Use Data to Tell the Future: Understanding Machine Learning

Published March 18, 2014   |   
Martin Hack

When Amazon recommends a book you would like, Google predicts that you should leave now to get to your meeting on time, and Pandora magically creates your ideal playlist, these are examples of machine learning over a Big Data stream.

With Big Data projected to drive enterprise IT spending to $242 billion according to Gartner, Big Data is here to stay, and as a result, more businesses of every size are getting into the game. To many enterprise organizations Big Data represents a strategic asset — it reflects the aggregate experience of the organization. Each customer, partner, or supplier response or non-response, transaction, defection, credit default, and complaint provides the enterprise the experience from which to learn. From a consumer perspective, every action performed online, every sales process, product interaction, prescribed drug, and environmental anomaly, is being tracked by various sources.

In recent years, companies have focused on how to store and manage this data. How should we best architect our enterprise stack to gain value from Big Data in terms of Hadoop, complex event processing, NoSQL and traditional data warehouses? Should we host our data on-premise or on the cloud?

These are fair questions to ask, but they don’t get to the core of why Big Data is a big deal. Only with advanced analytics, and specifically machine learning, can companies truly tap into their rich vein of experience and mine it to automatically discover insights and generate predictive models to take advantage of all the data they are capturing. This advanced analytics technology means that instead of looking into the past for generating reports, businesses can predict what will happen in the future based on analysis of their existing data. The value of machine learning is rooted in its ability to create accurate models to guide future actions and to discover patterns that we’ve never seen before.

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