Subsequently, the Delphi method was applied to other places, especially those that dealt with public policy issues such as economic trends, health and education. It has also been successfully and accurately applied in business forecasts. For example, in one case reported by Basu and Schroeder (1977) [16], the Delphi method provided for the sale of a new product in the first two years, with an inaccuracy of 3 to 4% compared to actual turnover. Quantitative methods produced errors of 10 to 15 percent, and traditional unstructured forecasting methods had errors of about 20 percent. (This is just one example; the overall accuracy of the technique is mixed.) Recent research has also focused on combining the Delphi technology and prediction markets. They stressed the need for standardized reporting frameworks to improve transparency regarding data quality and to allow for appropriate conclusions. The BEE-COAST framework [6] has been shown to appropriately summarize the important characteristics of a number of big data data sources. If this framework is imposed by scientific journals and describes details that routinely describe the context of data collection, data ownership, content and temporality of the dataset, it is likely that concerns about conflicts of interest and data quality will be systematically reduced. . . .