Data quality management
Managing data quality is essential to ensure that the data used is accurate, reliable and complete. To add to the existing difficulty of managing data quality, data is constantly changing; with increasing volumes, shifting types, and various delivery methods. Without proper quality maintenance, data can become outdated and unusable.
Defining what success looks like
Before we can discuss solutions for properly managing data quality, we must first explore and define what success looks like in an organization:
Completeness – monitoring of data to ensure data needs account for missing or incomplete data
Timeliness – available at the proper frequency to enable timely decision making
Validity – compliance with requirements; data collected in the right format and of the right type
Relevance – relevant for intended purposes; proper feedback process and quality assurance
Reliability – consistent process of data collection; over time and in between systems
Accuracy – accurate enough for the intended purpose; balanced with cost, use, and effort
Auditable – changes to a set of data needs to be traceable; and transformation of data needs to be auditable
Replicability – data generation / making it possible for a data process to be carried out again, either by the same individual or another
Benefits of using iData
To get a full understanding of why data quality is important, you need to think of the many benefits that accurate, actionable data provides. All tasks are easier (including data quality management) once you find the right tool.
Introducing…iData – the right tool for data quality management.
iData is a lean user-friendly solution that drives business improvement through comprehensive data quality management. It delivers cleansing, validation, secure movement and monitoring of your data with total coverage and saving time.
Below are a few of the benefits of using iData:
Standardized data – ensures consistency across data (e.g. phone numbers, zip codes, email addresses, etc.)
Remove duplicates – prevents wasted time and unnecessary expenses by removing duplicates from your data
Data validation – ensures that data that is migrating from a legacy database to a new target database is transformed correctly and is in line with internal rules
Data monitoring – continuously monitors your data and highlights issues of new and existing data in real time
Data profiling – reviewing source data for content and quality
Generation / Obfuscation – copying and scrambling sensitive data (via encryption), as a means of concealment
Training 2,020 people in data quality in 2020
iData truly cares about data quality and has developed a FREE online training; with a goal to train at least 2,020 people in data quality in 2020. Training modules include over 2 hours of video content from leading experts in the industry. The following areas are covered in the training:
In addition to expanding your knowledge of various aspects of data quality, you will also receive a certificate of completion that demonstrates your commitment.
To learn more about iData contact: email@example.com
Author: Kate Strachnyi
Why is Data Quality important in the Healthcare Sector?
The healthcare sector has undergone huge digital transformation in recent years, with outdated systems being updated and upgraded to meet...Read more
Improve your personal data quality maturity with the DQ Primer from iDataQuality Academy!
There is no shortage of data quality maturity models like the one below, but these typically refer to organisations rather...Read more