University of Kentucky
Information Resource Management
Glossary of Terms |
|
|
|
|
Access, Update
|
|
Ability
to not only view the data, but add, delete or modify it as well. |
|
|
Access, View
|
|
Ability
to only view or read the data, but not alter it in any fashion. . |
|
|
Accuracy
|
|
A
dimension of data quality. Data is accurate when it is free of errors
(Department of Defense; Inmon, Imhoff and Sousa
227). . |
|
|
Ad Hoc Query
Repository
|
|
A
collection of enterprise data from multiple sources, used to do ad hoc and
operational reporting where the need to use the most current and
un-standardized source data is a requirement. The Repository will typically contain only one or two years of
the most recent data, unless regulatory or statutory requirements dictate
otherwise. (Also known as an
Operational Data Store or ODS.) |
|
|
Administrative
Data
|
|
Data
used to support or is relevant to the administrative, operational or planning
functions within the institution (Arizona State University). . |
|
|
Attribute
|
|
Pertaining
to the development of the Data Architecture or logical data model, the term
is used to describe data that further defines or describes an Entity (Purdue
University; Radcliffe 12; Redman 289). The term “attribute” is similar to
“element”. For example, for a student
“entity”, attributes could include identification number, name, sex and
HEW/ethnic code. . |
|
|
Committee of
Data Custodians
|
|
A
group of middle level management responsible for the collection and
maintenance of specific data in their functional areas (Indiana University;
University of Virginia), enforcing corresponding policy and procedures, and
providing accurate analysis and presentation of their data for reporting.
These individuals are typically identified as the “super users” or “System
Administrators” who are the experts on the administrative systems which
support their areas, and are the most familiar with the interrelationship
between policy and procedures, business rules, data systems and data
standards. In addition, this committee includes the sector Effectiveness
Coordinators. The Custodians serve as a primary source of information on
their data, recommend security classifications and assign access rights for
all their enterprise data, and are responsible for assisting Data
Administration in researching problems, identifying solutions, developing
documentation, policies and procedures, and implementing any process, policy,
procedure or process changes required to address data administration issues.
(See AR III-2.0-1.) . |
|
|
Committee of
Data Stewards
|
|
A
group of higher-level management who have the responsibility for planning and
setting directions for the management of institutional data (Indiana
University; University of Virginia). Members are familiar with the
institutional ramifications of data access, security, quality and analysis,
and are cognizant of the various regulatory mandates with which the
University must comply. This group reviews pertinent information and makes
recommendations to the Vice President for Administration, and the Medical
Center Vice Chancellor for Information Technology (also CIO of the University
Hospital) where appropriate, on all data administration procedures and
policies, priorities for data quality projects, the sequence in which the
official institutional systems will be included in the Data Warehouse,
identifications of all enterprise data to be managed and stored within the
Data Warehouse, security classifications for all enterprise data, potential
system purchases or enhancements which will not be compliant with the
institutional data architecture, and disputes over data access, use and
ownership. (See AR III-2.0-1.) . |
|
|
Completeness
|
|
A
dimension of data quality. Data is complete when data values are present for
all records, occurrences or logical entities that require them within the
database (Department of Defense; Redman 256, 289). For example, if only 50%
of our students’ records contain a high school GPA, then this data is
incomplete. (NOTE: If data is
evaluated as incomplete, this does not necessarily indicate a data quality problem
if the particular data item is not a “required” piece of information.) . |
|
|
Computing
Services
|
|
Organization
within Information Systems or the Medical Center responsible for the
installation, maintenance and security of the official institutional computer
systems, and the development of Disaster Recovery Plans. Also referred to as
Enterprise Computing Services or Medical Center Information Services (MCIS).
