In this second section of part 4, I introduce key concepts from big data including: data governance, information technology, and data management.
These are probably new concepts few systems engineers have thought about – let alone addressed in their organization – yet are essential concepts for organizations to understand to be successful in their journey towards implementing SE from a data-centric perspective.
Data Governance
Data Governance (DG) is the formulation of policy to optimize, secure, manage, and leverage data and information as an enterprise asset.
The following basic principles of DG need to be established at the enterprise level. These basic principles guide all enterprise activities:
- Data and information are assets – Data and information are assets that have value to the enterprise and must be managed accordingly. Data and information are the life-blood of the enterprise.
- Data and information must be able to be trusted – To be trusted the data and information must be correct, consistent, of high quality, and managed.
- Data and information must be Secure – Data and information must be protected from unauthorized use and disclosure.
- Data and information risk must be mitigated – There is risk associated with data and information which must be recognized and mitigated. This risk also can represent a liability if data and information is compromised or misused.
- Data and information must be accessible and shareable – Users must have access to the data and information necessary to perform their duties; therefore, data and information must be sharable across the enterprise functions and organizations that have a need for the data and information.
- Data and information have an owner and steward – Each data element and information has a data owner accountable for proper management, access, and usage of the data and information and a steward accountable for data and information quality.
- A Common Vocabulary (ontology and schema) must be defined – All data and information must be clearly defined consistently throughout the enterprise with the definitions understandable and available to all stakeholders.
DG for the enterprise is established and controlled at the business management level to implement the basic principles defined by the enterprise. The focus is on the “what”. The “How”, implementation, is defined at the business operations/project level. DG includes vision, principles, processes, and requirements to oversee and control the management of data and information and the use of data and data-related resources and information within the enterprise to:
* Ensure that data and information is managed in alignment with the basic principles and needs of the enterprise;
* Manage data and information within the largest relevant context of the enterprise strategy, goals, and objectives;
* Define the data and information to be governed and policies for: security, assess, sharing, quality, and backup/archival storage, and retention;
* Ensure compliance with regulations, standards, policies, and requirements that govern access, privacy, quality, and security of the data and information;
* Support and enable knowledge based decisions, analysis, and analytics;
* Ensure data and information usage achieves maximum value to the enterprise and its customers while managing the cost and quality of information handling; and
* Enforce the consistent, integrated, and disciplined use of data and information within the enterprise and partners.
DG requires cross-organizational cooperation to deliver timely, trustworthy data for better decisions and knowledge. DG is achieved through a partnership between the Business Management and Business Operations as shown in Figure 2.
Figure 2: Cross-organizational cooperation
Data Governance defines the rules “what”. Data Management and Information Technology adhere to the rules “how”. Data Management defines the needs and requirements for the information technology infrastructure. Information Technology supplies and maintains that infrastructure per those requirements. Organizational elements (business units and projects) conduct business operations in adherence to the rules and with the supplied infrastructure. The organizational elements are responsible for Data Management and the resulting data and information assets.
Information Technology
Information Technology (IT) is responsible for the IT infrastructure needed in support of business management level data governance and management needs and requirements. The IT organization exists at the business operations level. The role of IT is to:
- Develop, establish, and manage enterprise data architecture and platforms in alignment with data governance and data management policies, principles, processes, and requirements defined at the business management level.
- Supply, maintain, and provide support for hardware and software (Project Management and SE toolsets) needed to meet the needs of the organizational element data management activities.
- Design and implement data access, security, search, sharing, quality, backup, and archival storage control services in alignment with enterprise data governance and management and needs of the individual organizational elements.
Data Management
The purpose of Data Management (DM) is the management of data and information assets within an enterprise and organization element(s). DM occurs at the business operations level by the business units and programs/projects within the business units. DM addresses the “how” to implement data governance and data and information management requirements defined at the business management level. There are multiple levels of data management:
* Business Management: defines, controls, monitors implementation, and ensures compliance with enterprise data governance policies, requirements, and processes and provides the direction, philosophy, and mindset required to manage enterprise data assets.
