Informatica, IBM, Trillium Software, SAS & SAP are in the leaders quadrant for “Gartner Magic Quadrant for Data Quality Tools : 2014”
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).