The new era of technological brilliance, innovations, inventions, and emergence of devices, applications, software and tools. Today, in the ever-changing day and space, the rise of analytics, data, sciences, machine learning, the Internet of Things, Artificial intelligence and other data-based technologies have spread their tentacles in different industries and revolutionised their way of operating and conducting their business. To thrive in such an advanced and nuanced space that keeps on bringing in new opportunities, digital pathways, and other getaways, one needs to understand these latest developments in the technological world.

With the advent of these technological procedures, nearly all the tasks can be automated by delivering precise results meeting the expectations of companies.

The brilliance of different fields such as digital marketing, finance, and others and their amalgamation with data sciences and artificial intelligence/machine learning allows professionals to gather insights about their strategies, competitors, and position in international markets, seeking new opportunities challenges.

Industry 4.0: The New Dawn of Technological Advancement

The rise of data-based systems for tracking and monitoring business procedures demands collaborative tools that can work on different interfaces and computers and allow several project members to work together for various projects.

Some of the best jobs in analytics are in the fields of information technology. IoT and other departments that utilise nuanced software and tools for gathering proper intel about their corporate infrastructures. The by-product of all these new and innovative pathways is designed to increase their organisations’ ROIs, profits, and revenues.

Combining industry 4.0 tools and technicalities with existing workflows, schemas, and protocols defined by organisations can help streamline operations, develop result-oriented strategies, better insights about how their corporation is performing, and other aspects.

The role of each and every industry 4.0 technologies- be it IoT, AI, ML, analytics, or data sciences- can help create better policies, survey, analyse and evaluate data trends, and deploy methodologies that can lead to successful outputs.

Industry 4.0 is the recent revolution in terms of technological transformation that is reforming the way organisations are producing their goods, services and end products.

Going over history books and looking at the different industrial revolutions, one can figure out how during the late 1800s the mechanisation was done via steam power and water to incorporating the usage of mass production and assembly lines in the second stage of the revolution to current utilisation of computers, their interconnectivity and focus on automation of processes.

The modern world of digitalisation offers the fourth instalment of the industrial revolution. The upcoming jobs in digital marketing are shifting how traditional business practices are being performed in the current digital spectrum.

Today humans are on the lookout for ways to reduce their workload and synthesise procedures that can perform redundant tasks. The world of machine learning, AI and analytics work on nuanced algorithms that identify the associations, trends and similarities between data sets that lead to quick and precise results.

Understanding the Main Technologies of Industry 4.0

As described above, industry 4.0 is an umbrella term given to different recent technologies that have thronged corporate spaces in the past few years. These include analytics, data science, artificial intelligence, machine learning, the Internet of Things, and other designated digital finance and marketing sectors. The main objective of Industry 4.0 is the computerisation and optimisation of its previous version- industry 3.0. The new face of the industrial revolution looks to fill the gaps and loopholes made by the last technical advances.

Given the scalability, exposure and interconnection of computer systems, today’s scenario presents the right opportunity and environment for Industry 4.0 to take centerstage. The combination and better interconnection between different computer frameworks have facilitated the Internet of Things and data science to co-exist together and make intelligent factories, an upcoming project, a reality. With end-to-end connectivity across platforms, there’s more access to data for devices, better management of devices and applications, and deployment of cloud-based storage systems for securing fragile and private data.

Data encapsulation is critical, and network engineers prioritise keeping data safe from leakages, cyber threats, and other hazards.

“The Fourth Industrial Revolution is still in its nascent state. But with the swift pace of change and disruption to business and society, the time to join in is now.”

The quote mentioned above by Gary Coleman of Deloitte Consulting sheds light on the importance of the introduction of the fourth industrial revolution in technological advancement.

Given how the current businesses and society, in general, are utilising data streams regularly, it’s vital to make a long-term plan for delivering better services, products, and software that can help make lives easier and offer information access across different paradigms.

The digital connectivity, access and delivery of information is the primary concern of industry 4.0 to make nuanced processes for solving consumer issues. Moreover, the convergence of IoT with data sciences, machine learning, and artificial intelligence can lead to developing a comprehensive platform that can help control information, data, and other digitised mechanisms.

