Self-Optimizing Enterprise Analytics Ecosystem Using AI Agents, Governed Semantic Models, and Adaptive Cloud Intelligence

Abstract

Self-Optimizing Analytics Ecosystem Self-learning analytics systems provide a means to reduce the friction in the design, implementation, use, and continuous improvement of data-driven decision support capabilities. A self-optimizing analytics ecosystem comprises three core components: AI agents, governed semantic models, and continuous adaptation of cloud intelligence. Merging adaptive cloud intelligence with traditional concerns for governance and transparency opens new avenues for cognitive automation in enterprise analytics and decision making. The increasingly complex and dynamic characteristics of a connected enterprise demand continuous monitoring, optimization, and tuning of decision analytics and support using intelligent actors. An enterprise might realize this objective by merging adaptive cloud intelligence and the self-learning paradigm of cognitive computing in IT-enabled services. Two fundamental propositions arise: first, that the continuous adaptation of cloud intelligence can be accelerated through the use of implicit feedback (by-products) and explicit ratings by and on different stakeholders; second, that these feedback loops (data) and the complex services are underpinned by transparent and publicly available semantic models. The development of these propositions supports the broader theme of a self-optimizing analytics ecosystem.

The continuous adaptation of cloud-based analytics can be realized through the use of implicit feedback supplied data and explicit stakeholder ratings. However, while self-learning models and services provide an effective means to reduce friction in the optimization of analytics-based decisions, concerns relating to governance and trust must not be overlooked. The presence of reliable devices, instruments, and support systems for quality control, governance, and auditability fulfill the requirements of trust and transparency. The explicit adoption of a formal governance framework, including business rules and policies, establishes the relevant compliance requirements governing specific incidents of business use. The fulfillment of these needs can accelerate the development of cognitive automation. In particular, self-learning EXPLAINER agents deployed during service consumption within implicit ratings address the data quality attributes of completeness and validity.