According to authors, the implication of big data is the quantity is paramount, the returns generated do not match the quantity of data generated. Experts point out, it is not per se the data that should be big, but the primary factor that counts is the diversity of data, the amount of richness they provide and the focus on accelerating human understanding of data , which has the potential to create output subject to increasing returns. More data retards innovation, the speed of experimentation and iteration. However IT teams helps in bringing order to chaos, in data and analytics, by managing data infrastructure, such as data warehouses and production processes . Data scientists, who’re occupying the space between IT and business consumers , have made enormous strides in getting grip on their data, analyzing and acting on it, thereby avoiding imbalance. Read more at https://aitrends.com/big-data/three-big-data-developments-no-one-is-talking-about/