Implementing Data Strategy: Design Considerations and Reference Architecture for Data-Enabled Value Creation
Balakrishnan, R., Das, S., & Chattopadhyay, M. (2020). Implementing Data Strategy: Design Considerations and Reference Architecture for Data-Enabled Value Creation. Australasian Journal of Information Systems, 24. https://doi.org/10.3127/ajis.v24i0.2541
With the arrival of Big Data, organizations have started building data-enabled customer value propositions to increase monetizing and cost-saving opportunities. Organizations have to implement a set of guidelines, procedures, and processes to manage, process and transform data that could be leveraged for value creation. This study has approached the journey of an organization towards data-enabled value creation through four levels of data processing, such as data extraction, data transformation, value creation, and value delivery. This study has critical inferences on using data management solutions such as RDBMS, NoSQL, NewSQL, Big Data and real-time reporting tools to support transactional data in internal systems, and other types of data in external systems such as Social Media. The outcome of this study is a methodological technology independent data management framework an organization could use when building a strategy around data. This study provides guidelines for defining an enterprise-wide data management solution, helping both the academicians and practitioners.
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