Today, several industries favour industry 4.0 simply because of the several benefits it has made already in the fields of digital marketing and finance, with more advancements to go in the coming years. Those graduates who have just finished their bachelor’s degrees and aim to formulate a career in the technological aspects must be aware of the subtleties of these different technicalities.

Enrolling on credible courses offered by several elite universities across the country can prepare these executives about the vitalities that constitute industry 4.0. Moreover, doing so will make them ready to seek machine learning jobs, amongst others. The following sections highlight the several technologies that comprise the industry 4.0.

Machine Learning: Critical Part of Industry 4.0

The innovation for developing a machine learning model started when humans realised that specific tasks are mundane and routine which machines can perform, and they can focus on taking care of other particulars. Of course, the major challenge was to make computers smart enough to carry out tasks by themselves.

It was considered rocket science to make intelligent and intelligent computer networks capable enough to perform tasks on their own and by learning through experiences.

The machine learning world has several elements of automation, research-driven analytics, and other computations for developing sustainable practices that help in the precise generation of results that match the expectations of their end-users. Machine learning has been instrumental in deploying workable models that can get to the root cause of problems and suggest solutions.

Today, machine learning is all about finding patterns from data sets, their associations, and interactions with other aspects. The science behind finding these solutions lies in adequate understanding of the different algorithms that help do those pattern-based searches.

Under the industry 4.0 practices, machine learning plays a titular role in gaining a foothold in businesses and several other factories. Machine learning with its umpteenth levels of optimisation and precision can serve as an avenue for massive progress in manufacturing smart factories, i.e. highly operational ML scenarios.

Today, machine learning has a plethora of devices, machines, technologies, systems, and smart factories that can regularly monitor data and collect different elements for large-scale production. ML can prove to be the primary driver of economic growth for companies that deal with advanced analytics and pattern-based research for carrying out their operations.

Furthermore, the primary reason for an increase in machine learning jobs is that they can lead to better efficiency without affecting the existing corporational resources. By utilising smart devices backed by machine learning protocols, upcoming smart factories can quickly assess product quality and with finesse.

Incorporation of Artificial Intelligence in Industry 4.0

Artificial intelligence is proving to be a real game-changer in the manufacturing aspect of industry 4.0 by industrial automation, new paradigms for business and intelligent technologies that can speed up the results. Today, manufacturers struggle with reducing production losses and preventing processing incapabilities and hence AI comes in handy for clogging the gap between the two.

The implementation of continuous, multivariate analysis by taking help from machine learning algorithms for getting an in-depth understanding of different processes. With its predictive quality and yield, artificial intelligence that reveals the hidden causes and underlining the reasons as to why perennial production losses occur. Moreover, the concept of predictive maintenance can help oversee any future challenges in manufacturing procedures and can inform the companies about the same.

There are numerous advantages associated with predictive maintenance and can help significantly in reducing costs and arrears. By anticipating chances of failure, manufacturing companies can prepare themselves in advance for the challenges that lie ahead. The different algorithms utilised can help systems to continue without lapses or interruptions.

Predictive maintenance also paves the way for a longer remaining useful life (RUL), the machinery of one piece of equipment, and helps avoid secondary damage for the same constituent parts. Moving, artificial intelligence leads to collaboration between humans and robots.

Industry 4.0 has leveraged a practical and workable cohesion for humans to take the help of robotics to carry out high-level programming operations, designing, documentation, and maintenance.

Currently, the efficiency of human-robot collaboration is being improved massively to develop successful robots that can work simultaneously along with humans.

With the increase in the adoption of robotics and it getting mainstream in industrial operations, artificial intelligence jobs will continue to grow in the years to follow. Artificial intelligence can also be used to optimise the supply chain of manufacturing processes and help them respond better to the changes taking place in international markets.

AI plays a major role in constructing estimations, budgeting, taking care of market demands, and deploying products accordingly.

