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Three Reasons Why DataOps Will Boom In 2021

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Forbes – by The Forbes Technology Counsel

Three Reasons Why DataOps Will Boom In 2021

While DataOps is a relatively new term, more and more people in the data industry are discussing it. From recent conversations with business analysts, to customers, to partners, there seems to be a growing demand for tools and platforms that address the growing pains and challenges of data teams. A recent survey of data professionals conducted by Nexla regarding how they use data, their team structure and data challenges found that 73% of companies are investing in DataOps.

Here are three reasons why DataOps professionals and teams should take center stage in 2021:

  1. There is a ridiculous amount of data.
  2. Urgency to end wasteful and ineffective processes. Very few organizations stop in order to measure the time and money wasted due to bad data processes. Also known as "data debt," the cost associated with mismanaging data isn't only impacting finances but also harming teams' abilities to make better decisions. Petr Travkin, solution architect in the data and analytics practice at EPAM Systems, sees DataOps as the answer to paying down organizational data debt. Travkin explains how organizations are often excited about applying advanced analytics to business areas yet struggle to see the value in improving workflows. The resulting data debt comes in the form of wasted data engineering, data science and analytics efforts.
    With many companies under increased financial strain due to a global pandemic that is taking its toll across most industries, cutting costs and optimizing processes is at the top of the agenda. DataOps is one of those areas in which the ROI from putting all ducks in a row is crystal clear. In addition, companies know that they can't cut corners on insights, and with many businesses expanding their online presence as a result of worldwide lockdowns, there is an increasing amount of digital data that needs to be managed, analyzed and used as strategic insights.
  3. Closing the gap between data and insights.

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