Beyond Dashboards: Turning Production Signals into Reliable Decisions
Abstract
Production systems generate enormous volumes of alerts, incidents, service metrics and business signals, and more data does not by itself produce better decisions. Drawing on experience building operational intelligence and leadership-facing analytics at Yahoo, this paper describes moving from fragmented reporting and metric reconciliation toward a Connected Intelligence model linking trusted data, shared definitions, operational context, governance and action. The emphasis on shared definitions is not incidental: much of the effort in operational analytics is spent reconciling numbers that disagree because the teams producing them mean different things, and no dashboard resolves that. The paper also covers the tradeoffs, adoption difficulties and lessons from building a system that was actually used in operational reviews rather than merely delivered. Readers take away an understanding of why dashboards and AI-generated insights so often fail to change what anyone does, and a framework connecting production signals, trusted definitions, business context, ownership and feedback loops.
- Published in
- Proceedings of the Raptors Conference, Vol. 1 (2026)
- Conference
- Raptors Conference 2026, 21 October 2026, online
- Accepted
- 2026-09-25
- DOI
- https://doi.org/10.68495/rc.2026.C087
- Presentation
- Slides (PDF)
- Licence
- © 2026Sushrutha Sreevathsa. Published under Creative Commons Attribution 4.0 International (CC BY 4.0).
- Publisher
- Hackathon Raptors, Community Interest Company 15557917, London, United Kingdom
- Permanent address
- https://proceedings.raptors.dev/sreevathsa-beyond-dashboards-turning-production
Open access. Every entry in the Proceedings is free to read, with no charge to authors and no charge to readers. Slides are served from this site, so they stay available at the paper's permanent address.
Cite this paper
Sushrutha Sreevathsa (2026). Beyond Dashboards: Turning Production Signals into Reliable Decisions. Proceedings of the Raptors Conference, Vol. 1 (2026). Hackathon Raptors, London. https://doi.org/10.68495/rc.2026.C087
@inproceedings{raptors2026-c087,
title = {{Beyond Dashboards: Turning Production Signals into Reliable Decisions}},
author = {Sreevathsa, Sushrutha},
booktitle = {Proceedings of the Raptors Conference},
volume = {1},
year = {2026},
publisher = {Hackathon Raptors},
address = {London, United Kingdom},
doi = {10.68495/rc.2026.C087},
url = {https://doi.org/10.68495/rc.2026.C087},
abstract = {Production systems generate enormous volumes of alerts, incidents, service metrics and business signals, and more data does not by itself produce better decisions. Drawing on experience building operational intelligence and leadership-facing analytics at Yahoo, this paper describes moving from fragmented reporting and metric reconciliation toward a Connected Intelligence model linking trusted data, shared definitions, operational context, governance and action. The emphasis on shared definitions is not incidental: much of the effort in operational analytics is spent reconciling numbers that disagree because the teams producing them mean different things, and no dashboard resolves that. The paper also covers the tradeoffs, adoption difficulties and lessons from building a system that was actually used in operational reviews rather than merely delivered. Readers take away an understanding of why dashboards and AI-generated insights so often fail to change what anyone does, and a framework connecting production signals, trusted definitions, business context, ownership and feedback loops.}
}
TY - CONF TI - Beyond Dashboards: Turning Production Signals into Reliable Decisions AU - Sreevathsa, Sushrutha T2 - Proceedings of the Raptors Conference VL - 1 PY - 2026 DA - 2026/09/25 PB - Hackathon Raptors CY - London, United Kingdom DO - 10.68495/rc.2026.C087 UR - https://doi.org/10.68495/rc.2026.C087 AB - Production systems generate enormous volumes of alerts, incidents, service metrics and business signals, and more data does not by itself produce better decisions. Drawing on experience building operational intelligence and leadership-facing analytics at Yahoo, this paper describes moving from fragmented reporting and metric reconciliation toward a Connected Intelligence model linking trusted data, shared definitions, operational context, governance and action. The emphasis on shared definitions is not incidental: much of the effort in operational analytics is spent reconciling numbers that disagree because the teams producing them mean different things, and no dashboard resolves that. The paper also covers the tradeoffs, adoption difficulties and lessons from building a system that was actually used in operational reviews rather than merely delivered. Readers take away an understanding of why dashboards and AI-generated insights so often fail to change what anyone does, and a framework connecting production signals, trusted definitions, business context, ownership and feedback loops. LA - en ER -