Such an influence of AI can use this information to streamline inventory control, staffing, energy consumption, and raw materials and make better financial decisions regarding the company’s policies and strategies.

The influx of Analytics in Industry 4.0

The interaction of analytics in industry 4.0 is based on developing pathways that can discover huge data sets to find meaningful insights.

The role of data analytics in the curreent industrial revolution 4.0 can be envisioned from the need for sensor data to predict exactly how and when one needs to have maintenance and repair operations for their machines. Big data analytics is a critical aspect of industry 4.0 since it looks over the data sources that can be surveyed for understanding the problems.

Manufacturing industries can take the help of big data analytics for increasing the efficiency of their production chains, goods, and services. The intelligent factories can understand their corporational topographies, get a real-time consensus of their implemented systems, and get better insights regarding areas that need further attention.

Data analytics has a primary role in enabling successful and result-oriented data-driven decisions that help foster sustainable practices for their firms.

The focus area of big data analytics in industry 4.0 revolves around collecting, analysing, and ensuring that information received from smart sensors, cloud computing and IoT practices lead to uncovering of patterns. This end-to-end connectivity helps in the better deployment of resources across the industrial supply chains. Big data analytics allows corporations to uncover some hidden and often ignored truths that are the main reasons for bottlenecks in production and supply chains.

These mechanisms shed light on the variables and other factors that lead to such hindrances and work towards clearing them. Once the source of issues has been identified, they then need to be dealt with using targeted data analytics to get to the root cause of problems. Incorporating successful issue absolvement leads to terrific returns for firms in terms of increasing effectiveness of procedures, more significant revenues and profits.

Studies have shown that implementing big data analytics in industrial operations reduces unscheduled downtime and breakdowns caused by external factors by nearly 25 per cent.

Big data analytics leverage tools and software that help in the robust production of goods and services. Moreover, analytics plays a centric role in maintaining reasonable overall means of production and keeps every right on track for achieving objectives set up by the firms.

The several benefits of big data analytics include-strengthening real-time performance, optimisation of supply chains, price and cost optimisation, prediction of faults and anomalies, product development and implementation of smart factory designing.

The utilisation of Data Science in Industry 4.0

The richness of data and its incorporation in industry 4.0 helps businesses with its different procedures, work-flows, techniques, problem-solving and other factors. Be it leveraging predictive, prescriptive, descriptive, and any other models for analysing various issues and rectifying flaws within the system or using data-driven methodologies for getting an in-depth understanding of supply chains, the role of data science is vital for industry 4.0 to become a reality.

Ushering in a digitalised space wherein there are specific guidelines that need to be followed for garnering success. Data science offers all the rudimentary elements that help young and aspiring data engineers to attract the best jobs in analytics, machine learning, and other areas. Given how the world has produced an astonishing 2.5 quintillion bytes of data in the past year highlights the importance of developing data-backed systems in manufacturing settings.

However, this data accumulated is from raw, unfiltered sources and hence unstructured. It needs to be treated and refined as per organisational standards for generating insights, subtleties, and associations between other data sets and other facets. Today, the cutting-edge technologies utilised by industry 4.0 have revamped the entire workable models in companies, which could lead to even better devices, gadgets, and other applications for giving consumers different experiences.

Data science procedures are heading the heart of evolution in the new digital transformation space, and it’s the revolutionary assimilation of tools. Data science as a collective technical aspect consists of machine learning, artificial intelligence, data analytics and analysis practices that drive the growth and success of organisations. These methodologies help in the in-depth surveillance of data streams.

The influx of data sciences today acts as the basis of industry 4.0 and provides a different set of procedures, protocols, mechanisms, and other features that help in canvassing successful production of goods, manufacturing of end-products, and keeping the supply chains connected together throughout the course of implementation of data-backed techniques.

As per a 2020 report presented by European Patent Office Study in 2020, the past three years witnessed a staggering 54% increase in the number of patents filed for industry 4.0 alone that helped in introducing new machines, software, tools and data-based models for gathering intel, streamlining schemas and optimising different areas of organisational planning. 

