A Simple Model for Management

 And why do we continue getting management wrong

Wikipedia defines management as a ”process of managing the resources of businesses, governments, and other organisations.” [Wikipedia] But management can also be understood as "the transformation of resources into utility" [Fredmund Malik] or as "to forecast and to plan, to organise, to command, to co-ordinate and to control" [Henri Fayol] or as ”the coordination and administration of tasks to achieve a goal.” Quite generic and somewhat controversial definitions. The article aims to describe management through a single model that includes most aspects of resources, transformation, tasks, utilities, and goals.

Creating a Simple Model

The simple model merges the above into one system view, as illustrated in Figure 1, composed of:

• Inputs are the resources or efforts, like personnel, time, money, material, information, and immaterial components.
• Actions are tasks that the organisation is doing to produce outputs.
• Outputs are the products, services, artefacts, information, or performances that the organisation produces, delivers, or creates using inputs and actions.
• Outcomes are the impacts, effects, or value that the outputs have on targeted systems of systems like businesses, consumer market, audience, valuation, or the theatre for conflict.
• Feedback is the information management collects from outcomes, outputs, actions and inputs in an attempt to steer the process through foreseeing, planning, coordinating, and appraising. This may be called data-driven decision-making  or a learning organisation.  

Figure 1: A Simple Model for Management

Using the Simple Model to Analyse Management Mistakes

The simple Management Model helps analyse, at a high level, the existing management operation models and guides possible remediation in Table 1.

Table 1: Samples for analysing and remediation of current management using  the Simple Model for Management

Component	Typical Misconduct	Possible Remediation
INPUTS	Management focuses on efforts and resources, such as hours/money/ material consumed, or manning /machines/contracts activated. This may lead to management by cost centres.	Follow resource consumption relative to outputs, and focus on core resources, such as competencies[1] (Pareto: 20% of inputs deliver 80% of outputs[2]).
ACTIONS	Management focuses on transactions, utilisation of service points, and performance of subsections. This may lead to suboptimisation and a stovepiped operation model.	Measure throughput and quality of the entire value stream rather than subcomponents, and then focus on eliminating ”muda” or wastefulness in LEAN process thinking.[3]
OUTPUTS	Management focuses mainly on manufactured products, delivered utilities, or what can be quantified, while neglecting everything else[4]. This may lead to burning resources or missing the strategic purpose.	Outputs are not linear causes of outcomes, so a Balanced Scorecard-type approach needs to identify the relationships among inputs, actions, outputs, and outcomes.
OUTCOMES	Management focuses on ends or goals but measures them using key performance indicators without an overarching context, such as benchmarking, market share, share valuation, and new user acquisition. This may lead to Machiavelli’s view[5] that the ends justify any means, like in Russian operational thinking.[6]	Effects or impacts are essential to identify and measure to keep the system/business on track towards its objectives.  Management may use a results-based approach to make progress toward the destination statement (Ends).[7] Military planning or design uses Lines of Operation and Courses of Action to deliver impact on Centres of Gravity.[8]
FEEDBACK	Management may focus on collecting feedback or data from a few components and on either lagging or leading indicators. This may lead to a stagnant organisation/system that lacks agility or flexibility, which will be replaced by competitors or destroyed by adversaries.	Management must create a balanced mix of leading and lagging indicators to improve reaction and prediction.[9] Leading Indicators are early signals that predict future trends (e.g., employee engagement predicting retention rates). Lagging Indicators are confirming past performance but don’t allow for proactive adjustments (e.g., revenue after a quarter ends).

