Currently, there are high expectations in the mining industry, across the Supply Chain, on how sensors and new technology providing real time data can support and optimise business decisions. In addition, sophisticated statistical algorithms, such as machine learning or conditional simulations, are more and more explored/used to address topics as uncertainty and “optimisations” in the plans, at different horizons, to “maximise the value of the business”. Despite the future of data collection is heading in the direction where sensors will be providing real time in-formation, this is still in the development stage. The main challenge of the current status for sensors, new technology or statistical analysis, is that they are mostly based on the assumption that the data used during calibrations or data processing, is correct or representative. This paper elaborates in more detail (with examples) on how the Theory of Sampling and the implementation of Quality Programs (QAQC & QM), across the supply chain, represent key enablers in the research, applications, selections and implementation of technology providing real time data, as well as the quality quantification of the information used as input for data processing: what a sampling protocol represents, how main and deleterious elements are distributed in the lot to be sampled, grade per grain size distribution profile, what can impact sample collection process, how gaps during sample collection shall be monitored, sources of bias, sources of variability and how this information can be used to quantify the current quality performance that will need to be improved with the technology. This paper also elaborates on the current expectations of minor/trace element data (normally on ppm levels), specifically in the understanding and challenges these types of data represent. The final objective of this analysis is to highlight the potential impacts during a capital process where new technological projects can be wrongly excluded from consideration due to errors in the baseline used for comparison, as well as the potential impact on reconciliation and marketing results due to technology or statistical analysis using biased datasets.