Today’s enterprises understand the rush to capitalize on the power of data science, but they may not always know exactly what a successful data science project looks like. Business leaders are primarily concerned with business KPIs such as revenue growth, efficiency and productivity. In contrast, data science teams deal in technical KPIs such as model accuracy and precision. For their projects to be successful, data science teams must bridge this gap, helping executives understand how technical KPIs translate into business KPIs. When both business and technical stakeholders speak the same language, they can determine what they want to accomplish with…

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