Data Quality Check & Cleansing Tool

Whether you move your data, consolidate it, migrate it or just get it cleansed - you need a clever Data Quality Check and Cleansing Tool.

Major capabilities

And our Data Quality Check & Cleansing Tool is clever in the ways you need it! Most importantly, in its ability to analyize data and create data quality reports, as well as cleanse/rectify found data issues

  • Overall quality measurements report providing short executive overview
  • Detailed reports on data issues, which include data source, exact placement of errors in data, rules violated, etc
  • Issues are compiled in groups by type of objects or business rules
    • Data Sources

    Due to its architecture the tool can be mapped to any data source - Oracle database, Text files, SQL Server 2000/2005, XML files, virtually any data source can be supported. We keep developing different data sources on demand if the existing ones cannot be re-used..

    Ease of use

    Our Data Quality Check & Cleansing tool is easy to use and fast to configure and deploy. It uses meta data on data source structure and data quality rules.

    Simple Architecture Sketch of How It All Works

    The Architecture of DQC tool As simple as is depicted, the tool generates quality reports based on the validation rules. Those rules are dictated by business specifics and customized accordingly.

    Performance

    We are proud of the performance we achieved. In terms of numbers, the tool processes around 1 mln source database records in 2 hours checking 2 rules of average complexity

    Customization

    The whole purpose of the tool is to make data quality checks easy. To define the data model - we configure the XML meta-model. To define validation rules - we again, configure XML meta-model. In rare cases when we need to introduce a complex rule we design, build and test a Java plugin. It takes little time to do both. It saves effort and time for us and our customers.

    Data Cleansing Capabilities

    The tool is capable of cleansing data based on the performed checks and identified severity (criticality) of found data issues. It's configurable to fix data issues in many ways, including updating incorrect values to correct ones, removing them or doing any other custom action.

    Built-in data validations
  • Unique value (E.g. "across all billing contacts, the phone number must not be duplicated")
  • Not empty value
  • Value is one of a set
  • Value must conform the mask (E.g. "US ZIP Code must be #####, where # - numbers")
  • Dependent values (E.g. column must have value A or B, if another column has value C)
  • Data entry must have counterparts (E.g. "customer must have at least one billing address"). Relation type can vary - 0..1, 1, 0..N, 1..N
  • Type check - numeric, date, etc
  • Precision check - numeric or string length restriction

    • Please Contact us for more information.

    Contact information

    System Integration Technologies

    E-mail

    Telephone: +1 (617)326-7667