As organizations look to advance with analytics, predictive analytics is frequently on their road map. Businesses are interested in better understanding their customers, predicting behavior, and improving operational processes. They want more accurate insights and the ability to respond faster to change. Machine learning—building systems that can learn from data to identify patterns and predict future outcomes with minimal human intervention—is often on their radar. Data scientists who engage in analysis are an important piece of the equation. Data scientists can build new models, develop algorithms and applications, and help the organization innovate. However, these data scientists are not always easy to find. TDWI research indicates that organizations are often looking to supplement the data science team by growing the skills of business analysts to use tools such as machine learning. For example, in a recent TDWI survey, 51 percent of respondents said that enhancing business analysts’ skills was one of their top two strategies for growing their data science competencies in the organization.1 That means that organizations need productivity tools for data scientists as well as a way to equip power users and business analysts to perform advanced analytics. These business analysts can work together with data scientists and other team members to bring machine learning into the organization. How do businesses get started with machine learning? How do organizations equip business analysts to use machine learning techniques and work in conjunction with data scientists? What do these organizations need to know? This Checklist defines machine learning and discusses best practices for the business as it takes the next step on its analytics journey toward using machine learning. Machine Learning for Business: Eight Best Practices to Get Started

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