• Satish Saka

Data-driven optimisation environments such as digital marketing, growth analytics, and performance management increasingly rely on short-interval performance metrics to guide scaling and intervention decisions. However, observed performance fluctuations often contain substantial noise arising from sampling variability, attribution delays, seasonality, and stochastic effects. Acting directly on unstable observations can lead to premature or erroneous optimisation actions. This paper proposes a structured decision-support framework that separates signal detection from action readiness in optimisation contexts. The framework introduces explicit evaluation of data sufficiency, directional stability, and variance behaviour before permitting scaling decisions. A four-state decision classification — Scale, Hold, Reduce, Block — is defined to represent intervention readiness under uncertainty. The proposed approach aims to reduce decision risk in automated or semi-automated optimisation systems by introducing an analytical layer between observation and action. The framework is applicable across domains where interventions depend on volatile performance data.

Decision-support systems, optimisation under uncertainty, signal stability, scaling decisions, data-driven systems

"A Decision-Support Framework for Optimisation Under Uncertainty in Data-Driven Growth Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.f630-f632, February-2026, Available :http://www.jetir.org/papers/JETIR2602585.pdf

"A Decision-Support Framework for Optimisation Under Uncertainty in Data-Driven Growth Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. ppf630-f632, February-2026, Available at : http://www.jetir.org/papers/JETIR2602585.pdf