Data Quality

Introduction to Data Quality Having a faculty activity reporting solution is important, but it’s even more important to ensure that the data which resides within the database is rich and accurate, so that your users have confidence in the reports generated—from the institution level, all the way down to a specific individual. In fact, achieving a high level of data qual...

Joni Boone, Faculty Developer, Purdue University GlobalLiz Fitzgerald, Faculty Developer, Purdue University Global Purdue University Global’s Center for Teaching and Learning offers help with reporting and data imports to save time and encourage participation in professional development events. Leave this session with techniques to easily prepare reports, from start to...

Data accuracy is one of the four components of data quality and sometimes gets the reputation of being one of the most difficult attributes to measure. It refers to whether the data values stored for an object are the correct values. If there is wrong data in the system, negative effects flows into reports. It’s the old mantra, “garbage in, garbage out.” Two important r...

To begin improving your Completeness score, remember that this data quality dimension is comprised of three metrics, listed below. Each metric can be evaluated and addressed to improve your overall Completeness score. Median records per user per year for “core screens” Percent of "core fields" populated in records for "core screens" Percent of dated records for "core s...

To begin improving your Consistency score, remember that this data quality dimension is measured using the following metric: Percent of records for “core screens” that use "Type" and "Description" fields as intended. For this metric, we evaluated the use of two types of fields: "Type" fields, which are predefined drop-down lists used for categorization "Description", "...

To begin improving your Currency score, remember that this data quality dimension is measured using the following metric: Median records per user for core screens. This metric is measured the same way as the first Completeness metric, but we use a different time frame because the goal here is to gauge whether your data can provide real-time answers to questions. You can...

Although Faculty Success is unable to measure for data quality accuracy, it’s important to have a process in place to regularly monitor and cleanse the records that exist in the system. To begin improving your accuracy, remember that the best way to spot flaws in data accuracy is to have a general reliance on the reports in Faculty Success. The more users there are usin...