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[1] https://en.wikipedia.org/wiki/Core_competency

[2] https://en.wikipedia.org/wiki/Pareto_principle

[3] https://en.wikipedia.org/wiki/Muda

[4] https://en.wikipedia.org/wiki/McNamara_fallacy

[5] https://simple.wikipedia.org/wiki/The_end_justifies_the_means

[6] https://www.wilsoncenter.org/blog-post/ends-justifies-means-logic-led-russia-war-and-repression

[7] https://en.wikipedia.org/wiki/Balanced_scorecard

[8] https://www.coemed.org/files/stanags/01_AJP/AJP-5_EDA_V2_E_2526.pdf

[9] https://metricsexplained.com/resource/balancing-leading-and-lagging-indicators-a-strategic-approach

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Improving the Model

When the model in Figure 1 is compared against a sample of other management models, one may find that:

• The model aligns with Boyd’s OODA loop in decision-making while including several feedback loops to learn and adjust the system. Nevertheless, it does not illustrate the cognitive processes of sense-making and decision-making.  
• The model facilitates Deming’s PDCA circle for iterative improvement, but does not illustrate the management functions of planning and checking.
• The model aligns with parts of the ISO37000 governance framework by reflecting the organisational purpose, oversight, strategy, and interfaces with beneficiaries and sources. 
• Porter’s five forces may be applied to the model in a specific environment or market. 
• One can also understand Hamel’s and Prahalad’s value chain for core competencies (inputs), products (outputs), and business (outcomes), but the model does not explain the utilisation of core competencies, particularly.
• The model misses some of the stakeholders and the strategic dynamics of Kaplan’s & Norton’s 3rd generation Balanced Scorecard. 
• The model lacks the functionally layered structure of Beer’s Viable System Model , but it may fulfil the viability definition by responding to environmental changes and achieving its purpose.
• The model fails to support Hamel’s Humanocracy viewpoint because the action component lacks the details of structure, bureaucracy, and human relationships. Therefore, it fails to illustrate the evolutionary tension between hierarchies and networks of competency. 
• Furthermore, the model in Figure 1 does not describe the environment or the theatre/market where the outcome takes place, as defined in EFQM  and studied in Introduction to Environmental Systems and Processes. 

While trying not to increase the complexity of the model, but address some of its essential gaps, the improvement presented in Figure 2 adds:

1. relationship between the system and its environment for adapting to environmental changes, 
2. sources for the inputs residing outside of the system for supply chain management,
3. relationships between actioning elements to recognise human relationships and lines of communication, and 
4. elaboration of the target systems (e.g., BtoB, BtoC, audience, theatre of war) of outcomes with the Cynefin Framework of four domains based on situational awareness.

Figure 2: Somewhat improved management model

With the above improvements, the model extends towards:

• Collecting data from the system’s environment supports capturing mega and microtrends and improving foresight.
• Understanding the short and long-term chances in sources for inputs improves predictions concerning supply chain changes, educational levels, labour market fluctuations, etc.
• A simple relationship graph of the organisational structure and a connection graph of the unofficial relationships help to know yourself, as Sun Tzu teaches.
• Understanding that not all markets, societies, and theatres of war are similar when it comes to making an impact. Collecting data points from a simple target area (i.e., known) requires sensing, categorising, and sense-making, which may use best practices. A more complicated target area (i.e., knowable) requires more effort in analysis (see Orientation in OODA-loop) before decision-making. In complex and chaotic situations (e.g., digital transformation, battlefield, or unknown unknowns), the target system needs probing or action to elicit a reaction that can be detected, identified, and analysed.

Improving the Agility of the Current Defence Industry and Forces Value Stream in Acquiring and Generating Capabilities

Is the current Defence Industry and Defence Forces Relationship agile enough to address the volatile arms–counterarms evolution emerging in the Russian-Ukrainian war? 

Is the current Defence supply chain capable of delivering continuous integration of a software-defined military system of systems? Will the Defence Industry meet the European Defence Forces' expectations for 4th industrial cyber-physical products and services? Is Europe coherent enough to engage the Russian 2/3rd industrial force in an attrition war with inevitable human casualties? Questions that military strategists are pondering nowadays.

