The role of data analytics in industry 4.0
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The role of data analytics in industry 4.0

The role of data analytics in industry 4.0

Efficiency and increased profits are not the only advantages of adopting an Industry 4.0 model

“Big data.” “Data analytics.” “Data mining.” It seems everywhere you turn, there is talk of ‘data,’ but there seems to be less discussion of what data is, what you can do with it and how it relates to the real world.

Now, more than ever, data is taking a centre stage as we move further into what experts are titling the ‘Fourth industrial revolution’ or ‘Industry 4.0’ – and some say that the data, and the byproducts of gathering and analysis of data is the fourth industrial revolution.

Let’s unpack this a bit by going back in time. The first revolution was titled the Industrial Revolution, which used steam power to automise factories and travel; the second revolution introduced electricity as a means to accelerate industry. The third included the adoption of computers, robotics and the internet. Industry 4.0, or the fourth revolution, involves harvesting the information, ie. data, that comes from sensors, aggregators, cloud computing and the Internet of Things, to name a few, and making sense of them to create more profit and run companies, factories and organisations more efficiently.

The advanced technologies that have evolved because of the fourth industrial revolution have severely disrupted industry and society by connecting processes and systems that were previously unconnected, creating new insights and innovation, and the rise of artificial intelligence. Due to the importance and centrality of data, the field of data science has rapidly evolved. Data scientists can now rely on machine learning models, computational algorithms and visualization to extract insights from massive data sets – to better understand what information previously disparate systems can offer them.

How do organisations use the data they collect? There are almost unlimited ways but the most common are around production efficiency – studying data from sensors in factories, for example to learn how production may be stalled or how it can be improved. Data can also aid in predictive maintenance and automated production. In both cases, analysts study patterns and create data models that help their industries run more smoothly and efficiently.

Efficiency and increased profits are not the only advantages of adopting an Industry 4.0 model, data-savvy companies are more attractive to talent; more competitive and are able to identify problems before they become an issue. The struggle to use data efficiently, however, is great. Almost 95 per cent of companies globally cite that unstructured data is one of their greatest challenges (Forbes). Companies who adopted Industry 4.0 practices early however, reported greater resilience to crises, including the Covid-19 pandemic, with 65 per cent of respondents to a recent McKinsey study saying that their perception of the value of Industry 4.0 was heightened since March of 2020.

So, if data analytics is so great, are there any downsides?

Well, yes, there are a few. The largest are:

1. Security – the sheer number of connected devices coupled with the fact the previously siloed systems now work together, decreasing visibility, means that cybersecurity challenges abound. There have been numerous high-profile cyber-attacks where hackers only had to identify one weak link to compromise the whole organisation. Of course, the information security industry has adapted to these challenges and whole industries are dedicated to cyber security at an organisation level, but the risks remain and means companies must lay careful security plans and train staff across the organisation.

2. Talent – One of the biggest pain points for organisations is the lack of talent that understand data and how to analyse it, and then apply the learnings to a specific business case. It can also be expensive to employ a full-time data professional for an SME or in a case where the workload does not warrant a full-time employee. In that case it makes sense to look for a freelance data expert who can come in and work on specific data related projects.

3. Artificial Intelligence (AI) – AI, in a basic sense, helps make sense of data and is able to ‘learn’ through increased data to make predictions. In the field of healthcare, AI aids physicians to accurately diagnose based on data collected over years, which has revolutionised care and saved lives. With the rise of AI, however, comes challenges related to privacy, governance and has led to a fear of AI taking people’s jobs as more tedious tasks normally performed by humans are made obsolete.

There’s no doubt the fourth industrial revolution is the most disruptive to date. The way that humans run companies, offer services in all fields and live their daily lives has been altered in some way, often quite dramatically. Data and data management and analysis form the background of all of the transformation and innovation we are living through – now is the time to hire data talent and start to understand what data management and analysis looks like for your organisation.

Jadd Elliot Dib is the founder and CEO of Pangaea X

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