In the military services, proper allocation of resources is vital to mission success.
Constraints on budget and operating reserves mean decision makers must be able to calculate readiness—the precise state at which they can perform the functions for which they are organized—in order to meet mission objectives, but current decision-support systems make this challenging. They rely primarily on descriptive data, the raw measurements of readiness information from commanders’ reports, training, equipment usage maintenance cycles, supply chains and other related factors. This data is vast, but it does not provide the deep level of insight or precision needed to address today’s demanding readiness challenges.
And adding more types of data into the mix will not solve anything—sufficient data is already available. Instead, decision makers need to be able to understand complex inter-relationships of readiness variables so they can ultimately determine how a given action will impact the readiness of a unit in the field as well as readiness of the force beyond that individual unit.
Data scientists and domain experts at Booz Allen Hamilton have come together to form strategies that can help military organizations move from a purely descriptive model to a diagnostic model—a tactic for ingesting data and reaching actionable, insightful conclusions that will help leaders translate a variety of inputs into an accurate assessment of a unit’s readiness.
With diagnostic analytics, leaders can effectively see how a small ripple will affect an entire ocean. By building cause and effect into analytics models, they can position themselves for mission success.
To build a useful diagnostic readiness model, start by identifying the questions that must be answered about readiness. Domain experts, who have in-depth and field-based knowledge of the relationships among readiness data, can work with data scientists, who understand the computational elements of analytics, to codify and incorporate commanders’ unique knowledge into preexisting readiness models.
Together, they can glean meaning from data already in the military’s hands.
Diagnostic models will help military organizations achieve greater readiness insight at the unit level and scale up from there. In time, leaders will be able to leverage unit assessments to develop broader, interaction-based models for military entities at large. Doing so will pave the way for the next step: predictive analysis. Once they master the art and science of analyzing the present, readiness teams will begin to develop analytic models that can make logical predictions for the future.
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