Databases Reference
In-Depth Information
Data quality
: Data is of high quality ''if [it is] fit for [its] intended uses in
operations, decision making, and planning'' (J. M. Juran). Alternatively,
data is deemed of high quality if it correctly represents the real-world
construct to which it refers. These two views can often be in disagreement,
even about the same set of data used for the same purpose.
Data quality refers to the degree of excellence exhibited by the data in
relation to the portrayal of the actual phenomena (GIS Glossary).
The state of completeness, validity, consistency, timeliness and accu-
racy that makes data appropriate for a specific use (Government of
British Columbia).
Data quality: The processes and technologies involved in ensuring the
conformance of data values to business requirements and acceptance
criteria.
Data profiling
: This is the process of examining the data available in an
existing data source (e.g., a database or a file) and collecting statistics and
information about that data. The purpose of these statistics may include
the following:
Determine whether the existing data can easily be used for other pur-
poses.
Provide metrics on data quality, including whether the data conforms
to company standards.
Assess the risk involved in integrating data for new applications,
including the challenges of joins.
Track data quality.
Assess whether metadata accurately describes the actual values in the
source database.
Understand data challenges early in any data-intensive project in order
to avoid late project surprises. Finding data problems late in the project
can incur time delays and project cost overruns.
Have an enterprise view of all data, for uses such as master data
management, for which key data is needed, or data governance, for
improving data quality.
Some companies also look at data profiling as a way to involve busi-
ness users in what traditionally has been an IT function. Line-of-business
users can often provide context about the data, giving meaning to columns
of data that are poorly defined by metadata and documentation.
Beyond simply working with the data, information management must
also address the organization's business processes. Business processes come
