Self-Serve Analytics: Enabling Business Users with AI-Driven Insights
Keywords:
self-serve analytics,, data democratization,, AI-driven insights, data visualizationAbstract
Self-serve analytics has become a transformative model in modern business intelligence. Non-technical users get actionable insights from complex datasets without extensive dependence on data science teams. This paper examines the core principle of data democratisation and the role of AI driven analytics in facilitating intuitive, real-time decision-making. Snowflake, Tableau, and Looker are advanced analytical platforms have the capability to integrate artificial intelligence which can enhance data accessibility, visualization, and interpretability.
Downloads
References
S. Chaudhuri, U. Dayal, and V. Narasayya, “An overview of business intelligence
technology,” Commun. ACM, vol. 54, no. 8, pp. 88–98, Aug. 2011.
H. Chen, R. H. Chiang, and V. C. Storey, “Business intelligence and analytics: From
big data to big impact,” MIS Q., vol. 36, no. 4, pp. 1165–1188, Dec. 2012.
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best
Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-
S. Kumari, "Agile Cloud Transformation in Enterprise Systems: Integrating AI for
Continuous Improvement, Risk Management, and Scalability", Australian Journal of
Machine Learning Research & Applications, vol. 2, no. 1, pp. 416-440, Mar. 2022
S. Kumari, "AI-Enhanced Agile Development for Digital Product Management:
Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation",
Adv. in Deep Learning Techniques, vol. 2, no. 1, pp. 49-68, Mar. 2022
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure
and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021):
-187.
S. Kumari, "AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging
Machine Learning to Automate Threat Detection, Vulnerability Management, and
Incident Response", J. of Art. Int. Research, vol. 2, no. 1, pp. 286-305, Apr. 2022
H. Watson and B. Wixom, “The current state of business intelligence,” Computer, vol.
, no. 9, pp. 96–99, Sep. 2007.
D. J. Power, “Decision support systems: Concepts and resources for managers,”
Westport, CT: Greenwood Publishing Group, 2002.
ystems and Pattern Recognition
Vol. 2 - 2022
T. Davenport, “Competing on analytics,” Harvard Bus. Rev., vol. 84, no. 1, pp. 98–107,
Jan. 2006.
P. Russom, “Self-service business intelligence: The next generation of business
analytics,” TDWI Research, Renton, WA, USA, Rep. 2012.
A. L. Porter, J. D. Roessner, and X. Jin, “Forecasting and management of technology,”
Technol. Forecast. Soc. Change, vol. 80, no. 2, pp. 228–243, Feb. 2013.
Y. Demchenko, C. de Laat, and P. Membrey, “Defining architecture components of the
Big Data Ecosystem,” in Proc. IEEE Int. Conf. Collaboration Technol. Syst. (CTS), Denver,
CO, USA, 2014, pp. 104–112.
J. Seddon, “Measuring and managing performance in organizations,” Int. J. Prod. Econ.,
vol. 34, no. 3, pp. 223–235, Mar. 1994.
R. Kimball and M. Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional
Modeling, 3rd ed. Indianapolis, IN, USA: Wiley, 2013.
M. Chau and H. Chen, “A machine learning approach to web page filtering using
content and structure analysis,” Decis. Support Syst., vol. 44, no. 2, pp. 482–494, Jan.
L. A. Zadeh, “Fuzzy logic, neural networks, and soft computing,” Commun. ACM, vol.
, no. 3, pp. 77–84, Mar. 1994
