20 Attributes of Data Quality – How Do You Fare?

bdna-blog-20-20-vision-data-quality

When it comes to data, a critical part of making sure you’re utilizing its full potential is to create accurate, quality data. But what exactly does quality data even mean to those who use it? That’s the conundrum Richard Y. Wang, director of the information quality program at the Massachusetts Institute of Technology, set out to answer. His research, which can be found here, develops a hierarchical framework that spotlights the important aspects of data quality.

Professor Wang used surveys to identify 118 data quality attributes identified by data consumers and consolidated the findings down to 20 data quality points, grouped into four categories. Wang did this to help information systems professionals better understand and meet their customers’ data quality needs. This subset got us thinking – how does BDNA fair when it comes to the 20 attributes of data quality?

BDNA’s 20-20 Vision

As a company, we went through all 20 attributes of data quality and looked at how our platform addresses each one. What we realized is that our vision is quite clear – we really care about data quality. Below are a few examples of how we address various attributes:

  • Accuracy: Data goes through an extensive verification process for accuracy and veracity before it makes it to Technopedia.
  • Relevancy: The data collected is based on years of experience working with IT leaders and organizations of all sizes.
  • Interpretability: Data is easy to understand and actionable. The BDNA platform filters out the irrelevant data and simplifies the interpretation and meaning of the data so you spend more time executing and less time trying to understand the data.
  • Flexibility: Normally inflexible IT data becomes easy to adapt and extend when it has a consistent representation and taxonomy alongside a context for that data. BDNA platform enables this.
  • Security: The BDNA platform has been designed with security in mind. It does not touch personally identifiable information (PIO). Any gaps in normalization are addressed using sanitized data from your environment.

Passing the Test

How does your company fare when it comes to the 20 attributes identified in the study? We’d love to hear your stories and how your company can improve upon data quality. Share our same vision? Learn more about how our solutions can help with data quality problems or contact us.