Figure 1: A high-level illustration of the value stream for legacy defence capabilities life cycle

With its war budget and legislation, Russia is building up its second industrial generation capabilities to produce armoured platforms, artillery, missiles, and ammunition, in addition to sourcing them from China and North Korea. Meanwhile, they are learning to use dual-use cyber-physical products sourced from China and Iran, such as Unmanned Aerial Systems, to deliver precision attacks and maintain 24/7 surveillance over the battlefield.  

Meanwhile, Ukraine relies heavily on conventional armaments, which are primarily supplied by NATO countries, albeit sporadically and subject to political constraints. While NATO countries are struggling to rebuild their Second Industrial generation manufacturing capabilities, Ukraine is building its 4th industrial capability  to provide dual-use cyber-physical platforms for both sensing and effect.  Partially, because the Western legacy weapon systems do not survive on the Ukrainian battlefield. 

Where does the European Defence Industry migrate from its current 3rd industrial capability to manufacture expensive platforms and precision missiles? How are the European Defence Forces utilising their strengths differently for transformation under the Russian hybrid operations? Will the European industrial and military value stream transform through:

1. Improving gradually the current processes and the operation model,
2. Accelerating towards 4th industrial software-defined armament utilisation, or
3. Fast-lining to acquire mass-produced, dual-use, cyber-physical platforms and adjust/configure/integrate them for military use.

First, the paper analyses the common bottlenecks along the current life cycle of armament from innovation to force utilisation. Secondly, the paper proposes three different lines of operation to improve agility, accelerate the life cycle updates and configuration, or connect the defence value chain more efficiently to meet the evident Russian threat.

Legacy Armament Life Cycle Model

The contemporary European ecosystem between the Defence Industry and Defence Forces is optimised for 2/3 generation industrial armament manufacturing and utilisation, optimising the long lifespan of platforms (main battle tanks, fighters, frigates) and complying with legislation for commercial procurement with a flavour for national security interests. The management of platform-centric life cycles suffers from three bottlenecks, though the value stream from innovation to battlefield and creating a strategic advantage in National Defence:

1. Valley of Death lies between ideation and experimentation, and manufacturers' intentions to create a viable product. Ideas, demonstrations, and proof of concepts often struggle to transition to pre-production and secure investments, ultimately becoming workable products with potential markets and profits. 
2. Valley of Death resides between vendors marketing/sales and the Defence Forces procurement. Commercial or armament-specific procurement regulation defines the behaviour between vendors and procurers in the market.  Requirements-based acquisition may inflate expectations beyond what any product in the market can deliver.  While minimising the ambiguity, both the military and industry tend to produce generations of similar fighting platforms. 
3. Valley of Death resides between the Defence Forces' force generation and force utilisation. Whilst integration and training may be successful, the platform appears not to be feasible in the battlefield or type of operation, or an element does not meet the requirements of the entire system of systems. For example, maintaining the Leopard 2 main battle tanks in the Ukrainian theatre .

Figure 2: Model for legacy value stream for military capabilities generation

The linear value stream requires both strategic support and a feedback loop to maintain the track towards integration and sustainment of armament. 

• Strategic direction is required through the life cycle of innovation, particularly in mitigating the bottlenecks in the chain of Ideating, Acquisition, and Utilisation. Whether this support and guidance is provided through governance, market regulation, or a hybrid manner is a question. Often, the ministerial strategic guidance is perceived as contradicting the legislation of the open market.

• A feedback loop is required to translate the lessons captured in operation, training and manufacturing to mid-life updates of platforms. Successful communication via the loop is based on trust, transparency, confidentiality, a shared knowledge base, and measurement for impact. As usual, communication fails to have a lasting effect on adjusting products and processes to meet military demand.

The legacy life-cycle value stream may work with platforms that have a lifespan of over 30 years and are loosely integrated, with operations mostly manual. The legacy model is not sufficient when the Russian defence industry has already gained a few years' advantage over the European defence industry.  The following sections study three ways to improve the agility of the legacy acquisition and life cycle management process.

