J.P.Morgan’s massive guide to machine learning and big data jobs in finance

Sarah Butcher  wrote an excellent article titled: J.P.Morgan’s massive guide to machine learning and big data jobs in finance

Ms. Butcher brings out 10 important points and I found #4 of particular interest.  It reminds of the first acronym I learned in Data Processing in the mid 1970s - GIGO - Garbage In - Garbage Out

"4. An army of people will be needed to acquire, clean, and assess the data 

Before machine learning strategies can be implemented, data scientists and quantitative researchers need to acquire and analyze the data with the aim of deriving tradable signals and insights.
J.P. Morgan notes that data analysis is complex. Today’s datasets are often bigger than yesterday’s. They can include anything from data generated by individuals (social media posts, product reviews, search trends, etc.), to data generated by business processes (company exhaust data, commercial transaction, credit card data, etc.) and data generated by sensors (satellite image data, foot and car traffic, ship locations, etc.). These new forms of data need to be analyzed before they can be used in a trading strategy. They also need to be assessed for ‘alpha content’ – their ability to generate alpha. Alpha content will be partially dependent upon the cost of the data, the amount of processing required and how well-used the dataset is already."

 The article does a great job defining areas of machine learning and what you need to know and what you do not. It also brings up the important languages and data analysis packages.