. |
|
|
Confidential
|
|
Security
classification given to data available to the general university population
for either view or update. In this instance, security is required, and
authentication is by login with group status (i.e., faculty, staff, student,
nurses or clinicians). Information may be transmitted across the network in
clear text. Examples of this type of information include the University
Administrative Regulations, library databases or other university specific
site licenses, public awareness information, and policies and procedures for
Medical Center employees. . |
|
|
Consistency
|
|
A
dimension of data quality. Data is consistent if it is represented in the
same format, with the same value range where the meaning of the valid values
is the same for like data in all databases (Department of Defense), and when
the values in a given element are reasonable when viewed in combination with
the values of other associated data (Redman 259). If a HEW code of “1” means
Caucasian in one database, but African American in another, then it is not
consistent. Also, if a student’s state of origin is CA, but the student has
been classified as an in-state student and tuition has been charged
accordingly, then the combination of the state of origin and student
residency status is inconsistent. . |
|
|
Data
|
|
A
term used to describe material which will be entered into or is contained in
computer files, and which can be processed by the computer (Radcliffe 60).
. |
|
|
Data Access
|
|
The
ability to view, add, delete, modify, query, report, summarize or otherwise
manipulate data. . |
|
|
Data Administration
|
|
The
application of strict guidelines in the management of data; also the
department within the university responsible for these functions (Indiana
University; Radcliffe 60). . |
|
|
Data Administrator
|
|
The
manager of Data Administration, and the individual ultimately responsible for
the tasks assigned to this department.
(Also known as the Director of Information Resources Management at
UK.) . |
|
|
Data Architecture
|
|
The
conceptual data structure or logical data model, including standards on data
format, valid value ranges and relationships with other data (Redman 41).
. |
|
|
Data Inventory
|
|
List
of all enterprise data owned by the university, including but not limited to
information on the data’s standard name, description, relationship to other
data, source database (Redman 40), Data Warehouse conversion requirements,
general access or special security requirements, who has access to the data,
how access can be obtained, how the data is used, the frequency with which
the data is updated, any archiving requirements, and the individuals
responsible for the data or who are considered the “primary owners”. The Data
Inventory serves as a resource for data management, and provides sufficient
information to enhance user understanding of and access to data. Also
referred to as Data Library or Data Dictionary. . |
|
|
|
Data Mart |
|
A
subset of enterprise data from the Data Warehouse that is summarized and
stored in an optimal fashion for analysis and presentation of information to
support trend analysis and tactical decisions and processes. Data Marts are typically designed based on an analysis of user needs to answer
specific questions in the pursuit of specific goals. The scope can be that of a complete data
subject such as Student, or of a particular business area or line of
business, such as Enrollment. (Improving
Data Warehouse and Business Information Quality, Larry P. English,
1999.) |
|
|
Data Mining
|
|
A
class of database applications that look for hidden patterns in a group of
data. True data mining software
actually discovers previously unknown relationships among the data. (http://www.webopedia.com/TERM/data_mining.html) |
|
Data
Mining is the process of analyzing large volumes of data using pattern
recognition or knowledge discovery techniques to identify meaningful trends
and relationships represented in data in large databases. (Improving Data Warehouse and Business
Information Quality, Larry P. English, 1999.) For example, Data Mining software can help identify the
relationship between student characteristics and their enrollment
status. |
|
|
Data Quality
|
|
A
dimension or measurement of data in reference to its accuracy, completeness,
consistency, timeliness, uniqueness and validity (Department of Defense).
Data is considered to be of high quality if it has all of the above
attributes. |
|
|
|
Data Staging Area |
|
Storage
and processing area for data extracted from the internal and external systems
prior to loading into the Warehouse, Data Marts or Ad Hoc Query
Repository. Some of the data will
remain un-cleansed and an exact replica of the data in the online systems,
for subsequent loading into the Ad Hoc Query Repository. Other data will be cleansed and
transformed before being moved to the Data Warehouse and Data Marts for
analysis. Some data will be located
in multiple places and in multiple forms and aggregations. |
|
|
Data
Warehouse
|
|
An
enterprise-wide, cross-functional, cross-organizational database typically
comprised of data extracted and/or summarized from multiple online
transaction processing systems, and other stores of data (Purdue University;
Stanford University). It is designed for query and analysis, typically
contains historical data, and is used to present information to support
decision-making, tactical and strategic business processes. A data warehouse tends to start from an
analysis of what already exists and how it can be collected in such a way that
the data can late be used. In general, a data warehouse tends to be a strategic,
but somewhat unfinished concept; a data mart tends to be tactical and aimed
at meeting an immediate need.
