OUR VISION

We Provide Best Data Quality Management Services

Data quality management provides a context-specific process for improving the fitness of data that’s used for analysis and decision making. The goal is to create insights into the health of that data using various processes and technologies on increasingly bigger and more complex data sets.

Predicting customer expectations
Assisting with effective product management
Providing organizations with competitor information

How To Improve?

Data Quality Dimensions

Uniqueness is the most addressed data quality dimension when it comes to customer master data. Customer master data are often marred by duplicates, meaning two or more database rows describing the same real-world entity.

Data Governance

A data governance framework must lay out the data policies and data standards that sets the bar for what data quality KPIs that is needed and which data elements that should be addressed.

Data Profiling

It is essential that the people who are appointed to be responsible for data quality and those who are tasked with preventing data quality issues and data cleaning have a deep understanding of the data at hand.

Data Matching

With company names the issues just piles up with funny mnemonics and inclusion of legal forms. When we place these persons and organizations at locations using a postal address the ways of writing that has numerous outcomes too.

Data Quality Reporting

The findings from data profiling can be used as input to measure data quality KPI’s based on the quality dimensions relevant to a given organization. The findings from data matching are especially useful for measuring data uniqueness.

Master Data Management (MDM)

The most, and the most difficult, data quality issues are related to master data as party master data (customer roles, supplier roles, employee roles and more). product master data and location master data.