In the domain of data science, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights. Organizations can then use these insights to take actions that ideally influence future outcomes for the better. The flow of IBM's data science methodology ensures that as data scientists learn more about the data and the modeling, they can return to a previous stage to make adjustments, iterate quickly and provide continuous value to the organization.

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Life revolves around prediction—for example, the route you take to get to work, whether to go on a second date, or whether or not to keep reading this sentence are all forms of prediction. Predicating our future is very much tied to progress. We use it to help us plan our lives so we can increase our likelihood of success. The problem is that the human mind cannot possibly process all of the insight flowing from big data. Machine learning is the answer to this problem, through its capacity to augment our decision making in the moment to deliver transformative business outcomes. We are already seeing machine learning powered by Apache Spark changing the face of innovation at IBM.

View IBM's privacy policy here

View IBM's privacy policy here