4 in 10 manufacturing firms experimenting with big data architecture

Just under half of businesses in the high-value manufacturing sector are currently experimenting with big data architecture.

This is according to a new study carried out by the Alan Turing Institute and Warwick Analytics, the results of which have been shared with The Manufacturer. It found that 41 per cent of businesses in the sector are currently at the experimentation stage with big data architecture, looking into how they can utilise this technology and the value it can provide. However, adoption is expected to accelerate in the near future, with the number of businesses still experimenting forecast to be only 11 per cent come 2019.

The study revealed there is a lack of clarity about some aspects of big data among manufacturers, with half of the companies surveyed unable to clearly understand the difference between business intelligence, big data analytics, and predictive analytics.

When it comes to technical barriers to adoption of big data analytics, having data spread across a number of systems, which will prove difficult to combine, was rated as the top factor. Concerns about the quality of data and difficulties cleaning it were ranked as the second biggest challenges. Other barriers mentioned ranged from a lack of data to having too much information, while some respondents believe data analytics is simply too difficult to understand.

A number of business challenges were also highlighted as obstacles to adoption, such as a lack of internal sponsorship, a shortage of specific data analysis skills and not having an effective business case for the technology. Despite these concerns, the majority of respondents see the value in big data analytics, with 92 per cent saying they believe it can drive a business improvement of more than ten per cent.

"The ability to extract meaningful insights about products; processes; production; yield; maintenance, and other manufacturing functions, as well as the ability to make decisions and take proactive action – when it matters – can deliver tremendous growth and profitability result," the report stated.

"Manufacturers have tremendous potential to generate value from the use of large datasets, integrating data across the extended enterprise and applying advanced analytical techniques to raise their productivity both by increasing efficiency and improving the quality of their products. However, the reality is that very few of today’s manufacturers are close to this vision yet," it added.

The study highlighted a number of key benefits that data analytics can deliver for manufacturing businesses. These include improving quality by providing a firm foundation from which the root cause of problems can be identified and making production more effective. Other advantages are faster time-to-launch, forecasting maintenance needs and improving supply chain operations.

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