The strategy has the following characteristics: the overall situation. Long-term. Programmatic. Objectivity. Competitive. Risk. Strategy focuses on the future, but the future is full of uncertainties, which inevitably leads to certain risks in strategic solutions.
Strategic features
Enterprise strategy is to set long-term goals and achieve the trajectory of the overall and guiding planning, belongs to the macro management category, with guidance, overall, long-term, competitiveness, systemic, risk six main characteristics.
Instructive
The enterprise strategy defines the business direction and long-term goals of the enterprise, clarifies the business policy and action guide of the enterprise, and plans the development trajectory and guiding measures and countermeasures to achieve the goals, which plays a guiding role in the operation and management activities of the enterprise.
Global
Based on the future, the company's strategy is based on the in-depth analysis of the international and national political, economic, cultural and industrial business environments, combined with its own resources, standing at the height of system management, and making a comprehensive plan for the long-term development trajectory of the enterprise.
Long-term
"Today's efforts are for tomorrow's harvest", "People have no foresight, there must be near-term worries". Taking into account short-term interests, the corporate strategy focuses on the thinking of long-term survival and long-term development, establishes the characteristics of the long-term goal enterprise business strategy, and plans the development trajectory and macro management measures and countermeasures to achieve the long-term goal. Secondly, around the long-term goal, the enterprise strategy must go through a continuous, long-term struggle process, in addition to the necessary adjustments according to market changes, the development of the strategy usually can not be changed overnight, with long-term stability.
Competitive
Competition is an unavoidable reality of the market economy, and it is precisely because of competition that the dominant position of "strategy" in operation and management has been established. In the face of competition, enterprise strategy needs to analyze the internal and external environment, clarify its own resource advantages, and form characteristic management through the design of an appropriate business model, enhance the confrontation and combat effectiveness of the enterprise, and promote the long-term and healthy development of the enterprise.
systematicness
Based on long-term development, the corporate strategy establishes the long-term goal, and needs to set up the stage goal and the business strategy for the realization of each stage goal around the long-term goal, so as to form an interlocking strategic goal system. At the same time, according to the organizational relationship, the enterprise strategy needs to be composed of three levels: the strategy of the decision-making level, the strategy of the public institution and the strategy of the functional department. The decision-making strategy is the overall guiding strategy of the enterprise, which determines the strategic elements such as the business policy, investment scale, business direction and long-term goals, and is the core of the strategy. The enterprise strategy explained in this book mainly belongs to the strategy of the decision-making level; the strategy of the public institution is the long-term planning of the independent accounting business unit or the relatively independent business unit of the enterprise in accordance with the strategic guiding ideology of the decision-making level, through the analysis of the competitive environment, focusing on the market and products, and carrying out the long-term planning of its own survival and development trajectory; Resource allocation and other strategies support the overall planning of the guarantee system, such as: planning department strategy, procurement department strategy, etc.
Risk
Any decision made by an enterprise has the characteristics of a risky business strategy, and strategic decisions are no exception. In-depth market research, accurate prediction of industry development trends, objective vision and objectives, proper allocation of human, financial, material and other resources at each strategic stage, scientific selection of strategic forms, and the formulation of strategies can guide the healthy and rapid development of enterprises. On the contrary, if you only judge the market subjectively, set goals that are too ideal or predict the development trend of the industry, the strategy formulated will lead to misleading management and even bring the risk of bankruptcy to the enterprise.
The U.S. Department of Defense released the first Data Strategy, which is another guiding strategic document in the field of IT (informatization) modernization after the U.S. Department of Defense Cloud Strategy, Defense Artificial Intelligence Strategy and Defense Digital Modernization Strategy released by the U.S. Department of Defense in 2019. At a critical period when the U.S. military is accelerating its transformation from "network-centric warfare" to "data center warfare," the introduction of this strategy is of great guiding significance.
Background analysis of the launch of the Data Strategy
1. The development needs of the further deepening of the U.S. military's digital modernization strategy
On July 12, 2019, the U.S. military released the Department of Defense Digital Modernization Strategy, which is the top-level guiding document for a series of other strategic documents in the field of IT modernization of the U.S. Department of Defense. The strategy clearly sets out a vision for the next four years (2019~2023) for the digital modernization of the Department of Defense: to create "a more secure, coordinated, seamless, transparent and cost-effective IT architecture that transforms data into actionable information and ensures reliable mission execution in the face of ongoing cyber threats." In the Department of Defense Digital Modernization Strategy, it is clear that "data is a strategic asset" and that it is important to "invest in and maintain the infrastructure needed to make DoD data visible, accessible, understandable, trusted, and actionable" to "enhance the data collection and processing, display processes, and inform decision-making at the senior level." To this end, the Ministry of Defense must optimize existing data centers, develop technologies and systems for data fusion and decision support, as well as systems with big data processing capabilities, so that they can operate autonomously and propose solutions to assist decision-making.