 

Ways to Improve the Agility of Contemporary Acquisition Processes and Operation Model

When choosing to evolve the current 3rd industrial acquisition value stream gradually, both Defence Forces and Industry may enhance the performance and agility of the value stream in the following ways:

Strategic guidance

• National Defence Science and Technology strategies that guide resources, potential technology focuses and research awards
• Government-driven strategic direction through innovation incubators, governance of military industries, and 5-year military investment plans
• Market-driven direction with long-term military acquisition lists and capability requirements, calling manufacturers and products to Defence Exhibitions for information sharing
• Examples: US National Defense Science & Technology Strategy 2023 

Creating and maturing ideas:

• Seeding the ideation and R&D with incubators or innovation hubs, 
• Bringing potential competencies together in hackathons or competitions, 
• Incubating and maturing potential ideas towards Proof of Concepts (PoC)
• Expressing a long-term commitment to the most viable PoCs.
• Examples: US DARPA , NATO Science and Technology Organisation, NATO innovation accelerator (DIANA)  and Multinational Experience (MNE) , UAE Innovation Incubators, FIN eAlliance , FIN DEFINE 

Acquisition:

• Capability portfolio management to coordinate the development of new capabilities and decommissioning the legacy while meeting the evolving capabilities of potential adversaries with a 30-year horizon.
• Guiding the Defence Industry to invest in new technologies and manufacturing methods in preparation for new products with strategic partnerships
• Target Enterprise Architecture to guide the integration and system of systems performance
• Create Defence Industry clusters or partnerships to eliminate parallel product lines, increase specialisation, and the ability to integrate system of military systems. 
• Examples: UAE IDEX , KSA World Defense Show , KSA GAMI/SAMI ,  Nordic Patria-Nammo-Kongsberg partnership 

Utilisation:

• Multi-geared Force planning to develop, integrate, train and deploy troops at a pace and quality that addresses the operational requirements and crisis escalation
• Blue and Red Force exercises to find vulnerabilities for mitigation.
• Force sustainment to maintain, repair and restore troops and capabilities in operation
• Examples: NATO Combined Endeavour 

Feedback loop

• Annual cooperation and lessons identified sessions between the military and industry
• Having key account managers visiting exercises
• Manufacturers' user groups
• Examples: US Project Convergence, NATO Multi-national Experience, Systematic user group for Sitaware Battle management system development, US Space Command integration of operations and R&D 

The above improvement enhances the legacy process but does not meet the contemporary requirements of the theatre. The next section studies a software-defined value stream for military defence capabilities.

 

Generating Software-Defined or Driven Military Capabilities

The software-defined capabilities have been evolving for the past 15-20 years in civilian systems and are gradually being adopted in military-grade platforms and systems.  Software-defined radios (SDR), antennas (SDA) ,  networks (SDN) and virtual computers/infrastructure (SDI)  are widely used in military C5ISTAR systems. Later fourth-generation fighters  are fly-by-wire controlled and equipped with fire-and-forget missiles . Air Defence systems have been computer-controlled and are currently receiving over-the-air software updates while in mission.  The US DoD has a concept for a military Internet of Things composed of autonomous systems and a combination of weapon systems networked together.  

Software-defined, virtualised, or cognitive  features are primarily coded in programs, and changing the program also changes the effect or features of the armament. This opens two opportunities for agile or adaptable military systems: 

1. Algorithm development and continuous integration (CI) of new software and configurations can respond more quickly to battlefield changes than contemporary mechanically defined platforms. After a software update, a MIMO phased-array surveillance  radar may operate on a different frequency band and modify its beamforming and RF features to avoid being identified as a military radar.
2. A variety of sensors and effectors can be connected to a software-defined network, which enables faster target acquisition and combined fires against the target. A cognitive network with edge processing capacity can accelerate the BLUE OODA-loop, making it quicker than RED, which will ultimately gain victory, at least in a long game. 

