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The Top Technology Trends and Their Impact on Data Science, Machine Learning and AI

An action plan for you and your career

Image by Gerd Altmann from Pixabay
Image by Gerd Altmann from Pixabay

2020 was an incredibly unique year.

On the one hand, we have all the lockdowns, are exiled working from home, can still not meet our friends in person, and are close to becoming lonely. On the other side, we could experience the most significant digital acceleration ever.


As a data person, you stand at the forefront affected by this revolution. While you must ensure your deep technical fitness and progress, it is also vital that you are not losing track of the big picture. So, you should know the technology trends and how they impact your work. That you can stay relevant for the future Data Science, machine learning, and AI job market, you need to set the course today.

That holds for the entry levels as well as for the experienced practitioners.


The digital revolution of 2020 is mirrored in the upcoming trends. And in my opinion, that is right.

One of the most reliable sources for technology trends is Gartner.

Because their opinion is mostly congruent with my personal expectation, I take the Gartner Top Strategic Technology Trends for 2021 as the bases for this article. Gartner lists 9 Top Strategic Technology Trends for 2021 under three themes.

In my opinion, these trends will shape the next decade to 2030.

In this writing, I give you:


Important advice: you will never be able to cover all skills needed for all trends. So, you have to decide on a direction you want to follow. Either you are already working in that area and want to further pursue your career or decide on your focus on a trend.

How to select a trend?

I propose to look at the four criteria: your technical background, your interest, the job availability in your preferred location, and your possible time investment to move into a particular area. Assess them and then choose one or two trends for your focus and take action stated at the corresponding trend.


The first block of trends falls into the people centricity field. Over the last several months, and despite the digitalization disruption, we all experienced that people are more critical than ever. We are at the center of work, interactions, business, data, and decisions. Everything became interconnected and will continue to stay here with the people as the connecting node.

1. Internet of Behaviors

What it is: According to Gartner, "The Internet of Behaviors (IoB) captures the "digital dust" of people’s lives from a variety of sources, and that information can be used by public or private entities to influence behavior." It is the data from all sources where people interact commercially, publicly, in social media, and the technology that makes sophisticated insights possible. The information is a powerful tool to influence and nudge our behavior.

My opinion on the impact: This trend is one of the main drivers of our work. The demand for data scientists, Machine Learning engineers, and AI experts is tremendous for bringing all the data together and extracting the information. Already now, it isn’t easy to find people with the relevant knowledge and skills. I am currently working for my day-by-day job on an extensive report about that topic and had more than 60 interviews globally with tech companies, corporates, and thought leaders in this field. Data and technology are not standardized. Corporates are launching their first behavioral-driven products, while capabilities are limited to only a few people within a company. Not only enormous numbers of data scientists and engineers are urgently needed across the various industries, but product designers, behavioral economics experts, strategy people, lawyers, consumer coaches, and so on. The field is in an early maturity stage and will dominate a new business paradigm over the next decade.

My advice for action: If you want to have a "safe job" for years, move into this area. It is also a perfect entry topic for aspiring data scientists, and engineers, or people with currently no technical background. Acquire skills like bringing a massive amount of non-standardized data in real-time together, (near) real-time analytics, signal and image processing, and natural language processing. Gain in-depth knowledge about behavioral economics, data privacy, and methods and become Internet of Things literate.

2. Total experience strategy

What it is: Gartner describes total experience as the combination "of traditionally siloed disciplines like multiexperience (MX), customer experience (CX), employee experience (EX) and user experience (UX), and links them to create a better overall experience for all parties." It streamlines and optimizes what we have experienced over the last months because of COVID-19. The traditional segmentation of work, home, shopping, safety, health, and consumption disappeared. Working from home combines all together.

My opinion on the impact: Again, it has an enormous impact on our work and career. There will not anymore be "a customer analytics data scientist," "a user experience designer," "a business person," or "a machine learning engineer." The areas amalgamate, and multidisciplinary teams become the new standard. The delivery of the right experience to people becomes more complex, and so the data science work and required methods. On the one hand, you need to acquire more technical skills. On the other hand, you need to become more generalist. It is a challenging pathway.

My advice for action: Learn new skills. Technically, acquire knowledge in advanced methods like knowledge graphs, big data processing, sparse representation, recommendation analytics, and computer vision. But more critical, enhance your business and communication skills. Data scientists and machine learning experts that can bridge the complex technical methods with the business value to deliver the experience are in demand, and it will accelerate your career. It starts from Powerpoint skills to speechwriting, data visualization for non-technical people, to storytelling.

3. Privacy-enhancing computation

What it is: Privacy-enhancing computation is about protecting data while using and processing it. There are three types of technologies: a secure and trusted environment, including trusted hardware, secure processing, and the anonymization and encryption of the data before using it. Having the data continuously ready for privacy-enhancing computing is an increasingly in-demand desire and a requirement for companies and organizations.

My opinion on the impact: The times where you as a data scientist can just work with data are increasingly over, not only because of strict regulations you have, e.g., in the EU, California, or China. It becomes a best practice in companies and a competitive advantage. I have several discussions per week about the technologies and methods behind that. Most sectors and companies work on them, and it will be the new standard for all data science and AI work and the profession.

