Data and analytics executives have always known in broad strokes the business value they can achieve from adopting machine learning (ML). The value tends to come in three ways: improving the user experience (customers and employees), generating operating efficiencies, or driving top-line growth.
But line-of-business teams face persistent challenges on the road to unleashing that value, with the number one roadblock being the inability to gain insights from their massive treasure troves of data. According to a recent data management Forrester Consulting study commissioned by Capital One, eight out of 10 data management executives cite poor data quality as their top ecosystem challenge.
