TY - CHAP
T1 - Data Quality Assessment
AU - Jansen, Bernard J.
AU - Aldous, Kholoud K.
AU - Salminen, Joni
AU - Almerekhi, Hind
AU - Jung, Soon gyo
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - This chapter explores data quality assessment in data analytics. Emphasis is placed on the importance of ensuring you have high-quality data for effective decision making and successful outcomes in data analytics. Various aspects of data quality, such as completeness, consistency, validity, accuracy, and timeliness, are examined, along with the methods and tools used to assess data quality, including profiling, cleansing, validation, governance, and auditing. The challenges organizations face in conducting data quality assessments, such as the scale and complexity of data, missing data, limited resources, and integrating multiple sources, are also discussed.
AB - This chapter explores data quality assessment in data analytics. Emphasis is placed on the importance of ensuring you have high-quality data for effective decision making and successful outcomes in data analytics. Various aspects of data quality, such as completeness, consistency, validity, accuracy, and timeliness, are examined, along with the methods and tools used to assess data quality, including profiling, cleansing, validation, governance, and auditing. The challenges organizations face in conducting data quality assessments, such as the scale and complexity of data, missing data, limited resources, and integrating multiple sources, are also discussed.
UR - http://www.scopus.com/inward/record.url?scp=85171991146&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-41933-1_5
DO - 10.1007/978-3-031-41933-1_5
M3 - Chapter
AN - SCOPUS:85171991146
T3 - Synthesis Lectures on Information Concepts, Retrieval, and Services
SP - 55
EP - 64
BT - Synthesis Lectures on Information Concepts, Retrieval, and Services
PB - Springer Nature
ER -