The Cornerstone of Data Science: Progressive Data Modeling
One of the direct consequences of the increasing diversity of today’s data landscape is the growing complexity of data modeling. Organizations are consistently broadening their array of data sources, incorporating more structures, formats, and types of data in order to maintain competitive advantage.
However, such diversification can considerably exacerbate the data modeling process—particularly for those still relying on typical relational methods. Each time business requirements change or additional sources are added, data modelers must recalibrate the underlying schema for repositories or applications. This recurring cycle considerably delays time to value.
Read the full article over at AI Business.