Data Quality

Framework Overview

The Blindata Data Quality module provides a minimal framework for actively monitoring data quality through the use of KQI.

The module enables a data quality management process which develops in the following steps:

  1. Definition and implementation of data quality rules and KQIs.
  2. Measurement of identified KQIs, thanks to the integration with various systems. Measurement values can be retrieved by querying systems in which data are stored or extracted from pre-computed or manually calculated indicators.
  3. Analysis of measurements through dashboards and aggregated indicators, such as synthetic scores and traffic lights attributed to each KQI on the basis of strategies and thresholds set by the user.
  4. Control and review of collected results as well as their trend over time, so that it is also possible to evaluate the effectiveness of any improvements and corrective measures taken.

Data Quality Management Process Diagram

The Blindata Data Quality module is designed to integrate with the Business Glossary and Data Catalog modules in order to obtain a unified view of the data. The KQIs collected in this way are shared within the organization and make the user of the data aware of which controls are active on that specific dataset.


For a better understanding of the document, some definitions are given.

Terms Definitions
Key Quality Indicators (KQIs) / Quality Checks Defining KQIs for data quality means establishing which are the fundamental metrics that impact the effectiveness of data use. These allow you to measure the quality dimensions in an objective manner and to examine them over time.
Quality Suite A quality suite is a collection of one or more logically related quality checks.
Probe A probe represents the implementation for the extraction of a metric of a KQI.
Project A project represents a collection of probes that are scheduled and executed all together.