Data quality

Quest For Data Quality

Pursuing data quality requires consideration of big data, data tools and resources, error correction and getting various interests to work together. Recent Inside Reference Data stories explore all these factors.

Choosing Tools and Setting Models

Efforts to raise data quality require both coordination of data processes and resources, and clearer definition of expectations in data modeling and contracting with service providers. Michael Shashoua reports on data managers' insights about how to…

Quality Time for Counterparty Data

A recent report by an international group of market supervisors has expressed concern about firms’ ability to report on their counterparties. Why does this area of data management remain so challenging and what should firms do to a get a handle on it,…

Quality's Matrix

Data quality improvement efforts—even those aimed at making the process simpler—are proving to be a complex mix involving different actions for different types of data, regulations to contend with, and management issues to be grasped. Michael Shashoua…

Updating the data quality toolbox -- Webcast

Inside Reference Data gathered leading industry experts for a webcast on December 17, 2013 to discuss what financial trading firms are doing to improve data quality and the measures they are taking in light of increased reporting rules and regulation.

Shake To Shuffle

The data management industry may be reshuffling the description of its most prevalent themes, as well as their importance, but a lot of the same basics remain in its infrastructure

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