Master Data Management Platform
Overview
Data Quality Management
High-quality data are the precondition for analyzing and for guaranteeing the value of the data. Poor data quality prevents the analysis of data for decisions which are critical for business. It also has a negative impact on business processes.
Data Quality Measurement Services refer to the assessment of information collected relative to its purpose and its ability to serve that purpose. By measuring outcomes, issues within the system are identified, and evidence-based processes and rules are used to develop or alter processes to improve the data quality.
Our Data Quality Management Services focus on data quality dimensions such as accuracy, consistency, timeliness, uniqueness, completeness, and validity to determine the data quality. Data quality dimensions capture the attributes that are specific to your context.
Data cleansing is a way to remove duplicate records and nonstandard data. It enforces the right standardizations for gaining insights into datasets. We also go through data profiling to verify data and establish trends. Our rules & validation processes enable you to manage low-quality data.
The objective of quality management is to ensure that a particular product or service will consistently fulfill its intended purpose. Good data quality increases the accuracy of analytics applications, leading to better business decision-making that boosts sales and improves internal processes.
Key Features
Timely notifications to users/user groups
Data masking & Data privacy features helps to keep the information secure
Workflow enabled data cleansing process
Scorecards helps to track the progress
Benefits
High Quality data
Improves operations efficiency
Predict business outcomes
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