Data Quality

Is your data full of hidden treasure – or hidden costs?

The iWay Master Data Managment Suite's data quality capabilities empower today’s organizations to make their data better, so they can tap into it more readily and use it more strategically.

Today's integration strategies require much more than just unifying diverse information assets. Disparate applications, databases, systems, messages, and documents need to be seamlessly brought together – and the information and content within them must also be accurate, consistent, and complete.

The iWay Master Data Management Suite includes powerful real-time and online data quality tools that allow companies to optimize their enterprise information by facilitating broad-reaching data quality management. Organizations can achieve and maintain true data integrity across all information assets by:

  • Profiling and analyzing data to assess its quality and immediately identify and correct any issues that may negatively impact its accuracy
  • Dynamically cleansing data via an automated rules engine. Restrictions, constraints, and other business rules and criteria for data quality can be defined and implemented, then dynamically applied to data values across the information landscape. Changes and modifications to existing data will then be automatically made based on those definitions to eliminate mistakes
  • Enriching information with content obtained from external and third-party sources. Existing data attributes can then be automatically appended to incorporate any appropriate new content found
  • Merging and matching related information across multiple data sets to promote consistency throughout the business
  • Implementing a real-time data quality firewall

These powerful and flexible features enable users to validate and handle data quality in many ways, and in real time – even as users are interacting with information online and new data is being generated during the course of transactions and events. This type of real-time data quality management allows companies to proactively keep bad data out of their environment. Data quality can also be managed in-batch at pre-defined intervals to effectively eliminate preexisting errors and inaccuracies.