AROhI: An Interactive Toolkit for Estimating ROI of Data Analytics

The cost of adopting new technology is rarely analyzed and discussed; however, it is vital for any technological investment because of the cost and effort associated with it. Thus, it is crucial to consider Return On Investment (ROI) when performing data analytics. Decisions on “How much analytics is needed?” are hard to answer. ROI could guide decision support on the What?, How?, and How Much? Analytics for a given problem. This work details a comprehensive tool that provides conventional and advanced ML approaches for demonstration using software requirements dependency extraction and their ROI analysis as a use case. Utilizing advanced ML techniques such as Active Learning, Transfer Learning and primitive Large language model: BERT (Bidirectional Encoder Representations from Transformers) as its various components for automating dependency extraction, the tool outcomes demonstrate a mechanism to compute the ROI of ML algorithms to present trade-offs between the cost and benefits of a technology investment.