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Augmented Analytics to play vital role in converting Big Data to Smart Data

Smart Data helps businesses reduce the risk of data loss and improve a range of activities.

March 13, 2019 / 16:00 IST

Advancements in data processing tools and adoption of next-generation technologies such as augmented analytics to extract insights from Big Data are expected to drive the Smart Data market toward $31.5 billion by 2022, finds Frost & Sullivan.

Augmented analytics automates data insights and provides clearer information, which is not possible with traditional analysis tools. Companies such as Datameer, Xcalar, Incorta, and Bottlenose are already focusing on developing end-to-end smart data analytics solutions to obtain valuable insights from Big Data.

"Markets such as the US, the UK, India, and Dubai have rolled out several initiatives to use Artificial Intelligence (AI) and machine learning-powered data analytics tools to generate actionable insights from open data," said Naga Avinash, Research Analyst, TechVision. "Smart Data will help businesses reduce the risk of data loss and improve a range of activities such as operations, product development, predictive maintenance, customer experience, and innovation."

Frost & Sullivan's recent analysis, Turning Big Data to Smart Data: Emerging Opportunities, highlights key market developments, technologies used to convert big data to smart data, government programs, and the companies applying data analytics. It also presents use cases and analyst viewpoints on smart data.

"The evolution of advanced data analytics tools and self-service analytics endows business users instead of just data scientists with the ability to conduct analyses," noted Avinash. "Technology developers can ensure much wider adoption of their solutions by offering in-built security mechanisms that can block attackers in real time. They could also develop new business models such as shared data economy and even sell data-based products or utilities."

Moneycontrol News
first published: Mar 13, 2019 04:00 pm

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