The stat mentioned above shows the utter dominance of data sciences in developing robust, efficient and successful data mining practices that can help in solving complex issues, render meaningful insights for the data possessed by a particular organisation and help in giving a practical form to industry 4.0.

How Does the Internet of Things (IoT) Gel with Industry 4.0?

Internet of Things (IoT) refers to the modern platform of digital devices, servers, and applications that are making a singular standardised way of managing data in the new and innovative era of data harnessing.

• So how does IoT fit in the Industry 4.0 setting?
• What is the correlation between IoT and industry 4.0?
• How can IoT make industry 4.0 a success?

Those newcomers that have recently joined the data science careers, machine learning jobs and other domain-specific roles need to understand the role and importance of IoT in industry 4.0.

It’s no denying that both IoT and industry 4,0 are the hottest buzzwords in the information technology world going around. However, there’s a fine line that separates the two.

While IoT acts as a repertoire of devices, applications, software, and servers, the industry 4.0 provides the right environment for the installation of these end products and services. With industry 4.0 focusing on generating enormous amounts of data, it would need several tools for gathering, sorting, analysing and processing mechanisms that lead to successful outcomes.

The Internet of Things (IoT) focuses on revolutionising and enriching industry 4.0 by enhancing the productivity of manufacturing technologies through data collection, which helps in devising adequate real-time workable solutions for solidifying procedures that are a part of industry 4.0.

There are several objectives laid out by IoT experts for making industry 4.0 a success. They include- efficient management and monitoring of updates and amendments that are required in the production process and taking care of the workload.

Moreover, IoT engineers are developing systems that can facilitate the work of QIs and LQs in different corporational departments by adopting mobile and remote devices. Next up, IoT aims at optimising and speeding up the data collection phases, production timings, and other aspects. In order to make these theoretical plans a success, IoT engineers have established an innovative and robust Web-based application software that is a cross-platform for integration of IT and ERP systems across different PCs, mobile tablets, smartwatches, and others.

IoT is also looking to increase the efficiency of the production planning stage of industry 4.0. It aims to do so by continual analysis of data sets surveyed by different software and bringing the production planning department to develop new and improved methods for planning and implementation of work-flows.

Different Technologies make the Best out of Industry 4.0

The different and varied technologies mentioned above have unique characteristics, highlights, features and other descriptive aspects. As seen from the plethora of technologies that make up the world of industry 4.0, they both bring their own elements into the mix and strengthen industry 4.0 to carry out its operations, responsibilities and other aspects.

The data-based technicalities form a basic part of the furnishing of industry 4,0 since they deliver those critical mechanisms that help in the development of smart factories, production houses, and manufacturing environments.

The engineering and synthesis of these technologies, namely- AI, ML, IoT, data science and analytics, render their key facets of treating data with their own systems and enabling the industry 4.0 to come together as the main driver of economic growth and success for upcoming businesses, industries and organisations. The results obtained from such minute observation, evaluation, and monitoring of data lead to collective practices that help change domains such as digital marketing, finance, and other areas.

Moreover, other positive impacts are associated with the deployment of industry 4.0 in healthcare environments that will lead to longer lifespans, better and affordable healthcare to all societal groups and backgrounds, reduction of diseases, better prediction of upcoming pandemic, and cases of flu and other viruses.

Each of these technologies that form a part of industry 4.0 add their value to the user experience, leverage practical and achievable methodologies that can assist engineers and data scientists in getting to the crux of problems and deliver better results. The digital revolution that has come up in the last few decades has changed consumers’ needs, demands, and concerns.

Today there’s no business operation, task, process, and a basic function that doesn’t require the utilisation of data (past or present) for displaying outputs. Hence, the cohesion of various data-faceted technologies can have a huge impact on the success of industry 4.0.

The technology associated with industry 4.0 is obtained from different sources that help set up acute and precise business intelligence measures for performing tasks as expected. There’s an amalgamation of artificial intelligence procedures that can study different neural networks that are rendered by machine learning to create intelligent software for analytics. Once these have been built, IoT systems come into play for spreading them across a broad group of applications and servers.

