ML challenges

Over the years, I have worked on many real-world problems using ML solutions and encountered different challenges faced by the different industries during ML adoptions.I often get the same question when working on ML projects: We have a lot of data – can you help us figure out what insights we can generate using ML? I refer to companies with this question as having the business use case challenge. Not being able to identify business use cases for ML is a very big hurdle for many companies. Without a properly identified business problem and its value proposition and benefit, it becomes difficult to initiate an ML project.In my conversations with different companies across their industries, data-related challenges emerge as frequent issue. This includes data quality, data inventory, data accessibility, data governance, and data availability. This problem affects both data-poor and data-rich companies and is often exacerbated by data silos, data security, and industry...