The Rising Importance of Predictive Analytics

As big data continues to grow in demand, it is tough for organizations to make predictions about their future business outcomes and trends based on their historical data. With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer an option for business analysts and line-of-business experts, but a necessity. Organizations today are harnessing the power of predictive analytics to examine large data sets for discovering patterns, uncovering new information, predicting future outcomes and increasing their business bottom line.

While predictive analytics has the power to help all types of organizations, before moving forward with a major investment, it’s important for businesses to understand how to build a strategy, how organizations can ensure data quality and how predictive analytics models can be improved. In an article for AiThority, Moshe Kranc, Chief Technology Officer, Ness Digital Engineering, shares some insights on establishing predictive analytics as a true business discipline with a unique strategy in place, alongside considering various other components in the data quality process, which further helps to improve the predictive analytics model complexity and usability.

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