Machine Learning (ML) uses statistical techniques that enable systems to “learn” from data without being explicitly programmed. This is important because as ML models learn from previous computations they independently adapt to new data to predict outcomes and produce reliable results.
Even with advanced software and hardware the enormous scale of data available to firms poses several challenges on how to handle and store it. But machine learning is about how the analysis of the data adapts to the size of the dataset. This is because big data is not just long, but wide as well. Consider a medium bank database of customers in a spreadsheet. Each customer obviously gets a row. But because we can now collect so much data on every customer, every variable in the data gets its own column too. In today’s banking world, data can be so wide it has reached the point where there are even more columns than rows.
Most of the tools in machine learning are designed to make better use of “wide” data.