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Introduction: Change Brings Opportunity
The fourth industrial revolution is happening now, promising a future world of cyber-physical systems and Big Data that let businesses increase their capabilities and improve operating efficiency. Revolutions bring seismic upheaval, and, among the exciting changes, a transformation is expected in the demands for skills and in the associated employment prospects.
As engineers and techs, we want to be part of making it happen and have a role in improving products and services with the potential to enhance the quality of people’s lives. As human beings, we want to discover our potential and use our skills to provide for those closest to us.
The pace of change has swept away the concept of the “job for life” valued by previous generations. Today, people are challenged to build on their current skills, to evolve, re-invent, re-target, if they are to keep bringing value to businesses and situations that are constantly striving for new models and new tools to gain an edge over competitors.
Demands for Skills
What are the most-wanted new skills right now? What roles are emerging in the digital world? And how can individuals ensure their professional development moves in a suitable direction for them to stay relevant and employable?
Among the most powerful technology trends that will determine how Industry 4.0 plays out are Big Data, Artificial Intelligence (AI) and Machine Learning. In addition, quantum computing is emerging from the haze between now and the horizon. Looking into these disciplines helps to discern the roles that make them work. From there, it’s possible to identify the education and experience needed to find a way in.
Work in Data
There is more to making Big Data work than massive number crunching using powerful machines. Human input is needed to create the systems that prepare, store, and protect the data, and make sure it’s presented in a usable way to data analysts and data scientists. This is the role of data engineering. As enterprises large and small become increasingly reliant on data, that role is increasingly important. Data engineers’ roles include building data architectures, streamlining data processing, and maintaining large-scale data systems. While typical language skills include Python, Shell, SQL, and Scala, proficiency with cloud and big data tools such as AWS Boto, PySpark, Spark SQL, and MongoDB is also required.
Data scientists are responsible for working out the right approach for analysing the data. Often having a mathematics, computer science, or economics degree, data scientists have technical competencies encompassing statistics, coding, databases, reporting technologies, using machine learning for predictions, and understanding how data affects business outcomes. Using these skills, data scientists develop data models, simulations, and algorithms, and test the effectiveness of different courses of action.
Data science and data analysis are closely related – and sometimes overlapping - disciplines. Data analysts handle data extraction, storage, cleaning, and filtering, and perform analysis using statistical techniques. Visualisation is another important aspect of handling data, to present the intelligence generated through analysis to the company’s decision makers in ways that can be understood and actioned.
Using data effectively to drive better business outcomes is vital for all companies today, whether they are engaged in the high-tech sector or elsewhere. Accordingly, enterprises everywhere are demanding data analysis and data visualisation skills and, while an appropriate degree-level qualification is a common requirement, data giants like Google and IBM are now offering online Professional Certificate courses that can help people adapt their skillsets and prepare for new opportunities. Courses are available in various IT-related fields, including data analytics as well as user-experience design, IT support, and project management.
It’s worth noting that data-driven business is driving change throughout corporate hierarchies. Not so long ago, few companies would appoint to a role such as Chief Data Officer. However, a high-level post responsible for ensuring data compliance has now become vital and the role continues to change as CDOs are also now expected to take the lead in using data to optimise business outcomes.
Make AI Real
Software solutions based on AI and machine learning have quickly come to prominence in products such as smartphones and wearables, assisting features such as activity tracking and photography. Moreover, they are at the heart of cloud applications supporting social media, directing business processes, and handling complex scientific tasks such as rapid sequencing of human genomes.
The commercial development of AI is in its infancy. There are opportunities at almost any level, from creating optimised FPGA processing engines, to developing new algorithms, and writing AI applications. While neural networks have proved incredibly successful addressing tasks such as image classification, speech recognition, and natural language processing, alternative machine-learning algorithms like support vector machine and decision trees can address other classification tasks and anomaly detection more effectively and need to be explored.
AI has been extensively deployed in the cloud, and flexible models such as pay-per-use have created a market for independent AI application developers to monetise their expertise. On the other hand, there is also demand for lightweight AI applications and inference engines that can deliver deterministic or real-time performance in embedded and mobile platforms that face tight constraints on power consumption and CPU cycles. This calls for expertise with compact, embedded machine-learning platforms like TinyML.
