Knowledge Management

1.    Define knowledge management and describe its purposes.
Knowledge management (KM) is the systematic and active management of ideas, information, and knowledge residing in an organization’s employees.
    Its purposes include effective and efficient problem solving, dynamic learning, strategic planning, and decision making. KM initiatives focus on identifying knowledge, explicating it in such a way that it can be shared in a formal manner, and leveraging its value through reuse.

Collaborative Computer-Supported Technologies and Group Support Systems

1.    Define groupwork.
Groupwork is “work done by two or more people together.”

2.    List five characteristics of groupwork.
The text gives the characteristics listed below. A correct answer can consist of any five:
•    A group performs a task (sometimes decision making, sometimes not).
•    Group members may be located in different places.
•    Group members may work at different times.
•    Group members may work for the same organization or for different organizations.
•    A group can be permanent or temporary.
•    A group can be at one managerial level or can span several levels.
•    There can be synergy (leading to process and task gains) or conflict in groupwork.
•    There can be gains and/or losses in productivity from groupwork.
•    The task may have to be accomplished very quickly.
•    It may be impossible or too expensive for all the team members to meet in one place, especially when the group is called for emergency purposes.
•    Some of the needed data, information, or knowledge may be located in many sources, some of which may be external to the organization.
•    The expertise of non-team members may be needed.
•    Groups perform many tasks; however, groups of managers and analysts frequently concentrate on decision making.
•    The decisions made by a group are easier to implement if supported by all (or at least most) group members.

Business Performance Management

1.    Define BPM.
“A framework for organizing, automating, and analyzing business methodologies, met-rics, processes, and systems to drive the overall performance of the enterprise. It helps organizations translate a unified set of objectives into plans, monitor execution, and deliver critical insight to improve financial and operational performance.”

2.    How Does BPM differ from BI? How are they the same?
BPM is an outgrowth of BI and extends it. BI needs to be extended to support BPM properly.
• BPM is promoted and sold by the same companies that market and sell the BI tools and suites.
• BI has evolved so that many of the original differences between the two no longer exist (e.g., BI used to be focused on departmental rather than enterprise-wide projects).
• BI is a crucial element of BPM.

Data Warehouse

1.    What is a data warehouse?
A data warehouse is defined in this section as “a pool of data produced to support decision making.” This focuses on the essentials, leaving out characteristics that may vary from one DW to another but are not essential to the basic concept.
    The same paragraph gives another definition: “a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision-making process.” This definition adds more specifics, but in every case appropriately: it is hard, if not impossible, to conceive of a data warehouse that would not be subject-oriented, integrated, etc.

2.    How is a data warehouse different from a database?
Technically a data warehouse is a database, albeit with certain characteristics to facilitate its role in decision support. Specifically, however, it is (see previous question) an “integrated, time-variant, nonvolatile, subject-oriented repository of detail and summary data used for decision support and business analytics within an organization.” These characteristics, which are discussed further in the section just after the definition, are not necessarily true of databases in general—though each could apply individually to a given one.
    As a practical matter most databases are highly normalized, in part to avoid update anomalies. Data warehouses are highly denormalized for performance reasons. This is acceptable because their content is never updated, just added to. Historical data are static.