MIAMI, FL / ACCESSWIRE / November 3, 2020 / As corporate digital transformations gathered speed over the past decade, the hype surrounding Big Data grew to astonishing proportions, but it also became apparent that quantity was meaningless without quality. Initially, much was made of data scientists, whose skills allow enterprises to benefit from their massive collections of information through extracting valuable insights to guide business decisions. While robust, sophisticated analyses are undoubtedly vital, there is a growing belief that data engineering is the foundational block of data-driven success. "We live in an age where several transformational technologies are deeply impacting the business landscape, among them cloud computing, the Internet of Things (IoT), artificial intelligence, and machine learning," says Trevor Silver, founder and CEO of leading analytics, data engineering, and cloud computing solutions provider Exusia. "In our increasingly digitized world, it has become crucial to navigate enormous databases with speed and efficiency to extract value. With the volume of information expanding at a staggering rate, business organizations are starting to realize that building a competitive advantage requires strong data engineering capabilities alongside data analytics talent."

As explained in one publication, "Data engineers design and build pipelines that transform and transport data into a format wherein, by the time it reaches the data scientists or other end users, it is in a highly usable state. These pipelines must take data from many disparate sources and collect them into a single warehouse that represents the data uniformly as a single source of truth." For a long time, this critically important function remained in the shadow, with most enterprises equating the roles of data engineers and data scientists, Trevor Silver notes. However, it has become clear that the skillsets required are different, which has gradually led to the realization that data engineering may be even more important for the success of business initiatives. Unless the information delivered for analysis is of high quality and relevance, data scientists will not be able to derive value from it through accurate predictions and actionable insights, Trevor Silver adds.

Data engineering has taken center-stage in recent years, the most obvious reason being the explosive growth in information: according to IBM, "90% of the data in the world today has been created in the last two years." However, managing this staggering amount of unstructured information is a challenge for 95% of businesses, and poor data quality is estimated to cost the US economy as much as $3.1 trillion annually. The task of turning corporate data into a truly valuable asset becomes even more daunting given the advent of cloud computing, IoT, the shift to mobile devices, and the boom in digital services, all of which are creating new sources of information and data formats. In the modern globalized business world, data engineering has emerged as a vital facilitator of prompt decision-making, scalability, competitiveness, and efficiency, and its importance is only expected to grow.