More than nine out of ten (92 per cent) of businesses in various verticals, including retail, technology, banking, healthcare and life sciences, are investing in real-time analytics technology, handling data generated by both humans and machines, it was said in a report on Thursday morning.
The report, entitled "The 2016 State of Fast Data & Streaming Applications”, also said that 79 per cent of companies plan to shrink investments in batch processing. Another 44 per cent cited lack of expertise on new data frameworks to analyse data pipeline failures.
OpsClarity’s report also said HDFS, Cassandra and Elasticsearch are considered the most popular data sinks, Apache Kafka, Apache Flume and Rabbit MQ message brokers, and Apache Spark, Map Reduce and Apache Storm as most popular data processing technologies.
“With new fast data technologies, companies can make real-time decisions about customer intentions and provide instant and highly personalized offers, rather than sending an offline offer in an email a week later,” said Dhruv Jain, CEO and co-founder of OpsClarity.
“It also allows companies to almost instantaneously detect fraud and intrusions, rather than waiting to collect all the data and processing it after it is too late.”
The biggest issue with data analytics is that it is usually done in offline mode, on historical data. By switching to real-time data, businesses get an opportunity to react faster and make better future decisions.
For almost a third of respondents (32 per cent), fast data powered core customer-facing applications, while 29 per cent said it powered analytics, helping with the optimisation of internal business processes. More than a third (39 per cent) said both.
The full report can be found on this link.
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