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Manual identification of data patterns are getting harder as the amounts of data is growing more complex. In 2009 more data was produced than all the years before that (Source: MIT Sloan Management Review). There is a lot of potential in all this data – predicting future events such as maintenance (see this case from Pentaho) and adapted content and products based on the customer’s behaviour – but we will be far from reaching that potential without automatic methods.
Data Mining is the process where algorithms are applied on data to find patterns, that would be hard to discover with the help of tables and diagrams. It has for long been used in the academic world but is now moving into all industries at scale. One the most common use cases for Data Mining so far is to adapt better to customer needs, and thus improve customer satisfaction. With the help of statistical models of the business – that is all transactions and information about customer behaviour – data mining is used to see trends and patterns in the historical data. The findings are used in improving products and services, routines and processes, and to “predict the future”, so that we can prepare for it.
Machine Learning is a subset Artificial Intelligence and its goal is to enable computers to learn on their own. Learning algorithms are used to enable a machine to identify patterns in data, build models that explain these patterns and then predict what might happen without having to pre-program rules. To learn more about Machine Learning and how it works, head over to our blog.