Model Based Systems Engineering (MBSE) and the Intelligence Community

Model Based Systems Engineering (MBSE) is not a new approach. it is an approach that is part of a long-term trend toward model-centric methodologies adopted by other engineering disciplines and is becoming more popular and useful in the Department of Defense and the Intelligence Community.

Model Based Systems Engineering (MBSE) Defined

Model-based Systems Engineering (MBSE) is the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing through-out development and later life cycle phases.” (INCOSE, 2007).

Why Model Based Systems Engineering (MBSE)?

• Rapid response to changing mission requirements.
• Identify missing concepts or objects or.
• Identify emergent capability from data (planned or surprise).
• Support additional analytical capability (e.g. fault analysis).
• Identify problems earlier.
• Reduce costs and risks.
• Better communication and feedback with user groups.
• Integrated, distributed system quality can be challenging to manage without an integrated model.

Model Based Systems Engineering (MBSE) Implications for the Intelligence Community

The Intelligence Community is the collection of raw data and the analysis and interpretation of that data into actionable intelligence. Most of the agencies in the IC rely on collection systems that are designed to support analysists in their production of actionable intelligence for the government and the war-fighter.

The design, development and implementation of these systems requires the application of systematic engineering techniques to produce quality mission results. Until now however systems engineering relied on documents and hard copy artifacts.

There are two problems facing the community in achieving this goal however:

The first is data overload. With more collection sensors, new technology and access to open source data agencies are required to collect and process greater and greater amounts of raw data for analytical use. By 2021 the data generated by global web traffic will reach 3.2ZB/year upfrom1.2ZB in 2016. The pace at which data are generated is increasing and the mission of the Intelligence community is falling behind.

This results in analyst overload and decreasing marginal returns and puts the United States behind its competitors. The solution is either add more analysts (not a workable solution because of the shortage of language qualified analysts) or design more powerful and effective systems that are scale able to meet ever larger data streams. Theses systems must be constantly re-engineered according to proper engineering standards, but engineering models can improve accuracy and reduce costs and delays.

The second problem is the rapidly changing mission that results from the dynamic world mission environment. As the threat changes rapidly so does the mission and the users of actionable intelligence in the field require a constant flow of new requirements from existing systems.

Real time intelligence requirements require new apps in real time that must be developed rapidly and operate within existing systems. Model Based Systems Engineering allows systems engineers to run “what if” scenarios, avoid time consuming errors and rapidly respond to user requirements that are consistent with the baselines of existing systems.

Comparisons to the Document Centered Systems Engineering Approach.

With MBSE Models provide a force-multiplier for engineering work. Essentially models can be quickly developed reflecting changing requirements and can involve different user groups and developer. “What if” scenarios can be introduced without affecting the original baseline until the final configuration is complete.

Model Based Systems Engineering (MBSE) in the Intelligence Community is not a cure-all: engineering expertise is still required.

At present MBSE is not the complete solution to solving complex engineering problems for several reasons:

• It is difficult to model non-functional requirements.
• The model can be a barrier to understanding for some stakeholders.
• Effective MBSE requires a disciplined and well-trained project team and a mature process approach. (Source: Incose )

MBSE approaches and tools must be integrated with existing processes and it is dangerous to adopt new process wholesale. This can be problematical unless the existing culture is open to change. Leveraging MBSE also requires investment in tools, training, and infrastructure. In addition, MBSE should be introduced incrementally by starting with a prototype project to streamline processes,

Model Based Systems Engineering in the Intelligence Community(MBSE): Experts Needed and Certifications

No matter what level of Model Based Systems Engineering is used, training and educational credits (including certifications) are required for both government employees and contractors.

However, the government has difficulty recruiting qualified MBSE engineers, particularly those with higher level clearances The alternative is the use of contractors.

Contractors can recruit faster, cross clearances quicker, and are more agile and can offer advanced training to meet changing technology advancements to qualified staff.

About Integrated Intelligence (IIS)

IIS provides solutions that improve the proficiency and quality of actionable intelligence collection production and sharing. Leveraging deep expertise in integrated intelligence concepts, agile development techniques, and shared platform concepts; both private and public institutions have trusted IIS to create infrastructures that are both effective and cost efficient. We continue to evolve to meet the future technology needs of the IC including machine learning, artificial intelligence, big data, Model Based Systems Engineering (MBSE) and anticipatory intelligence.

IIS can do this by bringing completely cleared and highly qualified STEM talent to provide integrated intelligence project solutions for the Intelligence Community.