Throughout the past 12 years, I have been involved in numerous executive decision-making processes that have involved significant financial investments, identifying problems among hundreds of machines, and designing machine learning(ML)/deep learning(DL) algorithms. However, before these high-level decisions can be made, there are many tedious and repetitive manual tasks that must be completed, such as data collection, cleansing, and visualization, as well as ML/DL model building. Although data engineers/analysts/scientists are often hired for these tasks, they may struggle due to the lack of a clean and efficient work environment. This can lead to significant overhead, including repetitive data mining and cleansing, and manual model building. Unfortunately, many machine learning projects are abandoned before they can be deployed, especially in organizations that are in the early stages of implementing AI/ML. To address this issue, we need to help emerging organizations gain insights and make better decisions more quickly and cheaply. This can be achieved through the use of automated data pipelines and AI tools, which can streamline the data preprocessing and modeling processes. Additionally, organizations can invest in cloud-based infrastructure, which can provide scalable compute, storage, and networking resources. To ensure that data-driven decision-making is effective and efficient, organizations should prioritize data quality and establish clear goals and KPIs. They should also foster a data-driven culture and promote collaboration between data scientists, engineers, and business stakeholders. Finally, organizations should continuously monitor and evaluate their data-driven decisions to ensure that they are achieving the desired outcomes, and make necessary adjustments to their data-driven strategies.