Data quality goals

Caroline Visentin

Chapter 37 in Elgar Encyclopedia of Life Cycle Sustainability Assessment, 2026, pp 125-128 from Edward Elgar Publishing

Abstract: Data quality goals are essential in ensuring that data is accurate, complete, and consistent. Defining data quality goals can help to identify areas where improvements are needed. Data quality goals should be specific, measurable, achievable, relevant, and time-bound (SMART). Data quality goals are crucial in various industries such as healthcare, finance, and education. Without clear goals, it is impossible to measure the success of any data quality initiative. The SMART framework is a popular tool used to set and achieve goals. In the context of life cycle sustainability assessment (LCSA), setting effective data quality goals is crucial to ensure accurate and reliable data. The aim of this entry is to describe the data quality goals, their benefits, the SMART framework and also relate the data quality goals to the LCSA.

Keywords: Data Quality; SMART Framework; Life Cycle Sustainability Assessment (LCSA); Sustainability; Data Completeness; Data Accuracy (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035309887
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