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Aussie IT chiefs boost investments in data analytics

Byron Connolly | March 17, 2017
A lack of a big data strategy is seen as a blocker for IoT adoption in one in four organisation

data analytics

Most Australian CIOs are planning to invest more money in boosting their big data analytics capabilities this year as demand for high volume processing and real-time intelligence grows strongly, according to analysis from Telsyte.

Intention to use data analytics is high across financial modelling, customer interaction, security and fraud detection, retail sales and e-commerce, and IoT and machine-to-machine infrastructure, Telsyte said.

The research found that half of Australian enterprises predict a five or more fold increase in the number of connected devices in their enterprise within the next five years. A lack of a big data strategy is a blocker for IoT adoption in one in four organisations.

“Just collecting and processing data is half the story,” said Telsyte senior analyst, Rodney Gedda. “Australian leaders must use real-time analytics to gain business value from data and transform their decision making from reactive to proactive.”

The research found the main barriers to the adoption of big data analysis initiatives among Australian enterprises and factors like software costs, lack of in-house skills and IT infrastructure requirements are holding back broader adoption in Australia.

It shows that big data processing is already the number one use-case for high performance computing and analytics and reporting in the top five.

Big data and associated analytics is now in the same league as CRM and marketing automation for share of the software budget, indicating its strategic relevance across a growing number of data sources, not just traditional databases.

Some 30 per cent of enterprises are using or planning to use big data for predictive analytics, indicating a strong requirement for these use cases, Telsyte said.

Furthermore, big data analytics is now in the top five of enterprise applications managed by third-party service providers, indicating a lack of in-house capability to process large data sets.

 

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