One of the most senior roles in the field is that of AI Architect, responsible for selecting the right technology for a particular project and leveraging knowledge of tools and technologies within the AI industry to realise a suitable solution. Engineering AI-based solutions can involve building business tools that help organisations improve their processes and services, such as airlines, hospitals, banks, or health authorities to enhance programs such as disease mapping. On the other hand, lightweight embedded AI applications are being developed to run on IoT devices or in automotive systems like pedestrian detection.
And, of course, there are opportunities for experts in industrial processes and business processes to provide high-level guidance as to the types of AI applications that are needed, the desired outcomes, and the way users will interact with them.
Any organisation that can benefit from identifying patterns hidden in data can benefit from AI. IBM’s Global AI Adoption Index found that almost one third of companies are deploying AI, while about 50% are exploring the opportunities: a position that suggests there are many opportunities available now and many more to come. The IT professionals surveyed indicated that lack of AI skills is a common challenge. Clearly, there is demand.
The education requirements to get into AI can be high, such as a master’s degree in computer science, data science or an AI-related discipline. Knowledge of applicable programming languages, particularly Python, is typically needed, and experience of implementing machine-learning solutions. You can dip a toe in the water by engaging with some of the AI ecosystems now emerging for embedded development with microcontrollers from manufacturers such as NXP, STMicroelectronics, and Beagleboard.
Commercialise Quantum Computing
Quantum computing is currently entering the transition from research into early commercial businesses. Quantum computers are in the cloud now, including prototypes built by the big computing players like Honeywell, IBM, and Google, as well as small, focused startups. While there is a long way to go in hardware-development terms, to make quantum computers more user friendly and stable, the teams responsible for putting these early models out there are encouraging end users to start engaging now with quantum computing in the cloud to help shape their development.
Now entering the early stages of commercialisation, quantum computing groups including incubators and university spin-outs are starting to look for roles such as a business development manager to begin seeking out opportunities where the group’s expertise can be used to generate revenue, further the development of quantum applications, and grow contacts among potential future customers as the technology becomes more and more usable. Now is the right time for building connections with government bodies and enterprises, performing the role of technology evangelist, commercial ambassador, and salesman simultaneously. A technical understanding is essential, to identify suitable opportunities, explain the advantages a quantum solution can offer, and win commitments from prospective customers and partners.
Cyber Security and Blockchain
In the connected world, there is an ongoing and increasingly important role for cyber security. As smart technologies pervade everything from industry to smart infrastructure, smart cities, and smart living, the opportunities for hackers are increasingly enticing and the consequences of their actions can be more serious.
Enterprises have needed computer security specialists to protect their IT systems for many years, and their requirements are now extending into protection for operational technology (OT) equipment within the enterprise as well as the growing diversity of assets connected to the IoT. Cyber security for IoT devices is an emerging and urgent need (industrial cyber security was covered recently with Phoenix Contact). From designing hardware-based security features at the silicon level to the work of standards bodies in defining best practices and establishing test methodologies, there are opportunities for security analysts, architects, engineers, software developers, mathematicians such as cryptography experts, and white-hat hackers hired to penetrate systems and report the weaknesses they discover to the owners. It’s a continuous and constantly changing battlefield, as the security industry is always in need of stronger policies and ciphers that hackers will never stop trying to break.
The next step is to leverage AI expertise to create algorithms capable of spotting the signs of hackers at work to help organisations respond quickly to their attacks. Using AI’s ability to detect connected assets that are behaving abnormally, it becomes possible to quickly isolate or shutdown equipment at risk to minimise or contain the potential damage.
Another emerging role that relies on cryptographic know-how and associated software expertise is that of blockchain engineer. Infamous as the underlying technology for cryptocurrencies, blockchain has a big future in enabling national digital currencies as well as applications in private enterprise such as securing transactions and automating access controls.
Find Your Path
Some of the opportunities now emerging are too new to have specialised undergraduate degree courses associated, although there are opportunities to take a master’s degree in subjects such as cyber security, for example.
Massive open online courses (MOOC) are a modern Internet-based phenomena that offers an unrivalled breadth of study opportunities, often available free of charge and offering the flexibility to study at any pace. Courses may be provided by commercial companies, perhaps for altruistic reasons or for marketing purposes, or by academic organisations aiming to offer a taste of higher education. Although not formally recognised, they can provide an introduction to a subject that could lead to various opportunities with a new or existing employer.
These – and, of course, established self-study routes such as The Open University – can be effective whether you want to adjust or radically change your career path. You may be surprised at what you can find with some well chosen Google searches about emerging digital careers.