The acquisition and generation of software-defined military capabilities need a different value-creating chain than the legacy armament. Figure 3 illustrates the separation of software (SW) and hardware (HW) supply chains with specialised features for:

• Sourcing from open code or algorithm pools and using public development environments to engage smaller and more specialised developers.
• Using agile methods to create software-defined features in products. Typically, the development windows (sprints) vary from a few weeks to some months. Hence, the span from idea to implementation is remarkably shorter than in a legacy value stream.
• Continuous integration (CI) ensures essential coherence and quality before the feature is introduced in force generation.
• Shorter feedback loops from integration, generation and utilisation to collect lessons and improve/correct features in the following iterations.
• More standard, mass-produced hardware that is operated by software that makes the difference in sensitivity, range, manoeuvrability, or effect at the tactical level.
• The governance of the value stream should be based on strategic partnerships for software development and integration, which uses as much as possible open-source code. Naturally, the military hardware still needs conventional procurement from the market.

Figure 3: A view of the software-defined capabilities value stream

The software-defined military capability requires long-term software development partnerships or a remarkable investment in a military in-house software development cadre, while actively using the value produced in an open-source society.  Furthermore, the hardware (platforms, weapons, sensors) needs to be digitised, more standard, and support the virtualisation of features. Software portability from one hardware platform to another, or integration with open application programming interfaces (APIs), becomes a significant threshold for the cost efficiency of the value stream. Naturally, the current manufacturers of bespoke platforms with closed licenses are opposing the model. 

There are several ongoing initiatives in the Armed Forces to improve their capability and transfer the value stream, for example:

• US DoD runs Project Convergence  to experiment with artificial intelligence and autonomous systems, enhance network cognition, and build defence capabilities for their cyber and electromagnetic space.
• The Land Command of Finland has been developing their Model 18 C5ISTAR system since 2010 with software-defined features and bi-annual development cycles. 
• US DoD has ordered a “comprehensive transformation”  of the US Army, utilising emerging technologies, integrating separate organisations to develop new capabilities, and transitioning to agile funding to build or acquire emerging opportunities. 

Consumer market, dual-use, military-specific, cyber-physical product/elements acquisition and integration

A more flexible acquisition model that would recover faster from battlefield surprise would be to utilise multiple sources (Government of the Shelf, Military of the Shelf, Commercial of the Shelf, In-house developed, and Strategic partnerships) to experiment, develop/manufacture, integrate, and generate. The model introduces a dual-use product line that sources from global consumer markets, integrates feasible parts into the military system of systems, and trains troops before rolling out capabilities to the theatre, as illustrated in Figure 4:

• Armed Forces pushes their experimentation closer to ideation by hosting hackathons, competitions or challenges. The winning concepts, prototypes or models will be awarded a development contract and hosted either in the software, defence, or civilian industrial chain.
• Continuous integration extends to include dual-use products that have shorter lifespans but can be acquired in vast quantities from the global supply chains. 

Figure 4: A view of the multi-sourced capabilities value stream

The multi-sourced model can be adjusted to meet the special requirements of each theatre if the force generation is also specialised. The adaptive military capability acquisition and generation model should address the current requirements on the Ukrainian battlefield  while also embracing the 4th Industrial Revolution, where manufacturing is brought to the theatre, as permitted by the threat environment. 

Instead of aiming for full operational capabilities with lengthened storage life, this model produces minimum viable products  that may mature through the integration and generation phases, ultimately achieving sufficient maturity for the battlefield. Naturally, the digital twin of the military system of systems  helps test how new elements integrate into the defence entirety, identify potential vulnerabilities, and determine the consequences of failure. 

Digital Transformation of Military Affairs

Trying to understand the playground of digital transformation in military affairs

Over the past 25 years, consulting companies have sold military Digital Transformation as a fast track to gain dominance on the battlefield, achieve cost efficiency to meet budget cuts, or a way to annihilate the vast masses of 2nd industrial forces in current near-peer conflicts.

