Why slow big data analytics adoption might be great news for manufacturers

Attend the webinar with Knorr-Bremse and find out

In my last blog, I discussed how research from Industry Week found that only one third of North American manufacturers felt they had significantly implemented analytics within their business. Manufacturing executives understand the benefits of Big Data Analytics so it made me question why adoption is still so low.

I’ll be holding a webinar with customer Knorr-Bremse on November 7th where I’ll discuss some of my conclusions. Register here. In the meantime, I began to think that it may actually be good news for those manufacturers. Here’s why.

Sad to say but I’ve been around for some time. I even remember the 60s. Few things endure from the time – with the notable exception of the Beatles (and Scotland beating new world champions England at Wembley, but that’s a personal thing). Something that has stood the test of time is the technology and innovation adoption curve developed by Everett Rogers (see figure 1 below). It has been a reference model for 55 years and terms such as ‘early adopters’ and ‘late majority’ still resonate.

Then came a chasm

You know how it is. You’ve just built something really good and then some smart person comes along and breaks it. I have this vision of Everett Rogers just getting comfortable with all these people telling him how incredible his model was when up steps Geoffrey Moore and drives a massive chasm through it (see figure 2 below).


Moore didn’t doubt Rogers’ model. His point was that, within the adoption curve for high tech products specifically, it is difficult for a market to move from the ‘early adopter’ stage into mainstream adoption. This appears to be the case when in comes to Big Data Analytics in manufacturing. There are innovators and early adopters – we’ve all read about the work being done by the likes of Siemens, GEC and John Deere. The rest of the manufacturing sector is some distance behind.

In our recent research with Industry Week, respondents understood the importance of data and analytics across a wide range of business activities. Yet, when asked, less than 20% of respondents said they had extensively implemented Big Data Analytics for any of those activities. That leaves over 80% of companies that are still on the wrong side of the chasm – so it look like both Rogers and Moore got it right!

Or did they? It’s my turn to throw the fly in Rogers’ ointment. I don’t want to question the validity of either model but just to point out that the curves were designed for a single innovation or high tech product. Big Data Analytics, however, is not a single technology. For me, it’s a combination of overlapping technologies:

  1. Big Data the tools and processes needed to properly capture, manage and exploit from the vast volumes and variety of data facing every enterprise.
  2. Advanced Analytics – the tools and processes needed for the autonomous or semi-autonomous examination of data to discover deeper insights, make predictions, and improve decision-making.
  3. Artificial Intelligence (AI) – the tools and processes required to create and manage intelligent machines that can operate, interact and learn in a ‘human-like’ manner.

While we can accept that each individual technology will, more or less, follow the same bell curve adoption process, there’s another model to consider. And, it’s from another Moore. This time it’s Gordon Moore who noticed the pace of technology change doubles every 18-24 months.

So, technology development is accelerating and, of course, no two technologies are adopted at exactly the same rate. When you have a combination of technologies, they will overlap and play off each other (see figure 3 below).


We are already seeing the result of this interplay with AI-enhanced Analytics solutions – such as OpenText™ Magellan – now radically changing what companies can achieve through analytics. AI-enhanced combines advanced analytics capabilities with comprehensive Artificial Intelligence features – such as deep learning, machine learning and Natural Language Recognition – to uncover what’s actually contained in information.

An AI-enhanced Analytics solution will get smarter and more accurate over time as it is continually learning the more interactions it has with data and humans. A deep understanding of information helps companies draw from the wide variety of data sources to improve the quality of enterprise knowledge and implement real-time decision-making. And, these solutions are available today

This is good new of manufacturers…how?

Neither analytics nor AI are new concepts for manufacturing but, with the massive explosion of Big Data, they are technologies whose time has come – in fact, here’s MIT saying ‘AI is the new black’ – and manufacturers are now able to invest in analytics solutions with vastly superior capabilities to traditional BI-style systems. By being slow to adopt analytics while the technology has advanced at incredible pace, those manufacturers now considering their analytics requirements may just be able to implement next generation platforms that will deliver real competitive advantage.

If you’d like to know more about where Big Data Analytics can benefit your business, please join me and Knorr Bremse for our Analytics webinar on November 7th.

Tom Leeson

Tom is Industry Marketing Strategist for the Manufacturing Sector globally.An Engineer by Trade, and Mathematician by Education, Tom’s entire career has been spent in Engineering, Manufacturing and IT helping customers digitally transform their business and their manufacturing sector.With Industry 4.0 and the Industrial Internet of Things, Manufacturing lives in exciting times, so there is much to talk about.

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