Features

Business rule validation lets you authenticate data against standard statistical measures as well as customized business rules.
Key relationship analysis helps you discover hidden relationships in data across tables and databases as well as different source systems. Automatically identify orphaned records within a specific database or across databases.
Redundant data analysis identifies unnecessary information across tables or data sources. The engine supports both exact match and fuzzy match redundancy.
Data validation verifies that the data in your tables matches appropriate descriptions.
Statistical analysis establishes trends and commonalities in corporate information and examines numerical trends via mean, median, mode and standard deviation.
Data sampling – of full data or sample data from a source system – lets you analyze for data quality. Users can specify the sample interval.
Data profiling results can be exported to HTML, Microsoft Excel or text files, and users can write custom reports.

Monitor data to detect anomalies such as variances, values inside or outside of ranges, values that violate mathematical calculations, those that vary from historical values and more.
Design and enforce rules to determine if data is maintained and within proper control limits, and ensure that data meets predefined business rules.
Create data alerts and controls to verify that data remains in compliance with internal and external policies.
React to data problems quickly before inaccurate or invalid data has a negative effect.
Create customized business rules to validate and audit operational processes.

Unify data from different databases and source systems. Merge multiple files or duplicate records from a single file into one entity or record with industry-leading matching technology and innovative clustering logic.
Establish data hierarchies and reference data definitions to create a unified view of a particular data entity, such as customer or product.
Perform gender analysis using an advanced gender analysis algorithm.
Intelligently break multivalue fields into parsed elements such as address, city, first and last name, date, phone number, etc. Standardize data and eliminate duplications and inconsistencies by using advanced standardization (mapping) routines such as element, phrase and pattern standardization. Replace an original field with a new value or append a standardized value directly onto the source record. Support householding through sophisticated match keys that enable the engine to group records and assign an integer-based unique identifier. Use natural language parsing to separate values as required.

Identify individuals across multiple data sources even from incomplete and unobvious relationships.
Manage entity resolution routines through advanced fuzzy-matching technology.
Create multiple-record clusters, confidence scores and scatter plots to determine potential clusters.
Recognize when slight variations suggest a connection between records.

Connect to disparate enterprise data sources, including relational database management systems, XML files, spreadsheets and text files.
Extract metadata such as field name, data type and length.
Collect, catalog and organize metadata.
Use advanced matching algorithms to identify relationships and potential redundancies, and to compare field names, types and formats.
Identify and catalog relationships between databases' tables.

Data access capabilities enable users to connect disparate technology systems and streamline the transfer of business information to and from various technology resources.
ETL and ELT methods let you extract, transform and load data from multiple sources into a data warehouse using both traditional batch processing and in-database methods.
Document extraction techniques let users automatically access and integrate unstructured data tucked away in emails, old records and invoices, eliminating the need for manual processing along with associated delays and errors – so complete data can be processed quickly for analysis.
Data migration means you can transfer data to new or different locations while improving the data's accuracy and consistency during the migration project.
Data linking and matching allow you to match information within or across data sources, standardizing formatting differences.

Integrate the creation and management of master data resources with comprehensive data management practices.
Create a hub of master data based on a subset of your existing data, using a phased approach.
Combine MDM capabilities with matching, clustering and other data management initiatives.
Conduct batch processing with an architecture that supports many MDM implementations without unnecessary complexity.
Connect to MDM hubs as if they were any other data target.