The function of Big Data technology and Data Science in the contemporary digital economy is examined in this article. According to the author, large and medium-sized businesses in the retail and service sectors have a greater interest in utilizing them. Banks, mobile operators, and major manufacturing firms actively employ these technologies (Artificial Intelligence) to evaluate data on equipment failures and minimize downtime, which enables cost-reduction. Big Data technology serves as a liquid product and a prerequisite for boosting business profitability through individualized customer care and predictive analytics. It is crucial to ensure the formation of unique data exchanges and legalize a single definition of big data for the modern digital economy.
The function of Big Data technology and Data Science in the contemporary digital economy is examined in this article. According to the author, large and medium-sized businesses in the retail and service sectors have a greater interest in utilizing them. Banks, mobile operators, and major manufacturing firms actively employ these technologies (Artificial Intelligence) to evaluate data on equipment failures and minimize downtime, which enables cost-reduction. Big Data technology serves as a liquid product and a prerequisite for boosting business profitability through individualized customer care and predictive analytics. It is crucial to ensure the formation of unique data exchanges and legalize a single definition of big data for the modern digital economy.
For its potential to facilitate the development of cutting-edgetechnologies with enhanced environmental performances,projected useat basic levels of life cycle analysis (LCA)technological readiness levels (TRL) has attracted enormousinterest in recent literature. But the normal LCA standardsfind it very difficult to evaluate developing technologies,necessitating methodological advancements in the presentLCA framework due to inadequate data, questionablefunctioning, scale-up problems, and uncertainties. We reviewpublished literature in this paper to identify majormethodological challenges and key research efforts to addressthese issues, with a focus on recent developments in five keyareas: cross-study comparability, data availability and quality,scale-up issues, uncertainty and uncertainty communication,and assessment time. Additionally, we provide a number ofsuggestions for further study to assist in the assessment of newtechnologies at low technical readiness level: (1) the creation of auniform framework and reporting procedures for life cycleassessments (LCAs) of developing technologies; (2) theincorporation of additional tools with LCA; and (3) the creation of adata repository for cutting-edge materials, procedures, andtechnologies. Decision-making techniques, such as multicriteriadecision analysis, risk analysis, and techno-economic analysis.
The new global economy has a need for business incubation, which is brought on by deregulation, globalization, and the quick and significant advances in the fields of technology, telecommunications, and digitization. Every business incubator must be skilled at developing startup company strategy and have links to financial resources, related business communities, and business information and consultancy. Because the Business Incubator may strengthen the capacity and business performance of new science and technology-based entrepreneurial prospects, its role in the development of Innovative SMEs is particularly strategic.
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