It can be considered that as early as a year ago, the U.S. Department of Defense put the status of data at the level of the cornerstone of digital transformation, and the release of the Department of Defense's "Data Strategy" is a key component of the Department of Defense's digital modernization plan.
2. The practical need for the Department of Defense's "network-centric" network security architecture to accelerate the transformation
At present, the U.S. military's "network-centric" network security architecture is facing various difficulties, and for this reason, the U.S. military has accelerated the transformation of the "network-centric" to "data-centric" network security model in recent years, which will affect every field of cyberspace. The US Department of Defense's interpretation of this is that the original mode of IT construction of the Department of Defense will be transformed, that is, from the network construction required by network-centric warfare to the data construction for the purpose of data use and sharing. In the context of network security construction, it refers to the transformation from network boundary protection to data security protection. Of course, this data security protection does not only refer to data security in a narrow sense (such as data leakage prevention technology), but also a data and resource-centric security protection concept. To this end, the U.S. Department of Defense has embarked on a journey of cybersecurity architecture transformation, from the Single Security Architecture (SSA) of the Joint Information Environment (JIE) to the Secure Cloud Computing Architecture (SCCA) to the current Zero Trust Architecture (ZTA), the Department of Defense's "data-centric" cybersecurity architecture has been evolving and developing.
It is precisely in this realistic need that the Data Strategy is oriented to the transformation of the "data-centric" cybersecurity architecture, eliminating the traditional "network-centric" security model of the Department of Defense, focusing on how to protect the data and critical resources of the Department of Defense as the primary task, and then focusing on the network of the Department of Defense, so as to accelerate this major change of the Department of Defense.
3. Driven by the application of emerging technologies represented by artificial intelligence in the military field
In recent years, with the continuous emergence of new technologies such as artificial intelligence, cloud computing, big data analysis, and mobile Internet, their development and integration have provided the basis for large-scale acquisition and utilization of data, and their application in the military field has become more and more extensive. One of the core priorities of the U.S. Department of Defense for the 21st century battlefield is to be free from the shortage of sensors, including soldier wearables, vehicles, drones, cameras, spectrum, signal and radio sensors, network sensors, and many other sensors that make up the battlefield Internet of Things. And more sensors means more data, and too much data limits the Department of Defense's ability to turn information into operational intelligence in a timely manner. Artificial intelligence is becoming increasingly important to the proliferation of tactical edges, the use of data from a wide variety of sensors, and the emerging applications that rely on this data.
The U.S. military first clarified the development strategy of artificial intelligence through the "Department of Defense Artificial Intelligence Strategy" (released in February 2019), and then tackled a number of challenges restricting the "understanding" of sensor data through many innovative research projects of artificial intelligence, and then promoted the application of artificial intelligence in key fields, such as the "expert" of the U.S. Department of Defense The program has leveraged the company's artificial intelligence systems to analyze U.S. drone video data to detect and mark targets and then relay them to analysts, to automate cybersecurity operations and respond to threats in real time, to better understand the meaning of signals obtained in the battlefield electromagnetic environment, to filter noise and classify signals to reduce the "cognitive burden" on signal detection by soldiers, and to enable real-time collection through joint all-domain command and control (JADC2). Analyzing and sharing data, as well as leveraging AI technology to ensure that the right data reaches the right forces to provide effective situational awareness and decision support, and more. With breakthroughs in the application of AI in these key areas, there is a solid technical foundation for achieving the data goals set out in the Department of Defense's Data Strategy.
Key takeaways from the Data Strategy
1. Come up with a vision
The strategy states that the Department of Defense is a data-centric agency that uses data at rapid scale to gain operational advantage and increase efficiency. The strategy emphasizes the need to work closely with warfighters. Initial areas of focus include: (1) Joint All-Domain Operations: Leveraging Data to the Advantage on the Battlefield, (2) Senior Leadership Decision Support: Using Data to Improve Defense Department Management, and (3) Business Analytics: Using Data to Drive Informed Decision-Making at All Levels.
2. Eight guiding principles have been clarified
The strategy states that the Department of Defense uses the following guiding principles to influence the objectives and essential capabilities of the data strategy. These guidelines are the basis for all data work of the Ministry of Defense:
(1) Data is a strategic asset: DoD data is a high value-added commodity that must be used in ways that deliver immediate and lasting military advantage.
(2) Collective data management: The Department of Defense must assign data stewards, data custodians, and functional data stewards to achieve accountability throughout the data lifecycle.
(3) Data ethics: The Department of Defense must put ethics at the forefront of all thoughts and actions related to how data is collected, used, and stored.
