Embracing predictive analytics in the fourth industrial revolution

The fourth industrial revolution is here. And, it is going to completely transform every single business and aspect of society.

A convergence of new technologies are driving this industrial revolution, what some call industry 4.0. These technologies are all supported by… drum roll… data.

The explosion of data, this whole new big data world, can be combined with “the explosion in artificial intelligence and machine learning technology; blockchain technology, the Internet of Things, robotics, virtual and augmented reality; all of those innovations seem to be coming together and driving this new industrial revolution,” explains Bernard Marr, the strategic business and technology advisor.

“I believe that like all previous industrial revolutions, it will have wide-ranging impact for people’s jobs and for how we run our businesses.”

Case study: the music industry. “We’re now using streaming services, those services use our data to give us songs we’ve never listened to before and would have never have found otherwise. It is pushing us to do more live concerts, because people want real experiences”  — Marr.

A data struggle

This explosion of data does not come without challenges — organisations are struggling to take advantage of the new wealth. Industries are in a completely unprecedented situation where they now have more data than ever before.

“There are a lot of different estimations,” says Marr, “but most people believe that the vast majority of data that we have today, 80% or 90% of it, was generated in the last two to three years.”

There are no signs this rate of data creation will slow down either. The amount of global data produced is generally expected to reach 175 zettabytes by 2025 — that’s 1,000,000,000,000,000,000,000.

“Many organisations are still trying to catch up with this and realise what it all means,” believes Marr.

There are lots of different reasons why organisations are struggling.

One is a lack of awareness of what can now be done with all this data. “I think people are sometimes afraid, they might think they haven’t got the right skills to use data effectively. This creates a fear factor and a cultural issue,” says Marr. “People are quite scared of this change.”

“I also see a technology barrier in lots of organisation,” he explains.

This is especially true of companies that have invested in technology over the last 20 years. They’re now in this new big data world and using data to drive machine learning, as an example. This requires new technologies, it requires integrated data platforms or cloud-based platforms, and this is a big shift that will take time for companies to complete.

The skills gap presents another hurdle. There are a lot of organisations that can’t quite access the talent and the data scientists or data analysts needed to turn data into insights.

However, there are some that are doing fantastically well in taking advantage of their data. What Marr calls the “trailblazing” companies, such as Amazon, Microsoft, Baidu, Tencent and Alibaba. “They see the value of data, compared to the more traditional companies,” he says.

“Accessing data is no longer the challenge — figuring out what data you need and how you best use it is the biggest issue” — Marr.

Embrace predictive analytics

MHR Analytics have partnered with Marr to deliver a series of whitepapers. The first focuses on how organisations can embrace predictive analytics.

The goal is to change the attitude to data. Quite often, organisations are using data in an ad hoc fashion. They need to start using data to help them with future planning, to predict the future.

“This is not necessarily the starting point,” says Marr, “but the idea is to show organisations that there is an evolution that they have to go through. I don’t think most organisations can start with embracing predictive analytics, but they can once the foundations are in place.”

SIDE NOTE — The foundation: organisations need to step back and ask what are their biggest business challenges, what are the biggest unanswered questions they have, where can they generate the biggest value from data? And then businesses need to start collecting and analysing this data to answer these.

“The starting point is exactly the same,” continues Marr. “Once businesses have their initial insights and their starting to answer some of their key business challenges, they can then start to use data in a more sophisticated way.”

One of those more sophisticated ways he refers to is using the data to predict the future.

There are two angles to this. One is customer related, where organisations use data to predict things, such as customer churn — they can predict what customers are likely to buy or what music someone wants to listen to next, for example.

The second is to use predictive analytics in internal operations, where organisations can try to identify production issues, for example. In manufacturing, business might want to look at their own machinery or their own service schedule and predict any clashes or shortcomings. Data can be used to predict maintenance cycles in this scenario.

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Real world examples

Companies are now using predictive analytics in their own operations and around their customers.

One company Marr recently worked with is Royal Dutch Shell. He helped them to develop their data strategy and the company is now using predictive analytics to predict demand for charging stations for electric vehicles.

There are other companies, such as Netflix and Amazon, who are predicting what consumers want to view next or buy next. “There are even brewing companies that are using data to predict what makes the best recipe for the best IPA,” says Marr.

KONE is one of the world’s leading lift and escalator manufacturers. They are using predictive analytics to predict when a lift might break down. “They are using a number of sensors in their lifts; they’re all connected to the internet with all the data streamed back to their analytics cloud. And they can then identify and predict issues with any given lift,” explains Marr.

This is a good example of a business model that has shifted, because of the impact of technology.

Data and the technologies it supports are changing business models; changing how companies interact with their customers and changing how companies operate internally.

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Top advice

“Do not ignore this industrial revolution that we are experiencing, because it is going to transform our world in the same way that all previous industrial revolutions have challenged business models and the way people work.

“This new industrial revolution will do the same. And the best way to respond to this is to think about this strategically. So when I work with companies on their digital transformation, what we do is take a step back from the existing business model and say is this still valid, is this the right business model for this fourth industrial revolution?

“Once you are clear that your business model is right, you then go and identify the key challenges and the key use cases for things like data and analytics. Then you will very quickly see that you will drive performance using the right data.

“This is a pitfall that I see lots of organisations falling into. They’re not quite revising their existing business model, they’re applying data analytics to outdated business models.

“Or they don’t identify the most important use cases and then they end up running a lot of pilots that might deliver some interesting value, but they’re not really tackling the biggest challenges and the biggest business issues. Therefore, they’re not really pushing performance.”

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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...