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AI-Led Cognitive Data Quality

Businesses across the globe have embarked on a data-driven digital transformation to reinvigorate their business, with a key focus on democratizing analytics across their business operations. As part of this journey, most businesses have transformed their data platforms from EDW to Data Lakes/Data on Cloud. Others are in the mid of their transition journey of achieving scale and flexibility. But still, at the forefront of this transition lies the major challenge businesses face – the challenge around data quality. Poor quality of data in enterprise data platforms costs businesses not only in fines, manual rework to fix errors, inaccurate data for insights, failed initiatives and longer turnaround times, but also in lost business opportunities.

The traditional way of checking the quality of data involves a techno-functional team that reviews the data assets of an organization. The team writes a set of rules to identify flagged anomalies for review of data stewards, which are referred to as Data Quality rules. As these rules are static in nature, they become obsolete in 12- 24 months, and a new assessment is needed, which most of the times is difficult due to constant transition in the data system. In such a dynamic business environment, the need therefore is to augment the modernization of data management with AI-based data quality, thus achieving data semantics for delivering trusted business-critical data at the organization’s fingertips.

Analyst Speak

LTI recognized as a “Representative Vendor” in Gartner Market Guide for Data...

Analyst Speak

LTI featured in HfS Blueprint Report: Smart Analytics 2018

Key Highlights

LTI’s AI-led cognitive data quality tool helps businesses leapfrog in their data governance initiatives, with pre-built AI-led data qualities rules that help augment the overall process. The tool autonomously learns the data quality related rules to be applied on a particular dataset, by leveraging the power of cognitive algorithms. The key essentials of this tool include:

  • Auto discovery of business DQ rules
  • Autonomous data quality rules creation with quality assurance
  • AI-led autonomous matching rules creation between data sources
  • Autonomous data quality index calculation
  • Easy-to-use, with plug and play interface

Key Business Benefits

  • Significant reduction in unexpected data errors
  • 10x increase in DQ rules coverage
  • Effective compliance to key regulatory requirements
  • Increase in TAT for the key data & analytics model