A road map for applied data sciences supporting sustainability in advanced manufacturing: the information quality dimensions

A review of data science in business and industry and a future view by G. Vicario and S. Coleman

“…This is indeed a domain with opportunities for both interesting complex applications and for new research developments. A mapping of such areas is provided in the work of Kenett et al In addition, the implications of these new developments are relevant to general systems engineering methodology. An edited book on such state‐of‐the‐art challenges is of Kenett et al…”

“…Areas that are of interest in the current interface of statistics and machine learning/artificial intelligence/computer science include (1) data integration, also called data fusion; (2) generalization and transportability assessments; (3) causality analysis; (4) combining observable data with experimental design such as done in reinforcement learning; (5) compositional data analysis such as time use epidemiology; and (6) multivariate time series forecasting and multivariate process monitoring. For a mapping of analytic topics necessary in supporting Industry 4.0 initiatives, see the work of Kenett et al What are the tools and systems we need to develop ? Analytic platforms such as R, MINITAB, and JMP are now an integral part of applied statistics and data science deliverables.…”

Managing Data in Digital Marketing

“…In a broader sense, it is achieved data potential that can be used through the applied empirical models. Data understood in this way must meet 8 assumptions (Kenett R. et al, 2017). First, the scale of the variables and the amount of data must correspond to the complexity of the problem.…”

“…R.S. Kenett et al (2018) also indicates that data quality is manifested in the ability to generalize data at the statistical level, i.e. the possibility of inference on a sample and scientific basis, as the possibility of application based on a specific target population to other populations.…”

“…The study based on good quality data gives the opportunity to operationalize the information provided as a result of the study. The last qualitative criterion for perceiving data quality is providing clear and comprehensible information to the right people at the right time (Kenett R. et al, 2017).…”