Data Sleuth
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Data Sleuth Framework for Asset Management

Financial firms often fail to capture and incorporate accurately semi-structured data into manual QA processes and operations, which can severely impede or compromise product quality. NIIT Technologies’ DataSleuth® solution addresses the QA, accuracy, and efficiency needs of financial services firms. It allows for the easy creation of “data difference” processes to compare both value and text data regarding specific data elements to allow for a comparison of one document or file and a prior version.

The DataSleuth® framework enables more cost-effective, error-free, and automated QA functionality by effectively capturing, storing, and utilizing structured and semi-structured data. These include publicly available Internet data files and the use of multiple data feeds, including company-generated reports (in PDF format), Excel files, internal and/or external data files, and much more.

Key Features

  • Extracts limited portions of documents and files to load data and store it in a database
  • Automates processes to capture and utilize semi-structured data
  • Automates comparisons of report and file contents between differing versions
  • Enables business users to easily set tolerance limits for changes in the data values of compared documents and/or data files
  • Identifies changes in text fields
  • Offers intuitive ‘drill-down dashboards’ for exception identification, review, and management
  • Streamlines automation and processes for exception overrides
  • Accessible from all major mobility platforms
  • Automates polling of internal and external data sources to capture data changes between versions to reduce timeliness issues facing your operation
  • Provides application support

The NIIT Technologies Advantage

  • Familiar platform, user-friendly tools, self-service BI for business users, and compelling visualization support
  • 50%–70% savings of efforts toward verification and re-verification
  • 100% coverage, as opposed to a manual verification process
  • Lower TCO and higher scalability given the intelligent Python-based framework
  • Reduced IT costs and quick response times
  • Flexible solution that can be quickly deployed, allowing LoB users to be parameterized based on changing needs and regulations

 

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