Practicing SE from a data-Centric Perspective

This is part 4 of the blog series “Integrated Data as the Foundation of Systems Engineering.

In part 3, Systems Engineering: A Data-Centric Perspective, I introduced and defined the concept of practicing SE from a data-centric perspecitve.  In the first section I discussed the Systems Engineering work products and underlying data and information that are generated as part of each of the SE lifecycle activities.  I then address the questions: “What is a model?” and “What is model-based SE (MBSE)?” from a data-centric perspective.   In the second section I discussed what the concept of integrated data as the foundation of SE is and provide a revised defintion of Systems Engineering from a data-centric perspective.

In this part 4, I go into more detail concerning 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.

In this first section I discuss the need for enterprise and business management buy in and support needed to transition the organization from their present state to practicing SE from a data-centric perspective.

In the second section, I introduce key concepts from big data including: data governance, information technology, and data management.

In the third section, I discuss the current state of many organizations practicing SE from a data-centric perspective and the path needed to move from the current state to a future state where the projects within the organization practice SE from a data-centric perspective using a common, integrated dataset.

Success starts at the top

For projects to successfully implement SE from a data-centric perspective the journey must start at the top. Stakeholder needs and requirements exist at several levels (Ryan, 2013) within an organization as shown in Figure 1.

Figure 1: Levels of Stakeholder needs and Requirements (Ryan 2013)
Reprinted with permission from Mike Ryan. All other rights reserved.

At the top, there is an enterprise level in which enterprise leadership sets the enterprise strategies; a business management level in which business management derives business needs, constraints, and requirements; a business operations level (where the projects exist) in which stakeholders define their needs and requirements; a systems’ level in which the system is defined in logical and physical views; and subsequently, there are lower levels for the subsystem and other system elements.

To successfully practice SE from a data-centric perspective, the levels of the enterprise above the project level need to address process, tools, and people:

*  Processes need to be defined at the enterprise, business management, and business operations levels that support the chosen level of SE from a data-centric perspective capability;

*  SE tools and information technology (IT) infrastructure appropriate to the level of SE capability chosen needs to be provided by the IT organization at the business operations level; and

*  People within the projects need the training, knowledge, and experience appropriate to the level of SE capability being implemented by the organization consistent with type and complexity of systems being developed and the SE toolset adopted by the enterprise.

At the enterprise level, strategies are defined that will guide its future.   Leadership communicates their intentions regarding the operation of the organization—in terms of existing systems, processes, and systems to be developed.  Leadership defines the enterprise in terms of ‘brand’ and establishes a mission statement and corresponding goals and objectives which clearly state the reason for the enterprise and its strategy for moving forward.

The senior leadership develops a vision and advocates for the need to adopt SE from a data-centric perspective.  Leadership acknowledges the benefits and Return on Investment (ROI) associated with implementing SE from a data-centric perspective.

At the business management level the concepts, needs, resulting requirements are documented that will result in an infrastructure that enables the enterprise to adopt SE from a data-centric perspective.  This includes choosing the level of SE capability appropriate to the projects, defining data governance and information management policies and plans, and developing the information technology (IT) architecture requirements tailored to the needs of the projects, product lines, and culture of the enterprise.  Included at this level configuration management (CM) policy is defined.

Key measures are defined enabling management to track progress, identify and manage risk, identify issues and take action before the issues become problems.  These measures include data to help quantify the ROI.  For each project, business management defines “success” in terms of these measures which they use to track each project’s progress.

At the business operations level where the projects operate, the infrastructure is put in place to allow projects to develop and manage systems using an SE approach from a data-centric perspective at the level of SE capability defined by business management.  This involves defining an organization standard ontology, operating procedures, work instructions, processes, etc.; acquiring the IT infrastructure, defining a master schema for the project databases and file management systems, and acquiring an SE toolset with the capabilities and features needed by the projects that are developing systems.  In addition, the infrastructure is put into place needed to train project and engineering teams in the processes and SE toolset as well as in the concepts associated with practicing SE from a data-centric perspective.

For organizations with multiple business units, each with different product lines, each business unit provides the infrastructure tailored to their unique needs.  Note that the various business units may decide on different implementations of SE from a data-centric perspective.  Section 5.1 discusses SE Capability Levels (SCLs) that allow organizational elements to tailor their SE capabilities needed to successfully manage the development of the systems in their specific domain and types of systems they develop.

Assuming these activities are completed at the enterprise, business management, and business operations levels, the projects within the business operations level will have a much greater chance of success in implementation of SE from a data-centric perspective.   For a project to be successful, the following actions must be completed:

*  The senior management has agreed to implement SE from a data-centric perspective, and there is an enterprise level “champion”.

*  Data governance and information management policies have been defined.

*  The level of data-centric SE capability consistent with the needs of the project has been agreed to.

*  An IT infrastructure has been put into place that meets the needs of the project.

*  An SE toolset consistent with the needs of the project has been procured and licenses put in place.

*  The project has a defined ontology and master schema for the project’s integrated dataset.

*  Plans, processes, procedures, and work instructions have been defined by the program/project (plans include: Project Management Plan (PMP), Systems Engineering Management Plan (SEMP, and Information Management Plan (IMP)).

*  Project team members are trained in practicing SE from a data-centric perspective, the SE tools, defined schema, plans, processes, procedures, and work instructions.

In the next section I continue part 4 by introducing key concepts from big data including: data governance, information technology, and data management.

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