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The Department of Defense (DoD) spends $350 billion a year tending to the readiness of its military forces for current and future operations. These costs include recruiting, retaining, educating and training personnel; maintaining and repairing equipment; and provisioning supplies to support operations. Despite these substantial investments, there is widespread agreement that the U.S. military today is at a degraded state of readiness, as ongoing conflicts in Iraq and Afghanistan as well as years of budget uncertainty and funding caps have taken their toll.
As the global security landscape grows more and more dangerous, it is imperative that DoD build and maintain readiness. Fundamental to that effort is the ability to accurately assess current levels of readiness across the DoD’s many facets, and determine how to project whether investments today will impact future readiness. Currently, commanders often do not have confidence in their readiness reporting structures. To become more adept at assessing and predicting readiness, leaders will have to address concerns pertaining to data, systems and models.
Due to the amount of data available, and the time in which it takes to effectively analyze all of it, commanders are often using data that is incomplete, erroneous, redundant, inconsistent, out of date or lacking context. When data misrepresents the reality of a situation, it puts lives, resources and national security at risk. Because of this, commanders may doubt the data they are presented with.
The data that the DoD uses to determine readiness is extracted from a wide array of dissimilar yet interconnected systems. The methods and standards used to collect and analyze data differ from unit to unit, making it difficult to aggregate and homogenize data across an entire organization. These systems also have little or no predictive capabilities, making it difficult for leaders to analyze future readiness.
The readiness models used today are largely manual processes that determine if a unit can deploy, but often are not up to the task of predicting outcomes. To create that capability, leaders have to build custom workarounds unique to their team. Due to the custom nature of these workarounds, they are difficult if not impossible to bring together and aggregate in order to create an enterprise-wide view.
Generally, efforts are focused on reporting and analyzing a unit’s current level of readiness, as opposed to optimizing and predicting readiness for the future. To address this, experts have called for a more dynamic approach to building and assessing the many factors that contribute to a unit’s level of readiness by refining existing models and relationships between inputs and outputs. Doing so, however, requires more complex readiness models and an agility that many offices simply don’t have.
Reimagining readiness approaches to be more predictive requires a totally new strategy that addresses how organizations analyze data, how they collect and aggregate it, and how they can redistribute resources to better meet new objectives.
Readiness systems today lack the ability to objectively assess separate resources and their associated costs, often making no distinction between different types of resources and how they contribute to overall readiness. These systems treat similar resources as equal, regardless of their impact on a mission. This lack of context leads to readiness reports that are not trusted and thus amended or tossed aside in favor of more subjective, non-standardized assessments.
The individual cost of these different resources is also a critical factor when determining budgets and shaping future strategies. Organizations have this data available, but it is not integrated into their readiness systems. In order to reflect a unit’s true readiness status, organizations need to utilize a system that can take a specific resource’s function and cost into context.
Evaluating readiness is not enough — organizations must also be equipped to optimize readiness. Accomplishing this will require a data platform that can digest, analyze and store different types of data in massive quantities. This way, data can be stored in its raw form until it is needed, and be cataloged and tagged for streamlined searchability. Booz Allen Hamilton proposes utilizing a platform that is built on open source software with open standards that can function as a data storage repository.
By working in an open data platform, organizations can set their readiness systems up for better insights, increased security, more effective tracking and extensibility. The ability to ingest and catalog more unstructured data can enable organizations to better find relationships between disparate data sets, determine exactly where specific data points came from and apply security needed down to individual data cell levels. Additionally, “plug and play” capabilities help tools integrate with existing components and systems, and modular designs are more apt to expand and evolve as needs shift and technology upgrades.
Though exact circumstances will vary from organization to organization, transforming and transferring an organization’s readiness capabilities is typically a complex process. This process used to take a considerable amount of time and resources, but modern technology and practices have cut down on those variables significantly.
There are several steps to ensure that the new readiness platform will work effectively for the whole enterprise. Experts within the organization and are familiar with it's structure, challenges and mission are key figures. With this background, these experts can help design and guide the transformation process, anticipate organizational needs and ensure that the new readiness tools will be a good fit for the full enterprise. Together, they can use their institutional knowledge to enact a four-phase process from conception to execution.
First, the development team builds a variety of custom applications that organizational leaders can experiment with in a test platform to best determine how the system will work and exactly what tools they will need. From there, a host environment is created based on the specific security and sharing requirements discovered during the experimental phase. Within this hosting environment, a platform that can host, process and tag different data sets is then built. Finally, advanced analytics are enabled, empowering organizations to quickly and easily find patterns between disparate data sets that provide more information to make smarter data-driven decisions.
With decades of experience and a deep understanding of military missions and data analytics, Booz Allen’s skills and expertise are uniquely positioned to assist government and military organizations reimagine their readiness assessment processes and develop new, more robust readiness capabilities. Booz Allen knows not only how to best collect and analyze data, but the value that data can provide to a mission.