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The Data Quality Improvement Assessment

Data is arguably the most important asset to your organization. In an ideal world, data connects the technical and non-technical users in the business through common definitions and processes that can then be used to make critical business decisions. Unfortunately, very few companies are able to leverage their data in this way today.

Organizations are beginning to recognize the need for a centralized data management strategy, but oftentimes they aren’t sure how their data management initiatives fare today and how they could get started on the path to an optimized data strategy for the future.

 Our new Data Quality Improvement Assessment is a short quiz that will help you understand where your organization sits on our data quality sophistication curve, as well as share the steps you can take to improve the quality of your data.

 The Data Quality Improvement Assessment has four potential results: unaware, reactive, proactive, and optimized. 

  • Unaware – Organizations that are unaware have a siloed view and patchy understanding of how data quality is impacting their business. Data quality fixes sometimes happen, but the primary data management methods used are typically very manual or Excel-based.
  • Reactive – Reactive organizations have a good understanding of data quality challenges across the business, but no data-specific roles have been defined. Data quality fixes happen more frequently, but they are done in departmental silos. Excel-based or manual processes are still the main methods for data management, but some departments have more sophisticated data quality tools.
  • Proactive – Proactive organizations have a clear data quality process and defined ownership between business users and IT. Data quality metrics have also been clearly defined. The technical team has the ability to identify data quality issues and focuses on discovery and root cause analysis.
  • Optimized – Organizations who receive optimized results have a central owner of data, such as a chief data officer, in place who is responsible for corporate-wide data assets. Data quality is monitored as a standard part of business practices and there is a platform in place to profile, visualize, and monitor data over time. 

Understanding where you fall on the data quality sophistication curve will help you understand where to focus your future data quality initiatives and where you can improve. Are you ready to learn where your organization stacks up and what steps you should take to push your organization’s data quality to the next level? Take our Data Quality Improvement Assessment now! 

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