DATA AS A PRODUCT: ENABLING SELF-SERVE CAPABILITIES IN MODERN ORGANIZATIONS
Keywords:
Data As A Product, Self-Serve Analytics, Data Governance, Data Democratization, Data Product ManagementAbstract
This article explores the concept of "data as a product" and its role in enabling self-serve capabilities within organizations. It examines this approach's theoretical framework, including its definition, the evolution of data management practices, and the context of self-serve data access in modern enterprises. The article delves into key components such as data product teams, data catalogs, governance frameworks, and user-friendly interfaces that facilitate this paradigm shift. It discusses how self-serve capabilities empower non-technical users and reduce dependency on IT teams supported by various tools and technologies. The benefits of this approach, including increased organizational agility, improved scalability, enhanced innovation potential, and faster decision-making processes, are analyzed alongside implementation challenges, such as cultural shifts and quality assurance concerns. The article also considers future directions in data product management and its potential impact on organizational structures and roles. By synthesizing current research and industry practices, this paper provides a comprehensive overview of the data as a product approach, offering insights into its transformative potential for data-driven organizations.
References
T. Davenport and R. Bean, " Developing Successful Data Products at Regions Bank, vol. 63, no. 2, pp. 1-4, 2022. [Online]. Available: https://sloanreview.mit.edu/article/developing-successful-data-products-at-regions-bank/
V. Khatri and C. V. Brown, "Designing Data Governance," Communications of the ACM, vol. 53, no. 1, pp. 148-152, 2010. [Online]. Available: https://dl.acm.org/doi/10.1145/1629175.1629210
Self-Service Analytics Implementation Strategies for Empowering Data Analysts”, IJMLAI, vol. 4, no. 4, pp. 1–14, Dec. 2023, Accessed: Oct. 04, 2024. [Online]. Available: https://jmlai.in/index.php/ijmlai/article/view/34
A. Saltz and K. Suthrland, "SKI: A New Agile Framework for Data Science Projects," in Proc. IEEE Int. Conf. Big Data (Big Data), 2019, pp. 2341-2350. [Online]. Available: https://ieeexplore.ieee.org/document/9005591
Yan Zhao, Franck Ravat, Julien Aligon, Chantal Soule-dupuy, Gabriel Ferrettini, and Imen Megdiche. 2021. Analysis-oriented Metadata for Data Lakes. In Proceedings of the 25th International Database Engineering & Applications Symposium (IDEAS '21). Association for Computing Machinery, New York, NY, USA, 194–203. https://doi.org/10.1145/3472163.3472273
M. Comuzzi and A. Patel, "How organisations leverage Big Data: A maturity model," Industrial Management & Data Systems, vol. 116, no. 8, pp. 1468-1492, 2016. [Online]. Available: https://www.emerald.com/insight/content/doi/10.1108/IMDS-12-2015-0495/full/html
D. Delen and H. Demirkan, "Data, information and analytics as services," Decision Support Systems, vol. 55, no. 1, pp. 359-363, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0167923612001558
Rene Abraham, Johannes Schneider, Jan vom Brocke, Data governance: A conceptual framework, structured review, and research agenda, International Journal of Information Management, Volume 49, 2019, Pages 424-438, ISSN 0268-4012, https://doi.org/10.1016/j.ijinfomgt.2019.07.008
C. Batini and M. Scannapieco, "Data Quality Dimensions," in Data and Information Quality, Cham: Springer International Publishing, 2016, pp. 21-51. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-24106-7_2