Nevertheless, there is more than one definition of Digital Transformation, so the military needs to recognise what it wants to achieve.

The military is evolving through five waves of emerging digital technologies. (Kale, 2020) 

1. Digitisation transferred information and content from analogue to digital format and improved military administration and office work. In the Finnish Defence Forces, this evolution started from information assets during the 1980s , and it is still ongoing, related to products and soldiers.  Some Armed Forces are still using paper-based decision-making due to tradition or the power structure.

2. Digitalization introduced enterprise-wide systems, like Enterprise Resource Planning (ERP)  or Maintenance, Repair, and Overhaul (MRO), which enabled human, financial, material, platform life cycle, and facilities management to gain cost-efficiency. Moreover, C3I support systems and battle-space management systems were deployed for improved situational awareness. The Finnish Defence Forces underwent significant evolution during this phase, primarily between 1999 and 2007. Some Armed Forces are still transitioning from functionally specified resource management systems to enterprise platforms. Meanwhile, several functionally specific management systems remain available on the market.

3. The first-generation digital transformation has enabled revolutions in military affairs, such as Network-Centric Warfare in the US Department of Defense and network-enabled Capability in the UK Ministry of Defence.  The Finnish Defence Forces underwent changes in their operational and management approaches in 2008 and 2015.  Some other Armed Forces are struggling to transfer their organisational culture to adopt these enabling processes because military organisations are built to resist change.

4. The second-generation digital transformation can be defined as software-defined everything. In this phase, mechanical systems are migrating to cyber-physical entities, which integrate with other entities, forming structures such as a combat cloud. The US DoD is experimenting with a tactical system of systems in the Project Convergence . The European Combat Air System  (FCAS) project combines manned and unmanned platforms to form a system of systems. Furthermore, the software-driven approach also transfers the engineering and manufacturing processes of the new platforms, as seen in the Rheinmetall Modular Open Systems Approach. 

5. The third-generation digital transformation may be defined as robotic or agent automation, digital twins and autonomous effectors/sensors. The US Golden Dome  is one example of the type of capability that the military may gain from integrating sensors, AI-enabled decision-making, and shooters across all domains against masses of air- and space-borne targets. Both Ukraine and Russia are utilising remotely operated commercial and military drones for enhanced tactical mobility and effectiveness. 

On the other side of the coin, digital transformations often fail to deliver expected outcomes because (Mattila, 2020):

• Canadian Armed Forces spent from 1980 to 2000 moving from Cold War capabilities to meet post-9/11 threats. Transformation took longer, mainly because personnel lacked training to certify them in new ways of defence and behaviour.
• Swedish Försvaret created a concept for their Nätverkbaserad Försvaret in the late 90s and early 2000s, as best practice for other militaries, but failed because the Swedish Government decided to cut the defence budget and downsize the entire national defence to a peacekeeping force.
• NATO aimed for Network Enabled Capabilities through the 2000s, but found itself without a shared network until the establishment of NATO Federated Mission Network 2015.
• The US DoD launched in 2000 its Network Centric program, which improved division and higher echelon situational awareness but failed to deliver it to the tactical level. The gap appeared costly in later Iraq and Afghanistan operations.
• Finnish Puolustusvoimat made a significant reorganisation in 2008 and 2015 for cost-cutting, but remained in service stovepipes while missing the essential ability for joint operations. The digitalisation of enabling and C4ISTAR processes met opposition at the cultural level.

When a military transformation fails to provide the intended security capabilities, the transformation command wastes unique resources. The failure may also open an opportunity for an adversary to gain a strategic advantage and a temptation to exploit it. The severity of the potential inability necessitates the use of more advanced tools to comprehend and model the transformation. 

A holistic understanding becomes increasingly vital as some emerging technologies, such as unmanned autonomous systems, machine learning, nanotechnology, and human enhancement, may provide the adversary with a surprising strategic advantage. In this situation, a military enterprise needs to have the flexibility to adjust and recover from a surprise through rapid transformation.