Therefore, now is the best time to seek a career along with the data-driven technologies and carry out the revolution envisioned by industry 4.0.

Careers: Best Jobs in the Era of Industry 4.0 (Analytics, Data Science, AI, ML, IoT, Digital Marketing, Digital Finance etc.)

The world as we know today is far away from steam engines, excellent plumbing systems, spreadsheets for managing different procedures and other aspects. Technology is a golden world that gets rusted in an instant once that very technological practice gets overused. However, with each new and improved technology, there are several opportunities to learn, grow and capitalise on, industry 4.0 is no different.

In the latest installment of the technological revolution, industry 4.0 provides a plethora of growth and successful propositions for able applicants. Today, there are several career options for new and emerging data engineers to choose from; the following sections sift over the best career options in different technological fields for industry 4.0.

Best Jobs in Analytics

Big data analytics requires an in-depth understanding of data-mining particulars, data visualisation, and a grasp of programming languages. Doing so will make upcoming professionals ready for acquiring the best jobs in analytics.

Data Architect

They are entrusted with the task of creating blueprints for data management systems. Data architects identify structural and installation solutions that can help in increasing data quality, accessibility, and security.

The average income of data architects falls somewhere between 10 lakhs to 17 lakhs.

Data and Analytics Manager

Data and analytics managers coordinate different tasks completed by their team members for completing a big data project. They communicate with several departments of a company to strategise and aling different goals and objectives together.

A key requirement for thriving in a data analytics role is exceptional business and interpersonal skills. The average annual income for data and analytics managers in India is around 11, 48,720 INR.

Data Scientist

Data scientists are amongst the most in-demand and high paying career roles in the job market today. These individuals are entrusted with the role of maintaining scalable data pipelines and building new API integrations to support ever-increasing data volumes and complexity.

Apart from identifying trends found by ML software, these individuals also need to write code/s for data analysis. They earn around 10-12 Lakhs INR on an average.

Moving on let’s take a look at career options in the field of data science.

Best Jobs in Data Science

The inflow of data streams from many sources has paved the way for several career options in data science in today’s digitalised world. As more and more firms adopt new and advanced systems for dealing with data, they look out for capable applicants that can fill the top-paying job roles.

Data Analyst

Data analysts are responsible for playing with data and forming consensus based on numerical reasoning for surveying the company’s performance. The job role of data analysts revolves around assigning numerical values for the critical business functions.

Moreover, data analysts are entrusted with making long-term deductions based on numbers for making well-informed decisions.

Business Intelligence Analyst

Business intelligence analysts are experts in programming languages, specialised business intelligence tools, technologies and systems. They are entrusted with identifying critical business priorities and requirements that can help better ROIs and profits for companies.

Furthermore, these individuals define KPIs, implement data warehouse strategies and other business intelligence solutions. Their average income per year is around 6 Lakhs and can vary depending upon the projects they finish.

Quantitative Analyst

Quantitative analysts are entrusted with the task of applying mathematical and statistical methods to financial and risk management problems. Several areas employ hardworking and successful quantitative analysts, such as investment banks, asset managers, hedge funds, private equity firms, and insurance companies.

They oversee several metrics and help in identifying profitable investment opportunities and management of risks. Their average salary is around 9 Lakhs per annum.

Moving forward, the next area of industry 4.0 is artificial intelligence.

Best Jobs in Artificial Intelligence

Over the past few years, a sporadic rise in artificial intelligence jobs, career opportunities, and technological advancements has been made.

Some of the top-paying career roles include the following:-

Research Scientist

Research scientists are responsible for looking after different domains of AI such as computational statistics, machine learning, deep learning, and other areas of applied mathematics.

To get a good-paying role as a research scientist, one needs to have a proven experience of working with graphical models, natural language processing, different models and other reinforcement learning. The average income for research scientists is somewhere between 9 and 11 Lakhs per annum.

Algorithm Engineer

These engineers look after the development of algorithms that can be managed by the core group of AI experts and record all operations. Furthermore, an algorithm engineer is concerned with the designing, development, deployment of scalable real-time systems.

