"A data model is a formal organized representation of real-world entities, focused on the definition of an object and its associated attributes and relationships between the entities. Data models should be designed consistently and coherently. They should not only meet requirements, but should also enable data consumers to better understand the data." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"Bad data are expensive: my best estimate is that it costs a typical company 20% of revenue. Worse, they dilute trust - who would trust an exciting new insight if it is based on poor data! And worse still, sometimes bad data are simply dangerous; look at the damage brought on by the financial crisis, which had its roots in bad data." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"Conformity, or validity, means the data comply with a set of internal or external standards or guidelines or standard data definitions, including metadata definitions. Comparison between the data items and metadata enables measuring the degree of conformity." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"Data-intensive projects generally involve at least one person who understands all the nuances of the application, process, and source and target data. These are the people who also know about all the abnormalities in the data and the workarounds to deal with them, and are the experts. This is especially true in the case of legacy systems that store and use data in a manner it should not be used. The knowledge is not documented anywhere and is usually inside the minds of the people. When the experts leave, with no one having a true understanding of the data, the data are not used properly and everything goes haywire." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"Data are collections of facts, such as numbers, words, measurements, observations, or descriptions of real-world objects, phenomena, or events and their attributes. Data are qualitative when they contain descriptive information, and quantitative when they contain numerical information." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"Data migration generally involves the transfer of data from an existing data source to a new database or to a new schema within the same database. [...] Data migration projects deal with the migration of data from one data structure to another data structure, or data transformed from one platform to another platform with modified data structure." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"Data quality is the capability of data to satisfy the stated business, system, and technical requirements of an enterprise. Data quality is an insight into or an evaluation of data’s fitness to serve their purpose in a given context. Data quality is accomplished when a business uses data that are complete, relevant, and timely. The general definition of data quality is 'fitness for use', or more specifically, to what extent some data successfully serve the purposes of the user." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"Lack of a standard process to address business requirements and business process improvements, poorly designed and implemented business processes that result in lack of training, coaching, and communication in the use of the process, and unclear definition of subprocess or process ownership, roles, and responsibilities have an adverse impact on data quality." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"The degree of data quality excellence that should be attained and sustained is driven by the criticality of the data, the business need and the cost and time to achieve the defined degree of data quality." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"To understand why data quality is important, we need to understand the categorization of data, the current quality of data and how is it different from the quality of manufacturing processes, the business impact of bad data and cost of poor data quality, and possible causes of data quality issues." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
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