Informatica, IBM, Trillium Software, SAS & SAP are in
the leaders quadrant for “Gartner Magic Quadrant for Data Quality Tools : 2014”
Market Definition/Description
Data quality assurance is a discipline that focuses on
ensuring data is fit for use in business processes. These processes range from
those used in core operations to those required by analytics and for decision
making, regulatory compliance, and engagement and interaction with external
entities.
As a discipline, data quality assurance covers much more
than technology. It also includes roles and organizational structures;
processes for monitoring, measuring, reporting and remediating data quality
issues; and links to broader information governance activities via
data-quality-specific policies.
Given the scale and complexity of the data landscape, across
organizations of all sizes and in all industries, tools to help automate key
elements of this discipline continue to attract more interest and to grow in
value. As such, the data quality tools market continues to show substantial
growth, while also exhibiting innovation and change.
This market includes vendors that offer stand-alone software
products to address the core functional requirements of the discipline, which
are:
Data profiling and
data quality measurement: The analysis of data to capture statistics
(metadata) that provide insight into the quality of data and help to identify
data quality issues.
Parsing and
standardization: The decomposition of text fields into component parts and
the formatting of values into consistent layouts, based on industry standards,
local standards (for example, postal authority standards for address data),
user-defined business rules, and knowledge bases of values and patterns.
Generalized
"cleansing": The modification of data values to meet domain
restrictions, integrity constraints or other business rules that define when
the quality of data is sufficient for an organization.
Matching: The
identifying, linking or merging of related entries within or across sets of
data.
Monitoring: The
deployment of controls to ensure that data continues to conform to business
rules that define data quality for an organization.
Issue resolution and workflow: The identification,
quarantining, escalation and resolution of data quality issues through
processes and interfaces that enable collaboration with key roles, such as data
steward.
Enrichment: The
enhancement of the value of internally held data by appending related
attributes from external sources (for example, consumer demographic attributes
and geographic descriptors).