(Improving Data Warehouse and Business Information Quality, Larry
P. English, 1999.). . |
|
|
Database
|
|
A
collection of data (Redman 290) that has been verified against specific edit
criteria in a structured manner, and stored with the ability to manage and
control usage. . |
|
|
Drill Down/Up
|
|
Drilling
down or up is a specific analytical technique whereby the user navigates
among levels of data ranging from the most summarized (up) to the most
detailed (down). The drilling paths
may be defined by the hierarchies within dimensions or other relationships
that may be dynamic within or between dimensions. For example, when viewing sales data for North America, a
drill-down operation in the Region dimension would then display Canada, the
eastern United states and the Western United States. A further drill-down on Canada might
display Toronto, Vancouver, Montreal, etc.
(http://www.olapcouncil.org/research/glossaryly.htm) |
|
|
Element
|
|
“An
item of data within an array, matrix, set or collection” (Spencer 205), or
attribute of an entity. (Corresponds
to the columns on a spreadsheet).
Examples of an element could include name, gender, ethnic code, and
date of birth within a student table. .
|
|
|
Enterprise Computing Services
|
|
Organization
within Information Systems responsible for the installation, maintenance and
security of the official institutional computer systems, and the development
of Disaster Recovery Plans. This entity includes Enterprise Database and
Applications, and Enterprise Systems. .
|
|
|
Enterprise Data
|
|
Data
considered to be important to the administration, operations, or planning for
a significant portion of or the entire institution (Arizona State University;
Redman 45); typically stored, fed into or received from one of the official
institutional databases; used as part of an official university report or to
evaluate the attainment of strategic goals (Arizona State University; Indiana
University); or whose existence and integrity must be guaranteed to
comply with legal requirements and University needs (University of Virginia).
. |
|
|
Entity
|
|
Pertaining
to the development of the Data Architecture or logical data model, the term
is used to describe a thing of significance about which the system or database
needs to collect and store data (Purdue University). It “can be a person
place or thing, or a concept”
(McClanahan 2). Examples of
entities could include student, employee, department or functional area.
. |
|
|
Executive Information System
(EIS)
|
|
An
application developed to provide senior management direct access to
information relevant to an organization’s goals and performance. These applications are developed to
gather, analyze and integrate internal and external data to provide
management with insight into key performance indicators, potential problems,
and changes in the environment.
Typical features include extensive use of graphics, simple
navigational controls, automatic replacement of report contents, drill-down
analysis, trend analysis capabilities, exception reporting or alerts,
graphical charts with links to underlying reports, provision of data from
multiple sources, and the highlighting of information an executive feels is
critical. (The Data Warehouse
Lifecycle Toolkit, Ralph Kimball, et al.) |
|
|
|
|
External Data or Database
|
|
Data
purchased, acquired or to which access is granted from an organization
external to the University (Inmon, Imhoff and Sousa 240), typically for use
within the Data Warehouse, such as economic and census data collected by the
government. |
|
|
Information
|
|
Generally
used as a synonym for data, facts or knowledge, but in the strictest sense,
is “any kind of knowledge or message than can be used to make possible a
decision or action” (Theoretical Analysis
of Information Systems by B. Langefors). .
