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July 26, 2018 | Sarah Fowler

Big Data, BI and AI Driving Cloud Adoption

Big data, Business Intelligence (BI), and Artificial Intelligence (AI) are top priorities for corporate IT in 2018 and are driving cloud adoption and migration, according to new data from 451 Research.

Here are some interesting highlights from the 2018 data:

  • “The key takeaway [from the survey] was data rules. Data-centric technologies in applications are the key focus of organizational IT for the coming year,” explained Melanie Posey, VP of Research for 451 Research, who presented the findings in a recent webinar.
  • The survey found that 45% of respondents listed BI and analytics as one of their top three top priorities for the year ahead, while 29% named machine learning and AI, and 28% big data. Those are substantial increases over last year’s responses.
  • Another study, a 2018 Forbes survey, found that cloud BI adoption doubled this year over 2016 levels, with sales/marketing being teams, of which about 67% currently use cloud-based BI and another 20% are considering using it in the near future. Sales and marketing, pure BI and manufacturing, supply chain and service/support are the top uses of cloud-based BI and analytics.
  • In terms of AI and machine learning, 451 Research’s data found that today’s initial adopters of cloud-based AI and machine learning are mostly in the finance, telecom, information technology and healthcare industries, but that many opportunities exist in other industries for leveraging cloud AI and machine learning. In fact, the biggest growth is predicted for AI and machine learning usage – a 17% increase in the coming year, compared to a projected 7% increase for BI and 5% for big data.

The adoption of these intelligent applications is driving public cloud adoption largely because most companies still lack the infrastructure and internal expertise to implement big data, AI and BI applications themselves.

Outside of large corporate data centers, only public cloud infrastructure can support massive data storage as well as the scalable computing capability needed to crunch large amounts of data and AI algorithms. Even those companies that have private data centers often opt to save themselves the cost of ramping up the hardware, networking and data storage required to host big data and AI types of applications.

Maximizing the value of these applications requires highly scalable infrastructure, noted Posey.

“To harness the business value of data, the underlying IT has to be flexible enough to have capacity and distribution scale in order to use data in a way that improves both business operations and provides insight into what customers are doing,” explained Posey.

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Another challenge for organizations adopting AI, BI, big data and machine learning is a lack of internal expertise. While organizations can rely on a managed public cloud services to fill a gap in cloud expertise, they must still have staff capable of using or developing AI, BI and big data applications. Amazon’s machine learning application SageMaker, for instance, is intendent to remove many of the barriers to machine learning adoption by providing the cloud infrastructure, platform and a developer application. But a customer must recruit data science and development expertise to make use of it.

Regardless of the current challenges, adoption of all of these cloud-based technologies is moving forward and is helping to drive more organizations to public cloud or hosted private cloud providers. It’s all part of the ongoing digital transformation, said Posey: “Off premises IT is emerging as the go-to model for the underlying infrastructure for digital transformation and for harnessing the business value of data.”

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