Difference between revisions of "Bookkeeping Quarter"
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#*[[Lessons learned]]. The learning gained from the process of performing the project. Lessons learned may be identified at any point. | #*[[Lessons learned]]. The learning gained from the process of performing the project. Lessons learned may be identified at any point. | ||
#*[[Lessons learned process]]. A process improvement technique used to learn about and improve on a process or project. A lessons learned session involves a special meeting in which the team explores what worked, what didn't work, what could be learned from the just-completed iteration, and how to adapt processes and techniques before continuing or starting anew. | #*[[Lessons learned process]]. A process improvement technique used to learn about and improve on a process or project. A lessons learned session involves a special meeting in which the team explores what worked, what didn't work, what could be learned from the just-completed iteration, and how to adapt processes and techniques before continuing or starting anew. | ||
| + | *[[Attribute]]. A data element with a specified data type that describes information associated with a concept or entity. | ||
| + | *[[Class model]]. A type of data model that depicts information groups as classes. | ||
| + | *[[Class]]. A descriptor for a set of system objects that share the same attributes, operations, relationships, and behavior. A class represents a concept in the system under design. When used as an analysis model, a class will generally also correspond to a real-world entity. | ||
| + | *[[Data dictionary]]. An analysis model describing the data structures and attributes needed by the system. | ||
| + | *[[Data entity]]. A group of related information to be stored by the system. Entities can be people, roles, places, things, organizations, occurrences in time, concepts, or documents. | ||
| + | *[[Data flow diagram]] (DFD). An analysis model that illustrates processes that occur, along with the flows of data to and from those processes. | ||
| + | *[[Data model]]. An analysis model that depicts the logical structure of data, independent of the data design or data storage mechanisms. | ||
| + | *[[Interoperability]]. Ability of systems to communicate by exchanging data or services. | ||
| + | *[[Knowledge area]]. A group of related tasks that support a key function of business analysis. | ||
| + | *[[Metadata]]. Metadata is information that is used to understand the context and validity of information recorded in a system. | ||
| + | *[[Optionality]]. Defining whether or not a relationship between entities in a data model is mandatory. Optionality is shown on a data model with a special notation. | ||
| + | *[[State diagram]]. An analysis model showing the life cycle of a data entity or class. | ||
| + | *[[Cardinality]]. The number of occurrences of one entity in a data model that are linked to a second entity. Cardinality is shown on a data model with a special notation, number (e.g., 1), or letter (e.g., M for many). | ||
===Methods=== | ===Methods=== | ||
Revision as of 19:39, 30 March 2018
Learning Quarter (hereinafter, the Quarter) is the first of four lectures of Operations Quadrivium (hereinafter, the Quadrivium):
- The Quarter is designed to introduce its learners to enterprise discovery, or, in other words, to concepts related to obtaining data needed to administer the enterprise effort; and
- The Quadrivium examines concepts of administering various types of enterprises known as enterprise administration as a whole.
The Quadrivium is the first of seven modules of Septem Artes Administrativi, which is a course designed to introduce its learners to general concepts in business administration, management, and organizational behavior.
Contents
Outline
The predecessor lecture is Group Dynamics Quarter.
Concepts
- Learning. Any relatively permanent change in behavior that occurs as a result of experience.
- Lessons learned. The learning gained from the process of performing the project. Lessons learned may be identified at any point.
- Lessons learned process. A process improvement technique used to learn about and improve on a process or project. A lessons learned session involves a special meeting in which the team explores what worked, what didn't work, what could be learned from the just-completed iteration, and how to adapt processes and techniques before continuing or starting anew.
- Attribute. A data element with a specified data type that describes information associated with a concept or entity.
- Class model. A type of data model that depicts information groups as classes.
- Class. A descriptor for a set of system objects that share the same attributes, operations, relationships, and behavior. A class represents a concept in the system under design. When used as an analysis model, a class will generally also correspond to a real-world entity.
- Data dictionary. An analysis model describing the data structures and attributes needed by the system.
- Data entity. A group of related information to be stored by the system. Entities can be people, roles, places, things, organizations, occurrences in time, concepts, or documents.
- Data flow diagram (DFD). An analysis model that illustrates processes that occur, along with the flows of data to and from those processes.
- Data model. An analysis model that depicts the logical structure of data, independent of the data design or data storage mechanisms.
- Interoperability. Ability of systems to communicate by exchanging data or services.
- Knowledge area. A group of related tasks that support a key function of business analysis.
- Metadata. Metadata is information that is used to understand the context and validity of information recorded in a system.
- Optionality. Defining whether or not a relationship between entities in a data model is mandatory. Optionality is shown on a data model with a special notation.
- State diagram. An analysis model showing the life cycle of a data entity or class.
- Cardinality. The number of occurrences of one entity in a data model that are linked to a second entity. Cardinality is shown on a data model with a special notation, number (e.g., 1), or letter (e.g., M for many).
Methods
Instruments
Practices
The successor lecture is Stakeholder Engagement Quarter.