Algorithm engineers in India earn around INR 7 Lakhs per annum.

Computer Vision Engineer

Computer vision engineers are tasked with solving real-world problems through computer-based vision/s. They understand different machine learning primitives and apply them in their client projects.

These engineers earn somewhere around 5 Lakhs INR per annum.

Now, let’s see the best jobs in the field of machine learning.

Best Jobs in Machine Learning

Machine learning today has become one of the most professionally rewarding and learning-oriented careers. One needs to learn a lot on the job and display certain skill-sets to attract high-paying job offers.

Here are some of the most lucrative career prospects in the field of machine learning today:-

Analytics Director

The analytics director is a senior role that involves mentoring and assigning tasks to team members to develop projects. They are tasked with organising technological, financial and human resources that cater to the business needs of organisations.

It’s a decent career role and pays around 3 Lakhs INR per annum.

Principal Scientist

It’s one of the high-paying roles in the machine learning field in the job industry today. Under this career role, scientists are responsible for conducting research in laboratories, coming up with novel solutions for innovative and high-impact data science projects, amongst other tasks.

Principal scientists need to coordinate with stakeholders and lead different cross-functional teams for achieving the objectives of projects. These individuals earn a whopping salary of around 16 Lakhs per annum.

Statistician

Statisticians are entrusted with the task of analysing qualitative data and prediction of potential trends that are taking place in the machine learning world. Moreover, they are concerned with turning data-driven problems into questions and hypotheses for arriving at conclusions.

Their average income in INR per year is somewhere around 5-6 Lakhs per annum.

The following section gives insights about well-paying careers in the IoT sector.

Best Jobs in IoT

The Internet of Things has been amongst the main accessors for leading software development in the past five years. Moreover, today it offers a set of comprehensible and integrated systems, networks, and other facets.

There are certain career roles that offer great salary packages:-

Cloud Engineer

These engineers are competent in their roles of looking after complex cloud-based software designing, storage systems and ensuring that data retrieval is done in a structured manner.

Cloud engineers need to be well-equipped with IoT basics that help them in establishing and deployment of middleware and NoSQL databases for collection of data from different sources. Their average salary is around 11 Lakhs per annum.

Materials Specialist

A materials specialist is someone who is responsible for looking after different nuanced materials that can help with information collection, deployment of different tools and techniques that help in making IoT reliable software.

Moreover, they look after different supply chains for procuring IoT materials that suffice their organisation’s network structure. Their average salary package is around 10 Lakhs per annum.

Data Scientist

Data scientists are entrusted with the tasks of looking, understanding, and filtering data sets that can be used for solving problems faced by organisations. They need to collaborate and work with different teams from IT and business domains.

Their average income is 8-9 Lakhs per annum.

Best Jobs in Industry 4.0 : Make Data a Priority

As reflected by different career paths described above, industry 4.0 relies heavily on data and its different technologies for finding insights, patterns, and solutions that solve complex issues and challenges that a specific organisation faces. The several career paths described in earlier paragraphs show how the different sectors- AI, ML, IoT, data science and analytics come together to change the workable corporate models.

The advancements in the current digitalised world have been possible because of the computing revolution and digital transformation. These coming of age data-driven technologies have given a new meaning and perspective to existing corporational procedures, work-flows and junctures.

“What is the calculus of innovation ? The calculus of innovation is really quite simple : Knowledge drives innovation, innovation drives productivity, and productivity drives economic growth.”

The quote mentioned above by William Broody sheds light over the need and crux of innovation and its interlinking with several other factors. Innovation and evolution requires an idea, a spark that generates a series of events and takes the world forward. The same goes for the recent industrial revolution in terms of technology advancement. Therefore industry 4.0 offers the best jobs in analytics, careers in data science and other respective fields.

More Information:

Career Prospects in Machine Learning

How to Become a Data Analyst in 2022?

What does a career in Data Analytics look like?

Prosperous Data Science Careers in the Digital Age

IIM KASHIPUR Executive Development Programme In Strategic Marketing Management