Data Quality Platform

With the onset of the digital era, all businesses are harnessing data and information from their own systems, and using them for critical business decisions and process definitions.

Enterprise Data has now come to be classified as a business asset, with management processes dedicated to the capture and management of enterprise data to generate meaningful business information and insights.
However, 46% of industry leaders cite enterprise data quality as a barrier for adopting business intelligence or Analytics products that are critical to deriving information from data. Enterprise data quality issues plague not only at a corporate business decision-making level, but also at the level of projects undertaken by organisations.

On one hand, poor data or lack of visibility into data quality owing to lack of data completeness, data consistency or data accuracy negatively affects projects overruns, costs and delays; and on the other, data quality best practices at a corporate level boosts revenue by 66%, and improves predictability at a project level.

Our Services

Maveric’s data quality platform is an evolving framework built with the intention of detecting and managing data quality and data accuracy parameters. Our platform adopts an incremental and iterative approach to enhancement of enterprise data quality.

The platform addresses 6 stages of data quality management.

 Stages of Data Quality Management

Profile: The quality of data is profiled against 6 metrics mentioned below:

  • Data Completeness: Are all the requisite details available at field level (e.g., missing data) and/or data level (e.g., missing line item for an order)?
  • Data Conformity: Do data values conform to the required formats as defined by domain values of that field and/or checking data consistency with other similar attributes?
  • Data Consistency: Are data values in one data set consistent with the same values in another data set?
  • Data Accuracy: Does data represent its intended purpose/requirement or is it rendered inadequate due to typos, acronym usage and/or untimely arrival of data?
  • Data Duplication: Is data maintained at a single source?
Cleanup: The data is cleaned up either using master reference data or through bespoke scripts to fix instances of specific errors. This data fix is based on an incremental knowledge framework.
Standardise: The data rules are standardised using geo-coding and matching/linking rules to ensure that same errors do not re-occur in future data feeds.
Monitor: A monitoring system is established for business rules and data patterns for continuous quality monitoring for the enterprise data.
Fix: Syntactic issues are automatically fixed for 22 common data issues that address 75% of all data issues. An alert system is also created to enable manual data fixes where automatic fixes are not possible or there is a need to manually confirm a data fix.
Report: Reports are provided for better understanding and progress of the data quality using online dashboards and email.

Maveric’s data quality platform is built to integrate with the banking domain model.
Syntactic, semantic and domain-based quality validations are possible using our data quality platform.