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Data - the fourth industrial revolution

Could the power of data technology create a new industrial revolution? Let's investigate

October 5, 2020

There is no denying that the term ‘Big Data’ has been a buzzword for some time now. Whether the power of data technology can be deemed as an Industrial Revolution remains up for debate. So, what is an Industrial Revolution and could data be the next revolution?

Defined in the Oxford dictionary, the Industrial Revolution refers to: “The rapid development of industry that occurred in Britain in the late 18th and 19th centuries, brought about by the introduction of machinery”. This clearly defines the use steam power to mechanise increased levels of production across the country. Arguably, this definition could be extended further, by deeming that an Industrial Revolution is simply a transformative movement responding to a new modern agenda. With this definition in mind, the second revolution saw the introduction of electricity in the late 1800s. The third, saw the digital revolution that fuelled World War Two technology and consequently, introduced the World Wide Web. As the third revolution developed, maybe the makings of the fourth commenced: The Data Revolution.

The Data Revolution

Today, we generate more data in 10 minutes than all ever created through to the year 2003. This statistic alone proves that we are in a new age. Like its predecessors, this revolution will change current business processes, create numerous jobs in many fields and create tangible wealth for our economy. A a result, new business strategies will no longer be underpinned by manual laborious processes such as  ‘box ticking’ policies. This data movement paves the way for agile new concepts and fresh thinking individuals. But how will it affect the Insurance Industry?

In the next few years, personalisation of insurance will likely break down broad traditional insurance policies into bite-size insurance consumables. The days of the annual policy may be numbered as we already see companies selling insurance that can be turned ‘on and off’ to suit the customer. With the use of data analytics, insurers can discover interesting and accurate predictors of risk that do not involve asking customers questions.

Unfortunately, the insurance industry is not known for its propensity to embrace change. It is crucial though that future insurance professionals are far more adaptable. Furthermore they must become agile and open-minded in their thinking and accepting of this technology. For industry trainees, there is already a movement away from traditional tasks such as collation, organisation and cleansing of data towards its analysis and interpretation. The fields of actuarial and data science will be of great benefit to the future generation of underwriters and insurance entrepreneurs. Insurers should be looking at their recruitment process carefully. Perhaps choosing candidates that have ambition to become the next data science leaders and ultimately progress in this new age.

Data technology is setting out the stepping stones to an imminent 5th movement. Data provides the essential foundation for development in the fields of Artificial Intelligence and Machine Learning. Bring on the fifth industrial revolution: AI!

How GIROUX can help

The explosion in information technology means that businesses have never had more data at their disposal – data which, if used intelligently, has the capacity to yield a wealth of profitable information.  But without the means to interrogate this ‘big data’, the secrets buried in its depths are destined to remain that way.  The rise of big data means that we are entering the era of machine learning. By making machine learning more accessible, easier and less expensive to use, GIROUX is committed to addressing this challenge.  Using technologies such as Microsoft Azure Machine Learning (Azure ML) and other processes, our data scientists are committed to bringing machine learning into the mainstream.

How can GIROUX make a difference to the value of your organisation through the application of machine learning technologies?

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