By Kyle Seaman, Head of Product at Sentenai, and Sourav Dey, Co-Founder and CTO at Manifold
Predictive analytics is an undeniably valuable technology, with research indicating its market size could top $12 billion USD by 2022. Across a range of industries, businesses, and applications, using historical data to predict future outcomes can lead to greater operational efficiency in a variety of ways. Predictive analytics can enable organizations to streamline their operational processes, optimize their demand forecasting, drastically reduce downtime, and better understand their customers’ propensity to buy.
That said, reaping the benefits of predictive analytics requires a fair amount of engineering legwork. For instance, before applying machine learning techniques to identify the likelihood of future outcomes based on historical data, the data in question must be prepared for training those machine learning algorithms in the first place. Looking at the historical data, organizations and their data scientists need to determine which data is viable and how trustworthy it is, then transform it from its raw initial state into clean datasets that a machine learning algorithm can use.
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