Building a Production-Grade Enterprise Analytics Platform: Engineering Lessons from Scaling Business Intelligence at ADP
Abstract
Building an enterprise analytics platform involves considerably more than producing dashboards. It means solving production problems: fragmented data, business processes that change under the platform, scalability, and the stakeholder trust without which the output is not used. This paper sets out engineering lessons from designing and scaling business intelligence systems supporting national business operations at ADP, covering the architectural decisions taken, the work required to improve data quality, the governance applied across multiple enterprise systems, performance optimization, and the tradeoffs that shaped the platform in production rather than in design. The emphasis throughout is on what proved durable and what did not. Readers take away an approach to designing scalable analytics platforms for production environments, methods for improving data quality and governance across systems that were never intended to work together, a way of balancing engineering complexity against business value, and the architecture and implementation mistakes that generate technical debt, so that executives trust the platform for strategic decisions.
- Published in
- Proceedings of the Raptors Conference, Vol. 1 (2026)
- Conference
- Raptors Conference 2026, 21 October 2026, online
- Accepted
- 2026-09-16
- DOI
- https://doi.org/10.68495/rc.2026.C066
- Presentation
- Slides (PDF)
- Licence
- © 2026Janardhana Naidu Kola. 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/kola-building-production-grade-enterprise
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
Janardhana Naidu Kola (2026). Building a Production-Grade Enterprise Analytics Platform: Engineering Lessons from Scaling Business Intelligence at ADP. Proceedings of the Raptors Conference, Vol. 1 (2026). Hackathon Raptors, London. https://doi.org/10.68495/rc.2026.C066
@inproceedings{raptors2026-c066,
title = {{Building a Production-Grade Enterprise Analytics Platform: Engineering Lessons from Scaling Business Intelligence at ADP}},
author = {Kola, Janardhana Naidu},
booktitle = {Proceedings of the Raptors Conference},
volume = {1},
year = {2026},
publisher = {Hackathon Raptors},
address = {London, United Kingdom},
doi = {10.68495/rc.2026.C066},
url = {https://doi.org/10.68495/rc.2026.C066},
abstract = {Building an enterprise analytics platform involves considerably more than producing dashboards. It means solving production problems: fragmented data, business processes that change under the platform, scalability, and the stakeholder trust without which the output is not used. This paper sets out engineering lessons from designing and scaling business intelligence systems supporting national business operations at ADP, covering the architectural decisions taken, the work required to improve data quality, the governance applied across multiple enterprise systems, performance optimization, and the tradeoffs that shaped the platform in production rather than in design. The emphasis throughout is on what proved durable and what did not. Readers take away an approach to designing scalable analytics platforms for production environments, methods for improving data quality and governance across systems that were never intended to work together, a way of balancing engineering complexity against business value, and the architecture and implementation mistakes that generate technical debt, so that executives trust the platform for strategic decisions.}
}
TY - CONF TI - Building a Production-Grade Enterprise Analytics Platform: Engineering Lessons from Scaling Business Intelligence at ADP AU - Kola, Janardhana Naidu T2 - Proceedings of the Raptors Conference VL - 1 PY - 2026 DA - 2026/09/16 PB - Hackathon Raptors CY - London, United Kingdom DO - 10.68495/rc.2026.C066 UR - https://doi.org/10.68495/rc.2026.C066 AB - Building an enterprise analytics platform involves considerably more than producing dashboards. It means solving production problems: fragmented data, business processes that change under the platform, scalability, and the stakeholder trust without which the output is not used. This paper sets out engineering lessons from designing and scaling business intelligence systems supporting national business operations at ADP, covering the architectural decisions taken, the work required to improve data quality, the governance applied across multiple enterprise systems, performance optimization, and the tradeoffs that shaped the platform in production rather than in design. The emphasis throughout is on what proved durable and what did not. Readers take away an approach to designing scalable analytics platforms for production environments, methods for improving data quality and governance across systems that were never intended to work together, a way of balancing engineering complexity against business value, and the architecture and implementation mistakes that generate technical debt, so that executives trust the platform for strategic decisions. LA - en ER -