Data Transformation
and Enrichment Services

Profiling gives us an idea of the data you are dealing with. Individual entries and broader patterns are examined to identify quality issues and decide the data transformation methods.

Automatic checks are done at the point of entry and/or after the data is ingested into the system to identify inconsistencies, such as conflicting data, incongruous data types, etc.

De-duplication, data standardization, data imputation, capping outliers, etc., are some of the methods used to cleanse and reduce errors and inconsistencies in data.

Sensitive data such as Personally Identifiable Information (PII) is anonymized using various data masking techniques to ensure privacy protection and regulatory compliance.

Depending on the machine learning problem, data features are extracted, transformed, or newly created to capture underlying patterns and enhance model performance.

Data from different sources are integrated after merging duplicate records, standardizing the formats, etc., to create datasets that are more coherent and usable.

Data is enhanced with additional information about its source, type, ownership, update frequency, intended use, etc., to improve its discoverability and usefulness.

Existing data is enriched with data from external, third-party sources. Adding new information or context enhances the quality, completeness, and relevance of your data.

For targeted planning and intervention, data is divided based on attributes such as demographics, purchase history, date range, geographic locations, etc.

Entities such as names, products, locations, etc., are extracted from unstructured data and categorized into classes to enhance search and analysis.

New data fields are created to analyze specific metrics, such as total sales, customer lifetime value, etc. This data can be appended to segmented datasets for richer analysis.

Location-based information is added to data to help localize marketing, personalize user experience, and obtain insights on specific regions.