A model for assessing the coherence of data-centric capabilities in Defence
Understanding the shift towards data-centric warfare
Organisations of all types are encountering technological disruption on a scale which has never been as rapid or sustained. Armed forces globally are no exception and the relentless development of technological and cyber capability, processing speed and more recent advances in artificial intelligence (AI) and machine learning (ML), assure that disruption to established military frameworks and concepts will continue. Operational evolution must occur in parallel and for this to happen, military leaders need to continually upgrade their understanding of the operational capabilities and possibilities that technology facilitates.
Advances in technology and connectivity mean that organisations now have access to more data than ever before. This data affords possibilities that simply did not exist a few years ago. Superior data collection, analysis and dissemination is essential for defence. It creates the opportunity to run simulations and predict outcomes of multiple variables and decisions in order to dictate tempo in the battlespace and optimise logistics.
Data Centric Warfare (DCW)[1] is a relatively new term which has evolved from the US concept of Network Centric Warfare (NCW). It is now 25 years since NCW emerged and was refined by the UK as Network Enabled Capability (NEC)[2]. The exponential increase in data generation, collation, and speed of analysis since then creates the foundation for DCW. Where NCW and NEC focused on the connectivity of the fighting force, DCW puts data and the use of data at the heart of battlespace planning and operation.
The complexity of this topic should not be understated. The collation and dissemination of data in the battlespace brings its own challenges and risks but the risk of not developing DCW capability is undoubtedly greater. Data centricity informs everything, from better procurement and effective mobility to pace, adaptability and agility in the battlespace during offensive and defensive operations. In the contemporary operating environment data centricity offers a tangible advantage.
Below, we explore the concept of DCW with the intention of providing leaders in Defence with a common understanding of its importance and a framework within which to begin to understand the subject more deeply.
Unravelling the complexities
The term DCW has emerged to encompass the opportunities offered to an information enabled force. However, the term is ambiguous and understood by different people to mean different things. To address this we have sought parallels in industries where greater progress has been made in unifying and exploiting data for competitive advantage. In so doing we aim to explore characteristics within a framework which enables military leadership to undertake informed discussion and debate, which will ultimately foster a decision-making advantage.
It is important to start with the difference between being data driven and being data centric.
To be data driven means to make strategic decisions based upon available data. It is without question better to be data driven than not. It is an important step towards achieving data centricity. Being data driven sees organisations develop cultures where data is vitally important but where the practice of data management and science may be immature. Large volumes of data might be absorbed but they can be analysed inconsistently and may not be capable of dynamic, real-time analysis and interrogation. Such an organisation will use the data it possesses to inform its strategy and execution but may move too slowly and make ill-informed decisions too frequently.
Data centricity on the other hand puts data at the heart of organisational design and decision-making. It results in an organisation that builds its data structures, decision making and operational execution on common language, optimised for information exploitation. The culture works towards the eradication of data ownership and data silos, to the creation of a centralised asset which transforms operational effectiveness and speed of execution. All of this is held together by agreed governance of processes and data that facilitate successful strategy and tactical actions.
Characteristics of data centricity
To fully understand how data centricity requires an organisation to change, we can consider the underlying characteristics of organisations that have successfully made the transition. Whilst all organisations are different and the outcomes sought after differ further still, these characteristics are found in all data centric organisations. These characteristics compound and multiply when applied together.
Strategy and governance – Data ceases to be something which is “used” and becomes an asset of integral importance and value. Strategic thought and planning as to how it will be used is central to preparation and governs its capture, storage, analysis, and presentation.
People – There is a strong commitment to the development of people and their understanding of how to collect and manage data effectively. The opinions of data scientists, data analysts and data engineers are highly valued. People with expertise in these fields are “in the room” for strategic discussion and tactical decision making.
Stability and adaptability – Infrastructure is resilient, robust, and secure. Storage and transmission of data is taken seriously and underpins strategy and governance. The infrastructure can handle massive amounts of storage, real time processing and model creation whilst also allowing rapid analysis.
Agility and innovation – Change is recognised as constant and the only way to respond to it is to continually learn and demonstrate agility as operational conditions evolve. Whilst strategy and governance provide the guidelines to navigate by and parameters to operate within, continual learning must be derived from situational realities. Continual learning is taken from each new campaign or data deployment and feedback loops ensure that knowledge and insight are shared to enable continuous improvement in future execution.
What we can learn
When we consider the earlier explanation of data centricity alongside the characteristics of data centric organisations, we can begin to understand the complexity of DCW. To assist decision-makers in fully considering the span and scope of opportunity and approach offered by DCW adoption, we have developed a maturity framework summarised in Figure 1 below. This framework aids understanding, development, and improvement of the key components of DCW, allowing an enterprise-wide assessment of the completeness and coherence of projects and solutions under development.
Figure 1: DCW Maturity Framework
It allows a maturity rating against each dimension to be determined so that capabilities are developed across a holistic set of requirements and objectives.
Conclusion
Data centricity is essential for contemporary Defence operations. However, it is neither quickly nor simply attained. It requires deliberate, iterative, and relentless progress towards the goal.
The current level of sophistication of each element of the DCW Maturity Framework provides the basis for understanding the coherence of any DCW endeavour. It enables an assessment of the amount of work and the priority that should be afforded to each step in the move to data centricity. Organisations that have successfully become data centric would all agree however, that the foundation and pillars of leadership, culture and governance must be in place to assure strength and stability.
Implementation of the elements presented in this paper would result in Defence demonstrating best practice in data centricity. No force will make this journey in a single leap. This is the work of considered and intentional action alongside experienced and thoughtful partners, wholly aligned to the strategic aims of UK Defence.
