Data Cleansing / Normalization

In today’s data driven society, ensuring accuracy of data is vital to staying competitive. Bad data can cause a myriad of problems resulting in revenue loss. Poor quality data cost businesses $ 600bn and up to 25% of their revenue every year.  In short a ‘Single View of Data’ which is critical for several decision making and reporting purposes has become the ‘Need of the Hour’. Scope’s data cleansing / normalization services ensure that a single version of data is available to users for accurate decision making.

As part of initial data quality audit, Scope will analyze the datasets available with customers to understand the type of data quality issues and data patterns in input. In this stage, Scope will analyze each field separately and document the data quality issues. Based on the data quality audit results, Scope will create a unique data cleansing methodology (customizing the rules and algorithms) for cleansing the data.

Scope uses a custom built DataMan to normalize information. Scope’s DataMan is used to normalize company data, contact person data, demographic information, social media content, email information, product data including attributes and UOMs (units of measurement), and metadata.

Scope’s DataMan contains the following modules:

  • De-duplication module (Exact and Partial Duplicates)
  • Standardization / Normalization module

data_cleansingThe de-duplication and standardization module work based on “exact matching” and “partial matching” algorithms. Scope’s data experts will customize algorithms based on data cleansing strategy adopted. The tool also refers to proprietary knowledge repositories to standardize company names, city, state, country and other information in a vendor master. Knowledge repositories are also available for product alternate names / synonyms

DataMan platform identifies partial duplicates / matching entries based on string matching algorithms. These string matching algorithms are programmed to ignore common company name extensions such as Ltd, Inc, LLC, and other such keywords.

All partial matching records will be reviewed by experienced data analysts. Our data analysts will also perform web research to validate partial duplicates. The self-learning algorithms in DataMan platform also learns from analyst inputs and automatically stores the rules for future automated processing.

Scope’s DataMan solution is inbuilt with a knowledge repository of company name alternatives / alias including Inc 5000 and Fortune 5000 companies. We are capable of handling data in multiple languages, including French, German, Chinese, etc.

processAn ISO 8000 certified master data quality organization, Scope employs ISO 8000 certified master data quality managers in leadership roles. Scope adopts a hybrid approach to address the data challenges/ needs of an organization. With service-based delivery model, there is no upfront investment in advanced tools/software required. By employing advanced technologies and knowledge repository for delivery of output, redundant issues are eliminated.

Service Differentiators:

  • ISO 8000 certified master data quality organization
  • ISO 8000 certified master data quality managers in leadership roles
  • Company Alias / Alternate names of Fortune 5000 and Inc 5000 companies
  • Product Lexicons of more than 220,000 products
  • Common company name extensions
  • World cities repository
  • World state and country repository
  • More than 100 fully trained team members across multiple domains