Note: This series of blogs replaces the previous series on MBSE.
This series of blogs is an outgrowth from discussions concerning the integration of the Model-Based Systems Engineering (MBSE) Initiative into RWG activities that occurred within the RWG sessions during INCOSE IW 2016 and IW2017 in Torrance, CA and subsequent communications between the author, members of the RWG, and members of other INCOSE Working Groups.
This series of blogs is written from the perspective that requirements, along with all work products (models, designs, documents, diagrams, drawings, etc.) generated during the performance of System Engineering (SE) lifecycle process activities are represented by underlying data and information that must be linked together and integrated into a common, integrated dataset. Using this perspective, the common, integrated dataset can be viewed as the foundation of Systems Engineering.
From this data-centric perspective of SE, there are many key benefits that will aid organizations in successfully meeting the challenges associated with today’s ever increasingly complex systems, meeting the intent of the MBSE Initiative, and helping organizations move toward INCOSE’s Vision 2025.
The basic premise of this blog series is that Systems Engineering (SE) is based on models, models are represented by data and information and other Systems Engineering (SE) work products are either projections of the same data and information or represented by data and information generated from other SE lifecycle process activities. To effectively manage ever increasing complex systems of the future, this underlying data and information must be integrated into a common, project dataset. This dataset not only represents an integrated architectural model of the system under development, but also a model of the SE lifecycle process activities and resulting work products that can be used to more effectively manage the system development efforts across all SE lifecycle stages.
In discussing “data”, it is important to understand the relationship between data, information, knowledge, and wisdom.),
* Data: individual facts/bits/datum without context, by themselves they have little value
* Information: data with context allowing us to expand on the data and gain information, insight, and knowledge
* Knowledge: aggregation of information, helps apply the information allowing us to define context, patterns, correlations, causations, inform standards, etc.
* Wisdom: knowledge plus experience
The SE tools used to generate and manage the various SE work products and underlying data and information provide context. This context results in information. This information represents an information model of the system being developed as well as provides valuable rationale and insights developed while executing the SE lifecycle processes involved in engineering the system. In practicing SE, the systems engineer’s emphasis needs to be on the data and information shared across lifecycle processes rather than on the individual lifecycle process activities themselves. Combining the systems engineer’s experience and knowledge with the information contained in the integrated dataset enables the systems engineer to use their wisdom to successfully deliver winning products – products that deliver what is needed, within cost and schedule, with the desired quality. Accepting this premise, it is useful to view SE from a data-centric perspective.
The practice of SE is often viewed from many perspectives. Similar to the old story of the blind men and the elephant, SE cannot be effectively practiced when viewed from just one perspective (requirements, models, patterns, standards, industry specific application, etc.). To successfully practice SE, wise systems engineers recognize and use each perspective as appropriate to the activity they are performing. The perspective of this document addresses the intent of the MBSE Initiative by presenting a broader, data-centric view of SE. From this perspective, modeling and other activities and SE are not synonymous. There are many work products, including models, that are generated during the execution of the SE lifecycle process activities.
This data-centric perspective of SE provides a better lens through which to acquire a complete understanding of the SE lifecycle process activities and resulting work products and underlying data and information needed to manage the development of increasingly complex systems of the future. This perspective aids in acquiring a practical understanding of the SE lifecycle processes from the perspective of not only models, but all work products that are generated from activities conducted during each of the SE lifecycle processes and the underlying data and information used to represent these work products.
Expanding on the concept of SE from a data-centric perspective, the goals of this blog are to:
* Present a broader data-centric perspective of SE that meets the intent the MBSE initiative and help organizations to move towards INCOSE’s Vision 2025
* Provide organizations an understanding that the integrated dataset is the foundation of SE
* Provide organizations guidance that can be used to successfully implement SE from a data-centric perspective
The overall goal is to make this blog series a useful contribution to help organizations implement the level of SE capability that best fits their needs.
The intended audience includes project and product managers and systems engineers who are stakeholders in activities defined by the SE discipline and are thinking about, or are in the process of, implementing SE within their organization. This guide will help those who are wondering how to successfully implement the intent of the MBSE Initiative within their organization and those that are interested in maturing their current SE capabilities toward a more data-centric implementation of SE – irrespective of the size and complexity of the system under development and the size and culture of the organization developing the system.
From a requirements perspective, this blog is also targeted to those who have been, or are currently, focused on defining, documenting, and managing requirements as a distinct and separate, stove-piped activity from other SE lifecycle processes. From a tool vendor perspective, this blog is targeted to those whose tools do not provide the capability to integrate requirements and the other work products and their underlying data and information across all SE lifecycle process activities. While these approaches may have worked in the past and may work for some present system development efforts, it is doubtful these approaches will allow organizations to meet the future challenges of increasingly complex systems and move towards INCOSE’s Vision 2025.
This blog series is organized as follows: (to read each part, click on the link for each part.)
Part 2, The Need for Systems Engineering addresses the need for SE and the benefits of adopting SE from a data-centric perspective. A list of challenges that need to be addressed due to the increasingly complex systems is presented followed by a list of benefits organizations can realize by practicing SE from a data-centric perspective. This section concludes with a discussion concerning another key advantage to practicing SE from a data-centric perspective – the use of measures to help better manage the system development activities.
Part 3, Systems Engineering: A Data-Centric Perspective introduces and defines the concept of practicing SE from a data-centric perspecitve. The section begins by discussing the SE work products and underlying data and information that are generated as part of each of the SE lifecycle activities. Next the questions: “What is a model?” and “What is model-based SE (MBSE)?” are addressed from a data-centric perspective. Lastly, the concept of integrated data as the foundation of SE is discussed followed by a revised defintion of SE from a data-centric perspective.
Part 4, Practicing SE from a Data-Centric Perspective goes into more detail on what it means to practice SE from a data-centric perspective providing guidance that can be used to understand and successfully create and manage the integrated dataset within an organization. This section starts with a discussion concerning the need for corporate management buy in and support needed to transition the organization from their present state to practicing SE from a data-centric perspective. Key concepts from big data are introduced including: data governance, information technology, and data management. This section concludes with a description concerning the current state of most organizations concerning practicing SE and the path needed to move from the current state to a future state where the projects within an organization practice SE from a data-centric perspective using a common, integrated dataset.
Part 5, Developing a Systems Engineering Capability that Meets the Needs of Your Organization focuses on topics to help organizations develop a systems engineering capability that meets the needs of their organization To aid in this journey, SE capability levels (SCLs) are presented to help organizations assess what their current SE capability is from an integrated dataset perspective and provide a roadmap to get to their desired level of SE capability based on their organization’s specific needs. Next, the selection of an SE toolset that is needed to implement the chosen SCL is discussed. The final topic in this section provides advice to help sell the idea of moving toward a data-centric practice of SE to management. Questionnaires are provided in the appendices to help organizations assess their current SCL and identify issues and risks they may be having which can be mitigated by practicing SE from a data-centric perspective.
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