Understand the quality of data


assesses data quality, quantity, composition, anomalies, similarities, and patterns. It is instrumental in the data discovery phase in understanding the facets of data and preparation of data for ingestion. Such qualification of data then can be used not only to correct and repair the data, but also as an essential step in establishing transformation rules for application migration and integration.

The eQ Approach

Using eQube-DP, eQ delivers an alternative to manual and query-based analysis. Using a business rules engine with advanced semantic mapping and logic, eQube-DP interrogates single or multiple complex data sources to deliver an initial data quality score, presenting the types of data issues in user friendly interactive reports, classifying the issues by various means such as duplication, incorrect formats, nulls, typing errors etc. These and many more methods can be configured using rules to highlight the data quality challenges. Using a method known as snapshots, we can capture the data quality state at any point in time, lock the results and provide comparison of states over a period of time.

eQ's approach to migration or integration solutions revolves around its 'IETLV' philosophy. IETLV stands for Identification, Extraction, Transformation, Load, and Validation. In any migration project, understanding the quality or facets of data in source systems is critical to help define the transformation rules and object model of the target system. eQube-DP delivers this initial Data Discovery and Data quality assessment (or 'I' of the IETLV methodology).
IETLV approach for data migration -Identification, Extraction, Transformation, Load, and Validation

eQube-DP Use Cases:

eQube-DP can be used to address the following use cases:

  • Legacy data clean up pre-migration
  • To de risk Migration and Integration projects by driving the requirements, configuration of the target system and business decisions required for successful deployment
  • Automatically Repair Data and provide real time data quality health monitoring
  • To compare multiple data sources, BOM's, attributes and data directly via standard connectors on a real time or scheduled basis
  • Data quality trend analysis
  • Master Data Management to control, track and audit changes to data sets and provide capability to merge data sets

Automated Data Repair & Health Monitoring Process

Using eQube-DP, a solution can be built that can assess the data quality, facilitate automatic data repair, and continuously monitor the health of data quality. The steps are as follows:

  • Aggregate & analyze multiple data sets by applying validation rules
  • Report on data issues, anomalies found or data set comparisons(eQube-BI)
  • Make changes in-memory with automated business approval process to undertake repairs
  • Once approvals are in place, update source systems based upon defined business logic and rules (using the business process modeling capability in eQube-MI)

The following figure depicts the concept.

Automated Data Repair & Health Monitoring flow using eQube Platform

Our solutions