* Organizational Element(s): develop, implement, and manage data and information management plans that implement enterprise data governance requirements and processes. The organizational elements are responsible for the day-to-day “activities” that must be performed to achieve the management of data and information assets within the organizational element. Practicing SE from a data-centric perspective is enabled by these data and information management activities.
INCOSE’s SE HB includes the following technical management processes: project planning, project assessment and control, decision management, risk management, configuration management, information management, measurement, and quality assurance (QA).
The Project Planning Process (INCOSE SE HB section 5.1) includes the development of a Project Management Plan (PMP) that establishes the direction and infrastructure necessary to enable the assessment and control of the project progress and identifies the details of the work and the needed set of personnel, skills, and facilities with a schedule and budget for resources from within and outside the organization needed to produce the system of interest.
A major activity in project planning is preparing the Systems Engineering Management Plan (SEMP). The SEMP needs to:
* establish that the project will conduct SE from a data-centric perspective and define the level of SE capabilities that will be used by the project.
* include definitions of the SE lifecycle processes and identification of all work products generated as part of the activities associated with these processes as well as the major deliverables of the project.
* address the form of the work products (paper vs electronic), the SE tools to be used to generate and maintain the work products and underlying data and information, and the IT infrastructure needed.
* define the key measures and work product attributes that will be used to manage the system development effort
* define a project ontology
Both the PMP and SEMP need to identify the reports that will be used to manage and track progress of the system development lifecycle process activities. These reports help define the data and information needed to be managed within the common, integrated dataset. Knowing what data and information will be included in the reports helps inform the formation of the common, integrated dataset schema.
The Information Management Process (INCOSE SE HB section 5.6) supplements the PMP and SEMP addressing the functions associated with project information management. The Information Management Process ensures the project’s data and information is properly stored, maintained, secured, and accessible to those who need it, thereby establishing/maintaining integrity of relevant system lifecycle work products and underlying data and information. The Information Management Process provides the basis for the management of and access to project data and information throughout the system lifecycle.
Specific details concerning the Information Management Process are tailored to a specific project and included in the project’s Information Management Plan (IMP). The IMP identifies the system‐relevant data and information to be collected, retained, secured, and disseminated. The preparation of the project IMP at the beginning of the project is essential to reap the benefits of practicing SE from a data-centric perspective. The IMP needs to:
* identify the resources and personnel skills required specific to information management;
* define the tasks to be performed;
* define the rights, obligations, and commitments of parties for generation, management, and access;
* identify data and information management tools and processes, as well as methodologies, standards, and procedures that will be used by the project;
* establish the scope of project data and information that is to be maintained;
* define a master schema for the integrated dataset and databases that will be used to store the data associated with the various work products and underlying data across all SE lifecycle processes. The schema includes formats and media for capture, retention, transmission, and retrieval of data and information;
* establishing and maintaining a system data dictionary;
* define project relevant data and information, access privileges, and sharing criteria;
* identify valid sources of data and information and designating authorities (owners) and responsibilities regarding the origination, generation, capture, archival, sharing, and disposal of information in accordance with the records and configuration management process and governing standards and requirements; and
* identify the standards by which the data and information will be created, managed, and stored. These standards enable the integration and sharing of the data and information contained in the integrated dataset.
As stated in the INCOSE SE HB (INCOSE 2015) of particular concern for practicing SE from a data-centric perspective is “the integration of data and information via databases, such as the decision database, the various datasets that represent the SE lifecycle work products, the ability to access the results from decision gate reviews and other decisions made by the project; requirements management and modeling tools and databases; computer‐based training and electronic interactive user manuals; websites; and shared information spaces over the Internet, such as INCOSE Connect.”
With effective data and information management, data and information is readily accessible to authorized project and organizational element personnel. Challenges related to maintaining databases, security of data, sharing data across multiple platforms and organizations, and transitioning when technology is updated are all need to be addressed by the PMP, SEMP, and IMP.
Effective data and information management is essential to successfully implementing SE from a data-centric perspective, enabling the projects to create and manage an integrated dataset that will be the foundation of all the project’s SE activities.
In the third section of part 4, I discuss the current state of most organizations concerning 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 an organization practice SE from a data-centric perspective using a common, integrated dataset.
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