What Does The Fourth Industrial Revolution Look Like?

Krishen Patel our consultant managing the role
Posting date: 3/5/2020 10:25 AM
We’re in the next stage of the fourth Industrial Revolution and technologies continue to merge. No longer is advancement as simple as adding “tech” to the end of a word - sorry Fintech, InsurTech, HRTech, and the rest.

Now technologies stand together as each becomes a separate piece of how tech operates in the business world. AI and IoT have merged to become AIoT. Data is as much commodity as it is information to fuel business growth. Computer Vision partnered with AI is teaching computers to convert their ones and zeros to images humans take for granted.  

In a word, it’s a transformative time for every industry and every industry is taking advantage of the benefits in one way or another. Smart manufacturing. Human Resources. Marketing. Even insurance has joined the party.

But, with so many advancements, we thought we’d take a look at just the tip of the iceberg, starting with A, B, and C. 

AI Meets IoT 


We’ve all heard how AI and the Internet of Things (wearable and smart devices etc.) are being used in the Health sector. With the kind of real-time Data available, patients, insurers, and medical professionals can map out health plans based on wearable devices to track patient health and encourage preventative care. 

Indeed, one insurance company is embracing these Data trends to ramp up the speed and efficiency of their data. Using Machine Learning and IoT sensors to develop an AI-based solution, customer information is used to match clients with the right policies tailored to their needs. 

Car insurance is another industry to benefit. Insurers are able to collect real-time driving data which they can analyse to determine risk or offer discounted policies for good driving. This kind of information can also be used to revisit and reconstruct accident scenes to figure out what happened and who’s at fault. 

Big Data, Big Money


We’ve all heard the phrase ‘Data Is The New Oil’ by now, which I’m sure we can all agree, just means Data is a resource everybody wants and is willing to pay a lot for. But the differences between Data and Oil are two-fold; Data has the potential to be infinite, and it tells us about what oil cannot; the human experience. 

Cloud technologies, edge hardware, and the IoT have helped shape the digitisation of objects, people, and organisations. From sensors to wearable devices, more and more data is being collected, allowing us to be more connected than ever before. It’s also providing more information to the tech giants than ever before. For example, Amazon’s Ring doorbell is logging every motion around it and can pinpoint the time to millisecond.  

Add these technologies to Natural Language Processing (NLP) and watch the world around us draw value from and understand our Data like never before. The wave of Big Data value shows no signs of slowing down.

Computer Vision in Business


In the last few years, Computer Vision has been making great strides in the business world. Yet the Data required for processing power and memory can still be impacted by image quality. The opportunities  are alive with possibility and, from small businesses to enterprise solutions, Computer Vision has seen a variety of industries finding practical business uses. 

Below are just a few additional areas Computer Vision is making its mark.

  • Facial Recognition – providing surveillance and security systems in such areas as police work, payment portals, and retail stores.
  • Digital Marketing – sorting and analysing online images to target ad campaigns to the right audiences.
  • Financial Institutions –preventing fraud, allowing mobile deposits, analysing handwriting, and beyond.

With the global market for fourth industry technologies predicted to be between $17.4 billion and $48.32 billion by 2023, now is the time to find your focus within the industry. 

Ready to take the next step in your career? Whether you’re interested in AI, Big Data and Analytics, Computer Vision or more, we may have a role for you.

Take a look at our current opportunities or get in touch with one of our expert consultants to find out more.  

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Data Science For Business Decision Making

All strong and successful businesses are built and run upon well-informed decision-making, which derive from a mix of leader experience, industry knowledge and, more recently, the regular implementation and use of advanced Data Science teams.  While the use of data has been around for many years, it’s hard to believe that it is only in the last five years or so that we have seen the adoption of such technology and skills really take off. Five years ago, the importance and demand for Data Scientists sat at a very meagre 17 per cent, whereas in 2019, we saw exponential growth of over 40 per cent – a number that is expected to continue growing as we move forward.  Within Data & Analytics, Data Science is a crucial arm within many businesses of all shapes and sizes. Through the collection and analysis of certain datasets, Data Science teams can delve into an organisation’s pain points, any potential obstacles and future predictions; crucial elements which, if looked at and planned for in advance, can be the making of a business.  So, how else can Data Science influence the decision-making process and make a positive impact on a business and its bottom line? The removal of bias and the increase of accuracy As humans we are innately susceptible to bias, conscious and unconscious, and this can be a hindrance on our ability to make informed yet impartial decisions. By relying solely on facts and figures instead of our own opinions, we are not only removing bias, but we are in turn making the decision-making process more accurate.  Accuracy within decision-making will remove the potential risk of mistakes and the need to re-do tasks, therefore saving precious time, resource and money, unequivocally a benefit for any business’s bottom line.  Efficiency There are elements of all businesses that require trial and error for example, hiring practices. People who look great on paper and perform exceptionally well in first interview may turn out to be utterly the wrong fit six months down the line. However,  collecting and recording data of those employees who do fit well into the business, compared to those who don’t, can help to reduce the chance of choosing the wrong candidate. This in turn improves staff retention rates, helps create a positive work culture and, of course, positively impacts profitability.  Considering the cost for hiring one person for a company is around £3,000, Data Science is of huge benefit to any company, large or small, in reducing the risk of high staff turnover.  Mitigating risk All businesses at some point in their lifetime will come up against potential obstacles and risks that, if not managed properly, can be potentially lethal. The implementation of Data Science will allow senior leaders to learn from past mistakes and create evidence-based plans to better tackle, or completely avoid, similar problems in the future.  This could be for either organisational risk or strategic risk, both of which can be extremely damaging if not prepared for. Organisational risk entails problems occurring within daily business tasks such as fraud, data loss, equipment and IT issues and staff resignations. Strategic risk relates to events that cannot be planned for in advance; those sudden and unforeseeable changes - a great example being the current COVID-19 pandemic.  However, with both risk groups, Data Scientists can help to mitigate these risks through learnings and observations made from reams of previous data, as well as real-time intelligence. This allows senior leaders to act fast where needed, and plan where possible.  Data & Analytics, and especially Data Science, has been, and will continue to be, a key driver in the evolution of many industries worldwide. As we move forward, we will undoubtedly see an even larger uptake of the available technologies as business leaders everywhere begin to see the influential value of data-driven decision-making. If you’re a Data Scientist looking to take a step up or are looking for the next member of your team, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

RELATED Jobs

recently viewed jobs