IJARSCT ISSN (Online) 2581-9429 International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) Volume 3, Issue 1, January 2023 Copyright to IJARSCT DOI: 10.48175/IJARSCT-7876 277 www.ijarsct.co.in Impact Factor: 6.252 From Business Objectives to Analytics and Machine Learning Solutions: A Framework for Conceptual Modeling Bhagyashree Pandurang Gadekar 1 and Dr. Tryambak Hiwarkar 2 Research Scholar, Department of Computer Science 1 Professor, Department of Computer Science 2 Sardar Patel University, Balaghat, MP, India Abstract: Analytical methods and machine learning are progressively being incorporated into all kinds of information systems. Despite the excitement around these technologies, contemporary firms nonetheless have trouble utilizing them to fully use their data and solve the company's challenges. Businesses must deal with a variety of challenges while developing business analytics and machine learning solutions, including requirements elicitation, design, development, and implementation. Although conceptual modelling and requirements engineering approaches to the process are important and relevant, little study has been done in this area. In this paper a conceptual modelling framework for business analytics and machine learning solutions that is shown and evaluated. The framework consists of instantiations, meta-models, techniques, design patterns and catalogues, rules, and recommendations. It is made up of three modelling perspectives that each reflect a distinct aspect of a solution or the perspective of a different role in the creation of such systems. Through the capture of stakeholders, strategic goals, choices, questions, and necessary insights, the Business View aids in the elicitation of business analytical needs. The Analytics Design View, which largely focuses on machine learning solutions, aids in the design of the solution by collecting algorithms, metrics, and quality criteria. Keywords: Machine Learning I. INTRODUCTION Analytical components are becoming more prevalent in software-based products, services, and systems. Despite the hype, many firms struggle to apply machine learning and business analytics. Able Company growth is hindered by poor analytics implementation. Developing usable business analytics solutions requires understanding the limits of analytical methodologies like machine learning algorithms. One must first establish a business case and then turn it into analytical issues. This includes data pretreatment and feature selection, algorithm selection and trade-off analysis, integrating machine learning models to operational processes, and aligning found applications with business strategy. Analytical and machine learning-savvy executives and stakeholders are needed to tackle these issues. Prerequisites Business analytics elicitation is difficult. Most initiatives start with unclear and insufficient analytics needs. Stakeholders may know their strategic goals, such as improving marketing campaigns or decreasing inventory levels, but they may not understand how analytical approaches might help them achieve them. Stakeholders and data scientists (those with integrated abilities in machine learning, statistics, databases, and optimization have a conceptual mismatch. This gap complicates the issue. However, linking analytics to company strategy is necessary to achieve benefit. Failure to align may lead to inaccurate assumptions about how analytics contribute to corporate strategy, lack of leadership support, and failed analytics project execution. To achieve such alignment, the organization must establish its analytics project goals, how to distribute resources, and which data assets to focus on [60]. All businesses should priorities finding, justifying, and proving the need for analytics. This aim requires discovering corporate objectives and translating them into analytics goals.