(4) Data collection: The Department of Defense must enable electronic data collection at the time of creation and maintain the source of the data at all times.
(5) Enterprise-wide data accessibility and availability: DoD data must be made available to all authorized individuals and non-individual entities through appropriate mechanisms.
(6) AI training data: Datasets for AI training and algorithmic models will increasingly become the most valuable digital asset for the Department of Defense, and a framework must be created to manage assets throughout the data lifecycle.
(7) Data applicability: The Department of Defense must consider the ethical aspects of data collection, sharing, use, rapid data integration, and minimizing any unexpected sources of bias.
(8) Design for compliance: The Department of Defense must implement IT solutions that provide opportunities for full automation of the information management lifecycle, proper protection of data, and end-to-end records management.
3. The four basic competencies that need to be possessed are planned
The strategy states that the following four essential capabilities are required to achieve the Department of Defense's data goals. These competencies are not specific to a single goal, and are required to achieve all of them:
(1) Architecture: DoD architectures supported by enterprise clouds and other technologies must allow data to be transformed faster than adversaries can adapt.
(2) Standards: The Department of Defense has adopted a set of standards that include not only accepted methods for managing and utilizing data assets, but also proven and successful methods for representing and sharing data.
(3) Governance: DoD data governance provides the principles, policies, processes, frameworks, tools, metrics, and oversight needed to effectively manage data at all levels, from creation to processing.
(4) Talent and culture: DoD staff will be increasingly able to handle working with data, making data-based decisions, developing evidence-based policies, and implementing effective processes.
4. Refine seven major data objectives
The strategy breaks down the data goals that the Department of Defense aims to achieve into the following seven areas:
(1) Make data visible: Users can find the data they need.
(2) Make the data accessible: Users can retrieve the data.
(3) Make the data easy to understand: Users can identify the content, context, and applicability.
(4) Data linkability: Users can leverage data elements through inherent relationships.
(5) Make the data credible: Users trust the data to make decisions.
(6) Data interoperability: Users have a common representation/understanding of data.
(7) Ensure data security: Protect data from unauthorized use/operation.
Key takeaways from the Data Strategy
1. Emphasize the realization of military integration through data fusion
One of the major visions of the "Data Strategy" is to use data advantages on the battlefield to achieve joint all-domain operations in the future. Integrated joint operations are the main form of modern operations, but the efficient integration of the actions of various services is constrained by the influence of factors such as the organizational structure of the armed forces, and the barriers between the services cannot be dismantled, which seriously restricts the effectiveness of joint operations. The Data Strategy states that the proper exchange of data between systems and maintaining semantic understanding is critical to successful decision-making and joint military operations. The Data Strategy emphasizes the achievement of data interoperability goals, including the Department of Defense's development and implementation of data exchange specifications for all systems, including allied systems, the Department of Defense's harmonization of different data standards and formats, the promulgation of data labeling strategies, and subsequent execution calculations, to achieve semantic and syntactic interoperability through common data formats and machine-to-machine communications, thereby accelerating the development of advanced algorithms and providing a strategic advantage for the Department of Defense to achieve joint operations through data fusion.
2. Attach great importance to the security of data
Among the seven big data goals proposed in the Data Strategy, "data security" occupies the largest part of them. Compared with the "Army Data Strategy" promulgated by the U.S. Army in February 2016, "ensuring data security" is one of the new goals, which reflects the higher requirements of the U.S. military for data. The strategy states that protecting the Department of Defense's data at rest, in motion, and in use is the lowest barrier to future combat and weapons system entry. Using disciplined data protection methods, such as attribute-based access control, across the enterprise, enables the Department of Defense to maximize the use of data while employing the strictest security standards to protect U.S. citizens. In the future, the U.S. Department of Defense will implement fine-grained rights management (identity, attributes, permissions, etc.) to regulate the access, use, and disposal of data; data stewards will regularly evaluate classification standards and test compliance to prevent data aggregation from causing security issues; the Department of Defense will implement approved standards for security identification, processing restrictions, and records management to identify and implement classification and control identification; develop and enforce content and record-keeping rules; and the Department of Defense will implement data loss prevention technologies to prevent accidental release and disclosure of data. Only authorized users can access and share data, access and processing restrictions metadata is tied to data in an immutable way, comprehensive audits of data access, use, and disposition, and more. Through this series of specific measures, the data security of the Ministry of Defense will be strongly guaranteed in the future.