Data “Strategy” in a Military Enterprise

What are the ingredients of a Military Data Strategy? Is there a Data Strategy? How does the approach to the digital economy reflect in military data strategy? 

In its current strategic plans, the military aspires to be a data-driven organisation, data-centric, data-dominant over potential adversaries, or data-use as a force multiplier. Those words echo current trends, but how are they defined in the roads the military must journey to gain more value from its data?

This article creates a definition for data “strategy” for military affairs and studies five data strategies from Europe and the US to see how their approaches differ from data maturity, enterprise architecture, strategic approach, and contextual viewpoints.

Figure 1: Essential components in the military strategic approach to data

1. So-called ¨Data Strategies¨ with the Military

In the military, strategy is perceived as a theory of victory. It explains how to use force to achieve political objectives in war.  A typical military strategy comprises Ends, Ways and Means following the Lykke model, defining “Strategy equals Ends (objectives toward which one strives) plus Ways (courses of action) plus Means (instruments by which some end can be achieved).“  

Data is recognised within an Enterprise Architecture as one domain or approach separate from or within the information domain. The TOGAF data domain resides between business and applications. Moreover, data architecture aims to translate business needs into data and system requirements and manage data and its flow through the enterprise.  Digitisation has increased data value, and digital transformations, if successful, have gained strategic advantages in business performance.  With digitalisation, military affairs value data, especially in multi-sensor surveillance, intelligence analysis, predictive maintenance, resource optimisation, force generation, and decision-making. The amount of collected data, big data, has been perceived as a game-changer or force multiplier.  

Combining data and strategy to create a data strategy provides a focused view of military affairs' achieving strategic Ends using data as a Means or data-related tools as Ways to enable or multiply capabilities. Hence, data strategy is more of an implementation plan to achieve goals in the Defence Strategy. While the global digital economy recognises three different approaches:  Market-driven (US), rights-driven (EU), and state-driven (CHI). Because of access issues, sampling for data strategies is done only from the US, Australia, and Europe. 

Therefore, the data strategic principles in military affairs are defined as:

• Recognised threat and technology development environment that may provide the adversary an advantage or an emerging vulnerability in their data structure.
• Ends defined in military affairs (processes, business) terms
• Data-related Ways: Analytics, data science, data fusion
• Data as Means: Big data, metadata models, semantic models, integration and sharing
• Military threat environment requires more efficient data-related confidentiality(C), integrity(I) and availability(A) methods to ensure the enabling or multiplying outcomes
• Since data is a component in a military socio-technical system, the transfer of culture is usually the most challenging hurdle to overcome on the transformation roadmap
• Multi-sourcing dimension to ensure that military affairs are integrated with society, the defence industry, and other governmental agencies and coalition partners.
• Compliance with the digital economy principles that the surrounding society has adopted: market-driven or rights-driven.
• Finally, data lives in a technical environment, platforms and networks. Do they enable the new valuation of data?

 

2. How do current data strategies reflect the principles?

Next, the paper uses the strategic data principles to compare contemporary military approaches to gain more value from data within their enterprise and as part of a wider data environment.

Table 1: Comparing a variety of defence data strategies against the principles of military data strategy

