An Integrated Framework for Real-Time Project Intelligence Across Execution, Collaboration, and Reporting Layers

A persistent challenge in modern organizations is the separation between systems used for operational execution and those relied upon for managerial and strategic decision making. Delivery teams work within task-level execution tools, while portfolio managers and executives require aggregated, cross-project views to guide governance, prioritization, and resource allocation. This paper presents an integrated, automation driven framework that connects execution, collaboration, and analytics layers into a coherent project intelligence architecture. Using low-code automation and API-based data exchange, the framework synchronizes near real time execution data with structured collaboration artifacts and portfolio-level analytical reporting. Execution systems capture authoritative delivery state, an intermediate collaboration layer supports aggregation and contextual interpretation, and the analytics layer produces validated indicators for decision makers. By enforcing clear data ownership and consistent roll-up logic across layers, the architecture reduces manual reporting effort, shortens feedback cycles, and improves the reliability and timeliness of project information. The framework is implemented using widely adopted platforms and is presented as a reusable, tool-agnostic blueprint for aligning day-to-day delivery with executive-level insight in complex, multi-tool organizational environments.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-26
DOI
https://doi.org/10.5281/zenodo.18381832
Primary Topic
Big Data and Business Intelligence
Type
article
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article

An Integrated Framework for Real-Time Project Intelligence Across Execution, Collaboration, and Reporting Layers

Miha Saluja
Zenodo (CERN European Organization for Nuclear Research)
Big Data and Business Intelligence
article

An Integrated Framework for Real-Time Project Intelligence Across Execution, Collaboration, and Reporting Layers

Miha Saluja
article en

Abstract

A persistent challenge in modern organizations is the separation between systems used for operational execution and those relied upon for managerial and strategic decision making. Delivery teams work within task-level execution tools, while portfolio managers and executives require aggregated, cross-project views to guide governance, prioritization, and resource allocation. This paper presents an integrated, automation driven framework that connects execution, collaboration, and analytics layers into a coherent project intelligence architecture. Using low-code automation and API-based data exchange, the framework synchronizes near real time execution data with structured collaboration artifacts and portfolio-level analytical reporting. Execution systems capture authoritative delivery state, an intermediate collaboration layer supports aggregation and contextual interpretation, and the analytics layer produces validated indicators for decision makers. By enforcing clear data ownership and consistent roll-up logic across layers, the architecture reduces manual reporting effort, shortens feedback cycles, and improves the reliability and timeliness of project information. The framework is implemented using widely adopted platforms and is presented as a reusable, tool-agnostic blueprint for aligning day-to-day delivery with executive-level insight in complex, multi-tool organizational environments.

Zenodo (CERN European Organization for Nuclear Research)
University of Toronto (CA)
Peace, Justice and strong institutions
Openalex Percentile: Top 81%
Big Data and Business Intelligence
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An Integrated Framework for Real-Time Project Intelligence Across Execution, Collaboration, and Reporting Layers — Miha Saluja · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS