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| | ===Concepts=== | | ===Concepts=== |
| − | #'''[[Learning]]'''. Any relatively permanent change in behavior that occurs as a result of experience.
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| − | #*[[Lessons learned]]. The learning gained from the process of performing the project. Lessons learned may be identified at any point.
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| − | #*[[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.
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| − | #'''[[Reporting]]'''.
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| − | #*[[Internal reporting]].
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| − | #'''[[Reporting principle]]'''.
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| − | #'''[[Data research]]'''.
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| − | #*[[Qualitative research]].
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| − | #*[[Quantitative research]].
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| − | #'''[[Data structure]]'''.
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| − | #*[[Structured data]].
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| − | #*[[Unstructured data]].
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| − | #*[[Data validation]].
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| − | #'''[[Data model]]'''. An analysis model that depicts the logical structure of data, independent of the data design or data storage mechanisms.
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| − | #*[[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.
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| − | #*[[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).
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| − | #*[[Data flow diagram]] (DFD). An analysis model that illustrates processes that occur, along with the flows of data to and from those processes.
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| − | #*[[State diagram]]. An analysis model showing the life cycle of a data entity or class.
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| − | #*[[Sequence diagram]]. A type of diagram that shows objects participating in interactions and the messages exchanged between them.
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| − | #*[[Class model]]. A type of data model that depicts information groups as classes.
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| − | #'''[[Data dictionary]]'''. An analysis model describing the data structures and attributes needed by the system.
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| − | #*[[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.
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| − | #*[[Attribute]]. A data element with a specified data type that describes information associated with a concept or entity.
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| − | #*[[Glossary]]. A list and definition of the business terms and concepts relevant to the solution being built or enhanced.
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| − | #*[[Jargon]]. Specialized terminology or technical language that members of a group use to communicate among themselves.
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| − | #'''[[Data manipulation]]'''.
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| − | #*[[Filtering]]. A sender's manipulation of information so that it will be seen more favorably by the receiver.
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| − | #*[[Filtering]]. The deliberate manipulation of information to make it appear more favorable to the receiver.
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| − | #'''[[Categorization]]'''.
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| − | #*[[Structural rule]]. Structural rules determine when something is or is not true or when things fall into a certain category. They describe categorizations that may change over time.
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| − | #*[[Knowledge area]]. A group of related tasks that support a key function of business analysis.
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| − | #'''[[Appreciative inquiry]]'''. An approach that seeks to identify the unique qualities and special strengths of an organization, which can then be built on to improve performance.
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| − | #'''[[Repository]]'''. A real or virtual facility where all information on a specific topic is stored and is available for retrieval.
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| − | #*[[Interoperability]]. Ability of systems to communicate by exchanging data or services.
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| | ===Methods=== | | ===Methods=== |
