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Quality-Driven Data Integration

Data has to be combined from disparate data sources in order to build a data integration layer. This increases the challenges to standardize and cleanse the data by many manifolds.

Kumaran’s approach is based on the Enterprise Data Management Architecture Framework. Here are some of the approaches we employ:

  • Data Quality Assessments Data quality bottlenecks are identified, by using our Data Quality Framework, and data quality profiles are created. This approach will help your organization asses the data integration risks upfront and flag it to the stakeholders. Data profiling strategies are created both at table and column level. This approach also helps in identifying redundant data.
  • Data Cleansing Rules Kumaran employs ETL-based customized data cleansing rules to clean the data. Based on individual client needs, we can also build customized pre-processor data cleansing routines.