Implementing Scalable Big-Data Tech Stacks in Pre- Seed Start-ups: Challenges and Strategies for Realizing Strategic Vision

Abstract

In order to prepare for choosing of pertinent data needed to prototype models, train them, and use the model, the massive volume of data generated by the Internet of Things must be verified and curated. However, open data and block chains are also significant data resources that must be included into the suggested integrative frameworks. The Impact Tech Start-up (ITS) is a brand-new, quickly evolving category of businesses. ITSs, which are typically supported by private investment, use creative approaches to address a range of environmental and social problems within a for-profit framework. These approaches are based on an entrepreneurial attitude and technology underpinnings. Currently, there is no discussion of this new organisational class in the academic literature. Machine Learning (ML) is an emerging development in technology. It is a subset of Artificial Intelligence (AI) that employs computer algorithms based on data that is accessible and may make decisions or improve upon them automatically based on experience and without requiring programmatic inputs beforehand. The first section of the article offers a theoretical framework for researching this organisational category, which combines elements of start-up companies and social enterprises. After that, it suggests a method based on Machine Learning (ML) to find ITSs in start-up datasets. ITSs are characterised using the Sustainable Development Goals (SDGs) of the UN, with indicators pertaining to the 17 objectives that meet the requirements for a start-up to be included in the impact category. The paper's conclusion discusses potential avenues for future research using the ML technique to examine ITSs as a unique organisational category.