Chapter 3

Strategic Data Management in Organizations: Techniques, Principles, and Value
Opara, Dumo Nkesi Ph.D and Chukuigwe, Nwakaego Ph.D
Department of Office and Information Management, Department of
Management, Faculty of Business Studies, Ignatius Ajuru University of Education, Port Harcourt, Rivers State, Nigeria
Email: This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

Data quality depreciates when data is not managed persistently by the business. The ability for data systems to support business processes in the pursuit of business goals degrades over time when data quality degrades. This results in more cost but less benefit. Attempting to fix these problems at the operational level will not work. Adding more data and data systems in an effort to fix these problems only makes them worse. What will fix these problems is not more systems, more technology or more data, but data management. Data management advances the twin goals of data reusability and data quality - data that is timelier, more accurate, more complete, more accessible, more useful and less costly.

Proper data handling and management is crucial to the success and reproducibility of statistical analysis in every organization (Singh, 2009). Data management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles. The objective is to create a reliable database containing high-quality data. Strategic data management is seen a process to ensure data meets precise standards and business rules as it is entered into a system to ensure proper usage.
Strategic data management (SDM) is seen by Molly (2016) as >>>>>more

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