2019 is already proving to be a banner year for the big data industry – no question. We’ve seen cloud migration, a critical factor for many big data projects, really increase over the past year. Hybrid cloud models are becoming a very well-trodden path for the enterprise able to stitch together their increasingly complex and business-critical data pipelines with speed and reliability and cost-effectiveness.
For the industry, worldwide revenues for software and services around big data were projected to increase from $42B in 2018 to $103B in 2027, according to Wikibon. And according to an Accenture study, 79 per cent of enterprise executives agree that companies not embracing big data would lose their competitive position and face extinction.
Cloud has begun to stake its claim as a staple of this in 2019. As data delivery options merge and enterprises seek the scalability of platforms that help them achieve their goals, they also tackle a skills gap dilemma that isn’t closing anytime fast. In fact, with expert resources thin on the ground and commanding high salaries, with both automation and AI breaking out and becoming an essential component to delivery and operations.
The talent gap within DevOps and big data have rapidly become a barrier to growth and efficiency of analytic operations. In a recent survey, conducted for Unravel Data by Sapio Research, one in three enterprise business and IT decision makers revealed that one of the biggest pain points was talent scarcity. Perhaps by causation, 34 per cent claimed it takes too long to get to insight too.
With this premise proving true, we will start to see AIOps converging with DevOps as a top priority this year. For the enterprise, data is funnelled into training and improving AI-oriented applications and their development and delivery. Where the algorithm can take the strain of an over-stretched

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