Data Normalization

Data normalization helps you protect the integrity of your data against logical or structural inconsistencies. Normalized data increases the stability of the data structure, takes away the risk of discrepancies between different versions of the same information, along with decreasing the data storage space, by minimizing the amount of data redundancies.

At SunTec India, with our data normalization services, we support you in keeping your data accurate and relevant for effective business transaction and analysis. We clean, format, standardize, verify and validate data to ensure correct alignment and definitions.

Equipped with database experts, domain professionals, and well-formulated data cleansing methodologies, SunTec India has demonstrated capabilities in normalizing data from diverse legacy sources. We ensure that we accurately assign the noun-modifiers, attributes and add right attribute values.

SunTec India offers a complete spectrum of data normalization services including rationalization of data, de-duplication of parts, extracting attributes and capturing value, expanding abbreviations, and ensuring overall quality control. Our well-established procedures can be adapted to the specific requirements of customers to normalize the key fields in your data. It includes:

  • Name and name components, such as prefix, suffix, epithet, honorary title, etc.
  • Organization along with division, wing, area, etc.
  • Job titles
  • Job functions/ grouping of job function
  • Normalization of supplier and manufacturer data
Discuss Your Project With Us
SunTec aims towards building a long term relationship work relationship with our clients and practices collaborative work culture. To learn how we can help you normalize your data, assigning the items in the right fields, consistent labels, along with standardizing and formatting, please get in touch with us.

Related Resources

Read the case study to learn how SunTec helped its client append nearly 1, 80,000 customer records and deliver uncompromising data quality.

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