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The Fourth Industrial Revolution and Why the GDPR Matters

I make no apologies for another blog on the subject of data, because I believe data is one of the most important subjects in both our personal and working lives at the moment.

But first let’s talk about the Fourth Industrial Revolution. I was at a conference this week listening to a great keynote presentation from Microsoft which mentioned the Fourth Industrial Revolution. This isn’t a term I had come across before, so just in case you haven’t either I will explain a bit of background before going into the main subject of this blog.

When we think about the industrial revolution we tend to go back to the end of the 18th century. This was when the invention of water and steam powered machines transformed humankind from animal power, which enabled production on an industrial scale. Jump forward a century to the next revolution brought about by the invention of electricity. This created the capability for widespread mass production and saw the huge advances in the Victorian era. Moving ahead another century we get the third industrial revolution brought about by electronics and IT.

In all three of these revolutions the working lives of millions of people have been transformed. Each one heralded a change in working environments that threatened lots of ‘traditional’ jobs, but in the end created many more.

We now turn to the current era and what is being heralded as the Fourth Industrial Revolution. This revolution is the transformation of our lives by connecting billions of people with digital technology. The possibilities will be enhanced by emerging technology in fields such as artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing.

The term Fourth Industrial Revolution was coined by Klaus Schwab, the Founder and Executive Chairman of the World Economic Forum in January 2016. Read more about it here.

Again we are being told that many, if not most, of the jobs we are familiar with today will be replaced by robots or machine learning algorithms. Should we be fearful of this or should we just be looking for the opportunities for new types of job, just like our ancestors did in the 18th, 19th and 20th centuries?

The Four Industrial Revolutions

(picture credit: Microsoft.com)

What has this got to with GDPR, I hear you asking?

Big Data

When you look at all these emerging technologies the common factor that makes them possible is data or Big Data as it is known. Big Data is data sets that are so  voluminous and complex that traditional computing methods and application software are inadequate to deal with them. It is the digital revolution that has given us the capability to create and manage these massive data sets and is now causing many moral and ethical questions to be rightly asked.

If you have any interest in technology (and I assume you have because most of my blogs have a technology angle to them) you can’t fail to have noticed the shift in what has been grabbing the headlines over the last 25 years.  At the end of the 80s, about the time the worldwide web was invented, all our technology advances were in infrastructure i.e. the hardware and communications that powered the technology. This tended to be the limiting factor that stopped us doing more.

Going forward to the ‘noughties’ and we had largely solved most of these infrastructure problems and it was all about software and apps. However, think about the last couple of years, when was the last time you heard about a step change or major advancement in software? You probably haven’t because now it is all about data.

This is represented by the diagram below which I came across at a presentation by Big Data specialists, NetApp.

(picture credit: NetApp Blog 2017)

Computing as a service

This change in technology emphasis from infrastructure to data is evident in the way computing is delivered to today’s organisation. How many businesses starting today would spend much time thinking about infrastructure, data centres and servers? I would hope not very many. All these systems have been successfully commoditised and are available as services through cloud computing or hosting services. This leaves the organisation to focus its energies on its data.

You can also see this when you look at many medium to large companies. The ‘Chief Technology Officer’ is usually only seen in pure technology companies. Much like the ‘IT Manager’ or ‘Head of IT’ these jobs have all but disappeared from management teams. They have been replaced by the ‘Chief Information Officer’, ‘Chief Security Officer’ and even the ‘Chief Data Officer’.  Even in the C-Suite the move to a data driven economy is evident.

Data Protection

When data protection legislation was first introduced in 1995 it provided the legislative framework for countries to protect data belonging to their citizens. The advances in technology described above now make that legislation unsuitable. Another driver has been the erosion of citizen rights since 9/11 and highlighted by Edward Snowden. We are now entering a period where European data protection laws are, if anything, ahead of the technology that is needed to comply. Just one example of this is the requirement to delete personal data (the so called ‘right to be forgotten’) from a data controllers systems. This deletion includes from backup sets, but at the moment technology doesn’t exist for removing selective data from a backup – but it will catch up.

The Five V’s

Big data is often described around the three dimensions of Volume, Variety and Velocity. This obviously refers to the quantity of data in the data sets, the different types of data that cannot be analysed using conventional relational database tools, and the speed of data processing.

However, in the last couple of years Big Data experts are adding more dimensions (conveniently all beginning with V!). The next dimension is Value. When Big Data first emerged it was almost an intellectual challenge of ‘we can do this’ rather that answering the question of why it is needed. Getting value from the big data sets is essential to continue to drive innovation and to make investment in computing power worthwhile. There are many examples of where big data is creating value and this article from Forbes explores some of the challenges.

This leads on to the fifth big data dimension and back to my point on the need for comprehensive data protection legislation – the dimension of Veracity. This refers to the uncertainty of the data. To obtain meaning from any data it must be accurate and have the capability to prove its accuracy.


The EU GDPR becomes active on 25 May 2018, exactly 6 months from the time I’m writing this blog. This will provide the necessary legislation to ensure that citizens are able to control the data that organisations hold on them. This includes protection to correct inaccurate information and withdraw consent to processing their personal data. Whilst the law has been designed to protect the rights of individuals and restrict wholesale unauthorised processing of personal data it is also helpful for organisations to ensure the veracity of their data sets.

Unlike many of my peers in consulting I’m not jumping on the GDPR bandwagon by offering ‘expert services’. Like any new piece of legislation the GDPR needs careful planning and preparation, backed up by sound legal advice on what is appropriate for your business. I’m not a privacy lawyer so won’t be providing any advice on GDPR. However, just like with any other new legislation I can help organisations with the implementation of new systems and controls.

In my experience of working with database systems the accuracy of both client and organisational data leaves a lot to be desired. The introduction of the GDPR and equivalent local legislation provides a real opportunity to get data sets cleaned up. As well as complying with the law this can only be beneficial for the organisation, particularly when the data is used to support decision making. I would not want to rely on decisions made with poor quality data, would you?

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