Data strategic principles	US DoD[1] 2020	UK MoD[2] 2021	SWEDEF[3] 2021	BUNDESWEHR[4][5] 2021	FINDEF[6] 2021
Recognised threat environment and confrontation	DoD has lacked the enterprise data management to ensure that trusted, critical data is widely available to or accessible by mission commanders, warfighters, decision-makers, and mission partners in a real-time, useable, secure, and linked manner.	With increasing data, it is harder to isolate insights from information	NTR	Recognises the cyber and information space	Disinformation and other data threats, emerging technology
Ends defined in military affairs	Warfighters at all echelons require tested, secure, seamless access to data across networks, supporting infrastructure, and weapon systems out to the tactical edge.	Data is an enduring strategic asset that, when effectively exploited, drives battlespace advantages and business efficiency. Integrated Operating Concept.	Interoperable processes. More effective, simple, safer, and asymmetric capabilities.	Harmonisation and automation of military processes, resilience of defence, value for money	Information dominance, user-driven
Data-related Ways	Data is Visible, Accessible, Understandable, Linked, Trustworthy, Interoperable, Secure.	Exercise sovereignty, standardise data, exploit data, secure data, curate data, endure data	Data asset management, data exchange with other agencies, data modelling, Chief Data Officer and Data Protection Officer, Data science	Data clusters, short innovation cycles, mobile access, and user experience-driven	Improve data accessibility, findability, usability, and establish a new information culture. Knowledge management
Data as Means	Architecture, Standards, Governance, Talent & Culture	Central data leadership, People skill and culture, Governance & controls, data foundations, exploitation	Information architecture, data annotation, master data management, open data, data clustering, Common Information Model, Information Supply Chain, Antifragile data architecture	Establish the inspector for the data and information space	Manage strategic assets, data fusion, and enrichment
Cultural challenges	Service Members, Civilians, and Contractors at every echelon make data-informed decisions and create evidence-based policies.	NTR	Information as a strategic resource, from application-focused to data-driven, the need for more data, Access to high-quality data, and Data trust.	Innovation-driven approach	Sharing culture, right to use data, and data consumership.
CIA methods	Consumers know that data is protected from unauthorized use and manipulation.	Secure by design	Data quality	Improve cyber defence capabilities.	Identifying critical information, risk management, and ensuring availability
Multi-sourcing	Achieve semantic as well as syntactic interoperability	Defence data ecosystem	National data sources, International open sources.	Transorganisational clusters	Global data environment, data exchange management
Compliance with the digital economy	Data management and compliance with policies are a top priority	Data complies with legal, regulatory and ethical obligations.	SWE, NATO and EU compliance	NTR	Open government
Enabling Technology	Sensors and platforms across all domains must be designed, procured, and exercised with open data standards as a key requirement.	NTR	A cohesive command chain with secure, robust, accessible, and user-friendly IT systems.	Consolidation and integration of data platforms	NTR

In summary, the data strategies in a variety of Defence Forces:

• Do not necessarily approach data from the threat environment or confrontation (2/5), but from the increasing amount of data and fewer abilities for sense-making (2/5).
• Aim to achieve the ends defined in military affairs (5/5), at least at the principal level.
• Data-related Ways are defined per the existing data management boundaries and maturity. Some emphasise ways to access data and process efficiency (3/5). Others see data as an accelerator for capability development (2/5).
• Some see data-related Means from the management approach (3/5) and others from the value creation viewpoint (2/5).
• Cultural challenges are approached from the consumer viewpoint (3/5). Moreover, innovation for novel data applications is seen as a cultural accelerator in one strategy.
• All strategies have a view to data Confidentiality, Integrity or Availability
• All strategies identify data as a Multi-sourced asset.
• All strategies recognise that with a global approach to data assets, compliance with the digital economy is a relevant issue.
• All strategies identify the Enabling and emerging Technologies but do not necessarily refer to them in the data strategy paper.

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[1] US DoD Office of Prepublication (2020): Executive Summary: DoD Data Strategy

Unleashing Data to Advance the National Defense Strategy

[2] UK MoD (2021): Data strategy for defence

[3] FOI-R--5112--SE (2021): Förstudie om informationshantering i Försvarsmakten – med

fokus på information som strategisk resurs

[4] WD 2 - 3000 - 063/21 (22. September 2021)

[5] Strategische Leitlinie Digitalisierung

[6] https://julkaisut.valtioneuvosto.fi/bitstream/handle/10024/163329/PLM_2021_3.pdf?sequence=1