts.

MI model-enhanced OR capabilities include:

Process and system vision

Strategic change management and forecasting

Threat and risk detection

Contingency execution and remediation speed

By centralizing cross-functional OR capabilities, well-constructed models can provide the data needed for educated remediation of operational issues as they occur.

But what makes an effective MI model, and what do you need to build your own? Here we examine the 3 fundamental competencies of MI models.


Vision of existing OR processes

The primary function of the MI model is to provide real-time monitoring of enterprise risks. By mapping key risks to their associated Important Business Services (IBS), an MI model can identify gaps in contingency plans and recovery capabilities.

Effective MI models should include the following data:

The state of the IBS ecosystem. Models should clarify how many services there are, the risks associated with each, and their related internal processes (e.g. infrastructure, platforms, databases, users, security architectures, and communications channels).

IBS prioritization for resource allocations. Models should highlight critical services for resource allocations – and present the data metrics defined to determine the priority list.

The scope of IBS external dependencies, including third-party software, applications, and shared platforms.

Existing Business Continuity Plan (BCP) capabilities, revealing obsolete processes for retirement and optimization opportunities. BCP data should be as granular as possible to avoid the retention of inefficient processes.


Accessible data and communications channels

Clear vision of key risks and where they affect IBSs is not enough. OR teams, business functions, and other stakeholders should have consistent access to the model to facilitate feedback and translate insights to operational action. 3 key stakeholder groups require MI platform access for data insights to be effective:

Senior leadership. The leadership team must have a clear view of ongoing resilience levels across business functions to align OR capabilities with strategic vision. Historical and contextual data, resilience level trends over time, and operational forecasts are critical auxiliary insights.

Business function heads and internal stakeholders. MI models facilitate open communication and coordination between functions by enabling stakeholder input and access insights relating to their area of focus.

The dedicated OR team manage model analytics and insights. Stakeholder feedback channels enable continuous monitoring, adjustment, and optimization of data metrics, processes, insights, and model performance.

Remember the representation of every business function is crucial for MI model effectiveness. A single misalignment can disrupt disaster remediation speed, causing a domino effect as dependent functions stall and cohesion collapses.


Rigorous data quality control

MI models are only as effective as the data that underpins their insights. Without thorough and continuous evaluation of data quality, capture, and triage processes, any resultant findings are unreliable and unlikely to produce actionable insights.

OR teams should focus on achieving 4 data qualities when constructing their MI model data architectures:

Data completeness. For the model to be effective it needs to encompass data inputs from all areas of the business. Missing IBSs or incomplete DRPs can throw the viability of the entire model into question.

Data topicality. MI model insights need to accurately reflect the organization at the moment of data capture. Continuous monitoring of the underlying model, scheduled updates, and data refresh cycles can ensure data is kept topical.

Data consistency. Variations in data format or refresh cadence can lead to incompatibilities within the model’s data analytics – decoupling specific data sets from the model and rendering any insights worthless.

Data accuracy. Captured data must be subjected to rigorous, comprehensive quality control. False data can severely damage OR effectiveness as contingencies built on inaccuracies may not perform during an actual crisis.


The data elements required for MI model success will differ from enterprise to enterprise, and effective models must be built iteratively as capabilities are tailored to unique enterprise needs. Expert advisory is recommended for best results.

Contact a Wavestone expert for bespoke advisory on optimizing your Operational Resilience capabilities.



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