|
|
|
Information Systems
|
|
Sector
within the institution responsible for the development and operation of the
infrastructure fueling the computing, networking, telecommunications, print
and electronic publishing, and postal services systems that support the
university's academic, research and service missions. This sector includes
the following departments: Financial Services, Technology Planning,
Communication and Network Services, Enterprise Computing Services, Data
Administration, Academic Computing Services, Desktop Support and Publishing
Services, Media Design, Libraries, and Distance Learning Technology. . |
|
|
Institutional Systems
|
|
“Official”
institutional systems are those systems and databases primarily containing
enterprise data, and installed and maintained by Enterprise Computing
Services within Information Systems, or by Medical Center Information Services
for use across the Health Care Enterprise. “General” institutional systems
are all those purchased or created by any part of the university. . |
|
|
Integrity
|
|
This
term is often used as a synonym for “quality”. Files and records of data are said to maintain “integrity” if
the data quality during the transmission or movement of data from one source
or location to another is not compromised in any fashion. . |
|
|
Knowledge
|
|
The “acquaintance with facts, truths, or
principles” (Costello 750). . |
|
|
MCIS
|
|
Medical
Center Information Services; a division within the Medical Center providing
computerized services and application systems. . |
|
|
Metadata
|
|
A
term used for data that describes or specifies other data. It is used to define all of the
characteristics of data required to build databases and applications, and to
support knowledge workers and information producers. This includes the element name, meaning,
format, domain values, business integrity rules, relationships, owner,
etc.. |
|
|
|
A
category of software technology that enables analysts, managers and
executives to gain insight into data through fast, consistent, interactive
access to a wide variety of possible views of information. |
|
OLAP
helps the user synthesize enterprise information through comparative,
personalized viewing, as well as through analysis of historical and projected
data in various “what-if” data model scenarios. This is achieved through use of an OLAP Server. |
|
OLAP
functionality is characterized by dynamic multi-dimensional analysis of
consolidated enterprise data supporting end user analytical and navigational
activities including: ·
calculations and
modeling applied across dimensions…, ·
trend analysis over
sequential time periods, ·
slicing subsets for
on-screen viewing, ·
drill-down to deeper
levels of consolidation, ·
reach-through to
underlying detail data, ·
rotation to new
dimensional comparisons in the viewing area
(http://www.moulton.com/olap/olap.glossary.html) |
|
|
|
Operational Data |
|
Data
from internal systems, such as IDMS (FES, FRS, HRS, SIS), Oracle or
Sybase. |
|
|
|
Pivot |
|
See
Rotate. |
|
|
Protected
|
|
Security
classification given to highly secure institutional data as identified by
federal or state laws or institutional officials. This data is typically
associated with an individual patient, student or employee and restricted for
legal reasons of confidentiality and privacy. It is restricted for view and
update to a limited number of the university community, usually based on some
attribute such as organizational department, functional area, account number,
etc. In this instance, security is required, and authentication is by
individual login against a security database requiring valid passwords and
user IDs, in addition to a challenge/response validation process. Data of
this type may NOT be transmitted across the network in clear text. Examples
include the Hospital patient records, and student data covered by FERPA.
. |
|
|
Public
|
|
Security
classification given to data available to the general public for viewing and
query with open access. In this instance, no security or authentication is
required for access, and the data may be transmitted in clear text across the
network. Examples of this type of information include the University Web
page, Medical Library news or guidelines for diagnosis, schedule book, course
catalogs, etc. . |
|
|
Reach Through
|
Reach through
is a means of extending the data accessible to the end user beyond that which
is stored in the OLAP server. A reach
through is performed when the OLAP server recognizes that it needs additional
data and automatically queries and retrieves the data from a data warehouse
or OLTP system. (http://www.olapcouncil.org/research/glossaryly.htm)
|
|
|
Record
|
|
“A
collection of related items of data treated as a unit” (Spencer 485). (Corresponds to the rows on a
spreadsheet). Examples of a record
would be the data related to a particular student or employee within a table.
