Bridging Technical Telemetry and Business Decision-Making through Data Product Management

Sushrutha Sreevathsa

Citation: Sushrutha Sreevathsa, "Bridging Technical Telemetry and Business Decision-Making through Data Product Management", Universal Library of Innovative Research and Studies, Volume 03, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Enterprise leaders rely on telemetry from digital platforms, yet operational metrics, incident records, logs, traces, and service signals often remain detached from managerial work. Technical teams use these signals for diagnosis, while executives need evidence that explains customer impact, operational risk, cost exposure, and prioritization trade-offs. The article examines how data product management turns telemetry into trusted decision input for enterprise operations. The study develops an analytical model that connects observability signals, shared metric definitions, governance controls, ownership, and feedback routines. The method combines qualitative source analysis, comparative review, typologization, and conceptual synthesis of ten recent academic and industry sources on data products, observability, Business Intelligence and Analytics (BI&A) success, data governance, analytics capability, and Connected Intelligence. The analytical section identifies three results: telemetry needs product boundaries before it enters management routines, trust depends on semantic discipline and ownership, and decision value increases when feedback links business outcomes with system behavior. The proposed model supports Monthly Business Reviews, SRE analytics, operational intelligence programs, and enterprise data functions.


Keywords: Data Product Management, Technical Telemetry, Operational Intelligence, Observability, Business Intelligence, Decision-Making, Site Reliability Engineering (SRE) Analytics, Data Governance, Metric Standardization, Connected Intelligence.

Download doi https://doi.org/10.70315/uloap.ulirs.2026.0303006