3. Emphasize the standardized processing capability of the whole life cycle of data
Data collection and identification, representation, analysis, utilization, exchange, sharing, management, etc. constitute its whole life cycle, and the "Data Strategy" particularly emphasizes the standardization ability of data in the above stages, which is one of the four basic capabilities planned in the "Data Strategy". In the future, the U.S. Department of Defense will implement a series of standards throughout the data life cycle, including the implementation of metadata standards, including the location and access methods of shared data to ensure data visibility, the implementation of standard application programming interfaces (APIs) to make data accessible, in particular, to achieve data comprehensibility, the Department of Defense will present data in a semantic-preserving way and express it in a standardized manner throughout the Department of Defense, use a common data syntax for the same data types, and include semantic metadata in data assets。 In addition, the Department of Defense will implement globally unique identifiers so that data can be easily discovered, linked, retrieved and referenced, and utilize common metadata standards to enable data to be consolidated and integrated to ensure data linkability; Through the above standards, the U.S. Department of Defense promotes the realization of data goals such as visualization, accessibility, trust, comprehensibility, interoperability, linkability, and security of core data functions, so as to form an efficient information sharing battlefield environment.
A few takeaways
1. Fully understand the strategic position of data assets and actively respond to the arrival of data center warfare
As the concept of decision-centered warfare matures, the data that drives decision-making will inevitably become a strategic asset and a key weapon in the field of national defense. Military data, as a basic strategic resource, plays an increasingly prominent role in army building and operational application. We should fully realize the huge value and application potential of "data", learn from the experience of the US military in military data planning and construction, and plan a data-centric national defense organizational structure by clarifying military data requirements, construction contents, standard requirements, and establishing supporting organizational structures, management systems, and coordination mechanisms, improve the level of data governance, use data to obtain strategic opportunities and tactical advantages, and improve our military's combat and winning capabilities in the era of great power competition.
At present, the U.S. military is in a critical period of transformation from "network-centric warfare" to "data center warfare", and the "Data Strategy" will inevitably promote its accelerated transformation. Driven by artificial intelligence and big data technology, the main content of the enterprise strategy, the US military's data center warfare will realize the acquisition, processing, utilization, and sharing of massive data, break down the barriers between networks, achieve efficient combat force coordination, greatly strengthen combat capabilities, accelerate combat processes, and ultimately greatly improve combat efficiency. For us, this is a challenge that must be faced. But on the other hand, we also have development opportunities, and the mainland is basically in sync with the developed countries of the West in this round of industrial transformation represented by artificial intelligence and big data. Our military has great potential to develop new data-based combat capabilities, and it should respond to the arrival of data center warfare with a positive attitude.
2. Unify the top-level design and comprehensively build a standardized data set
At present, the U.S. military has different standards for data management, labeling, and cross-agency access, which hinders AI and machine computing users from accessing the data they need.
At present, our military's data formats are different, the sources are different, and there is a lack of unified data processing procedures, resulting in a great waste of data resources. To this end, our military should formulate relevant data strategic objectives, carry out standard research on data acquisition, data processing, data exchange, data services, data security, etc., build a set of military data standards with excellent structure and complete functions, issue corresponding military standards to guide the development and construction of relevant data functions, and unify the top-level design. Drawing on the experience of the U.S. military, we will formulate and improve core standards in key areas such as data quality management, cloud computing management, and big data management, and formulate relevant general standards for metadata, data security, cloud computing data, and other functions to standardize their top-level design.
3. Vigorously promote scientific and technological research, and explore the innovative realization of data security
When it comes to military combat data, "security" is the foundation of a data strategy. Information, whether public, sensitive or confidential, must be handled in accordance with laws, regulations and policies to ensure the security of data and information. The means to achieve the strategic goal of data security will be constantly updated with the changes in the form of warfare. At present, the U.S. military is exploring advanced data security implementation technologies such as digital twin technology, which can realize a verifiable, privacy-preserving synthetic twin that matches the real world, and accelerates data utilization and analysis under the premise of protecting data security. Its representative project is that of the U.S. Air Force, capable of creating statistically equivalent data points (but not any potentially classified information) that preserves the primary attributes of the data point without sacrificing anything that might be relevant to a particular time or place of collection. When a military or civilian institution wants to share data with potential research partners, but does not want to share actual data due to information security considerations, they can share digital twins instead for research, development and modeling; blockchain technology, the U.S. military is testing the waters through the blockchain database project to achieve operational data confidentiality and non-tampering, to ensure secure communication and data integrity; to achieve non-denial of combat operation data, to solve complex, multi-party, After-the-fact issues such as joint military operations, tracking data sources and usage to ensure that data is verifiable and auditable, implementing built-in native data security capabilities, and enabling users to meet military data security requirements through encryption, signature, verification, timestamping, and data on-chain, etc., so that users cannot close or bypass during use, etc.
In this regard, we should learn from the practice of the US military, explore the main content of the enterprise strategy on the advanced implementation technology of data security, and accelerate the implementation of the strategic goal of data security by taking the road of military-civilian integration, and guide, plan, guide and support the construction of innovative technology research projects of the future data security strategy.