. |
|
|
Restricted
|
|
Security
classification given to data restricted to a limited number of the university
and Medical Center community for view or update, usually based on some
attribute such as organizational department, functional area, account number,
etc. In this instance, security is required, and authentication is by
individual login against a security database requiring valid passwords and
user IDs. Data of this type may NOT be transmitted across the network in
clear text. Examples include the accounting information residing in the
Financial System, and patient Care information within the Medical Center. . |
|
|
Rotate
|
|
To
change the dimensional orientation of a report or page display. For example, rotating may consist of
swapping the rows and columns, or moving one of the row dimensions into the
column dimension, or swapping an off-spreadsheet dimension with one of the
dimensions in the page display (either to become one of the new rows or
columns), etc. A specific example of
the first case would be taking a report that has Time across (the columns)
and Products down (the rows) and rotating it into a report that has Product
across and Time down. An example of
the second case would be to change a report which has Measures and Products
down and Time across into a report with Measures down and Time over Products
across. An example of the third case
would be taking a report that has Time across and Product down and changing
it into a report that has Time across and Geography down. Synonym:
Pivot. (http://www.olapcouncil.org/research/glossaryly.htm) |
|
|
Security Classification
|
|
Categorization
of data into one of four groups (public, confidential, restricted and
protected) which identifies the level of access to computer systems and
networks required to protect data from inappropriate disclosure,
manipulation, and misuse. These security categories are determined based on
the institutional Security Policy, plus federal and state regulations or
laws. . |
|
|
Slice
|
|
A
slice is a subset of a multi-dimensional array corresponding to a single
value for one or more members of the dimensions not in the subset. For example, if the member Actuals is
selected from the Scenario dimension, then the sub-cube of all the remaining
dimensions is the slice that is specified.
The data omitted from this slice would be any data associated with the
non-selected members of the Scenario dimension, for example Budget, Variance,
Forecast, etc. From an end user
perspective, the term slice most often refers to a two-dimensional page
selected from the cube. (http://www.olapcouncil.org/research/glossaryly.htm) |
|
|
Table
|
|
“Contains
data of a certain type and represents an entity or relationship” (McClanahan
2). (Corresponds to the data
contained in a spreadsheet). Examples
of a table could be a student demographic table, student address table, etc.
. |
|
|
Timeliness
|
|
A
dimension of data quality. Data is timely when it is current or up to date as
defined by the data’s owner (Department of Defense). Some data may only need
to be entered once, as it never changes, while other data may need to be
verified and/or updated on a frequent basis if it is not static. An
individual’s birth date would fall into the former category, while their
local address would belong to the latter. .
|
|
|
Uniqueness
|
|
A
dimension of data quality. Data is unique if one value can be attached to
only one record or logical entity (Department of Defense). For example, an
individual should only have one identification number, and a given
identification number should only be assigned to one person. However, many
people may have the same name. . |
|
|
Update, Departmental
|
|
Data
update category for data only updated either online by a limited number of
university staff approved by the Data Custodians, or by an internal batch
computer job that updates the data based on internal business rules and the
value of other data in the database. (Data of this type usually falls into
the restricted or protected security classifications.). |
|
|
Update, External
|
|
Data
update category for data initially loaded from an external source. While this
data may be later modified by university staff, the majority of the data is
entered and/or maintained by loading data from an external source such as the
Department of Education or ACT. (Data of this type usually falls into the
restricted or protected security classifications.) . |
|
|
Update, Personal
|
|
Data
update category for personal data an individual may enter directly into the
database through either IVR or Web access. For example, data updated by a
student through the use of either Voice or Web Registration applications
would fall into this category. (Data of this type usually falls into the
restricted security classification.) .
|
|
|
Users
|
|
Individuals
who have either update or view access to institutional data. . |
|
|
Validity
|
|
A
dimension of data quality. Data is considered valid if all of its values are
within a predefined range (Department of Defense). If a student’s state of
origin is OH, it is within the valid range of state codes. . |
|
|
