Data Quality Management

Data Quality Management

May 18, 2020 Data Science 0

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 Without proper quality maintenance, data can become outdated and unusable.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:

  • Streamlined database – prepares and enhances your data to remove all waste; to have it ready for use or for migration
  • 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:

  • Importance of data quality
  • Data profiling
  • Data preparation
  • Data transformation and assurance
  • Data quality impact on AI and Machine Learning
  • Data obfuscation
  • The cost of poor data quality
  • Relationship between data quality & data visualization
  • Selling data quality to your business

In addition to expanding your knowledge of various aspects of data quality, you will also receive a certificate of completion that demonstrates your commitment.

Click here to take advantage of this opportunity.

To learn more about iData check out their website:

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