My advice for action: There are three options for your career, and the corresponding actions: 1) As a general data scientist and data expert, you need at least the general knowledge of the regulation, best practice, and data protection and encryption methods. Acquire that knowledge. 2) With the demand for such Technology, such experts’ demand grows exponentially, and the market has already not enough such experts. On Indeed.com, or LinkedIn Jobs, you can find several thousand jobs in that field that requires a technical background. Specialize as a data scientist in that area. Acquire skills in federated machine learning, privacy-aware machine learning, differential privacy, homomorphic encryption, and synthetic data generation. Because of the enormous demand for such people, plenty of entry-level positions and internships are available that companies can cover their needs. 3) For all non-technical people, there are multiple jobs in that field that deal with the regulatory side and provides the opportunity to enter the data technology field. Look where you fit best and move into that field and develop your career over the next years.


The second theme is location independence. COVID-19 decentralized the actions to the locations where customers, employees, suppliers, and organizational ecosystem physically exist, connected by technology. That requires a technology that supports this new way of business, markets, and living. Companies and the public sector have upgraded the systems during the last months and continue to do so.

4. Distributed cloud

What it is: Distributed cloud not only means using cloud options on different physical locations but pushing the executions to the points of need. With the upcoming 5G and new chip technology, execution is shifted to the network’s edges, the so-called mobile edge computing. The rising adoption of the Internet of Things urges distributed services, and smart cities call for metro-area community clouds, i.e., the "distribution of cloud services into nodes in a city or metro area connecting to multiple customers."

My opinion on the impact: The distributed cloud is the future, and the technology has reached a high quality, secure, and convenient feasibility state. But we stand still at the beginning of the adoption, and during the next 10 years, all industries and public sectors move to the distributed cloud. The impact is that the whole data science work is moving entirely into distributed cloud solutions, but the execution needs sophisticated machine learning algorithms. So, the effect is twofold: how data scientists work and their contribution that it works effectively.

My advice for action: Again, there are two opportunities for action. 1) Pushing the execution to the edges needs new technical skills. TinyML – tiny machine learning to make deep learning possible at the edges, and automated machine learning (AutoML) to effectively maintain the systems are crucial skills. Further, at least one cloud-related certificate track of AWS, Azure, and Google is a must.

2) But you can also think of a career change. Many cloud-based tech companies recently had their IPOs or will have them. On LinkedIn, you find more than 20’000 open jobs for cloud architects in the U.S. and more than 10’000 in Europe. According to Payscale, the average Cloud Architect salary is 127k, compared to 96k of a data scientist. So, moving into a cloud architect and engineer career is the second option. And again, there is a lack of these skills in the market. People without a technology background can now take the opportunity to invest in their skills and transfer into this field. I give the resources for learning at the end of this article.

5. Anywhere operations

What it is: Gartner describes it as "Anywhere operations refers to an IT operating model designed to support customers everywhere, enable employees everywhere and manage the deployment of business services across distributed infrastructure. The model for anywhere operations is "digital first, remote first." But it is not just working remotely. It is a seamless and scalable experience, providing workstream collaboration with smart workspaces, secure remote access, distributed cloud, and automation of support – a smart operations experience.

My opinion on the impact: Data scientists will mainly be users of such infrastructure, but that allows them to work in an effective collaborative team independent of the physical location. It counts less your location availability but the skills that you bring into a company and a team. That also means, on the one hand, that your job competitors are now globally. On the other side, the opportunities for you are global, too. And as long there is a worldwide lack of these skills, I doubt that this will lead to pressure on the salary. Skill-based hiring – technically and non-technically – will gain importance.

My advice for action: You need to position yourself in the market for specific skills of demand. That can be knowing certain advanced methods like computer vision, TinyML, applications for a particular industry, an additional programming language like Go or Rust, or experience in special topics like explainable AI, combined with communication and presentation skills. By building up your brand and act as your entrepreneur, you get success and freedom in the job market. Already today, leading experts are approached and hired based on their portfolio on GitHub and not on technical interviews. I predict that this becomes the future standard. So, besides becoming an entrepreneur, start to build up your portfolio on GitHub, where you show your contributions to specific topics. You do not need to be already an expert. No, it is more important for beginners to start working on your brand and develop it over time. That opens up a new opportunity to enter this job market with a completely different background, or for women who want to transfer into that field.

6. Cybersecurity mesh

What it is: "The cybersecurity mesh is a distributed architectural approach to scalable, flexible and reliable cybersecurity control. COVID-19 has accelerated an existing trend wherein most assets and devices are now located outside traditional physical and logical security parameters. The cybersecurity mesh enables any person or thing to securely access and use any digital asset, no matter where either is located, while providing the necessary level of security."

My opinion on the impact: Like the trend before, it enables data scientists and machine learning engineers to work securely from anywhere. So, the effect is the same as before.

My advice for action: The actions are the same as in trend no. 5 anywhere operations. Also, it opens the chance for a career change. Cybersecurity experts are in high demand. On Indeed.com, more than 20’000 open positions are found in the U.S., of which 2’000 remote jobs, and many of them on entry-level or internship for people with no corresponding background. According to PayScale, the average salary of a cybersecurity expert is 90k. I know that is not directly related to a data science job. But it opens up an entry point. More and more machine learning algorithms have become an integrated part of cybersecurity. A coworker with a background in economics and regulatory affairs does in parallel to his work and on his own pace the online master in cybersecurity at Georgia Tech, focusing on the integration of analytics and machine learning. He is using it to transfer into the data technology field.


The last block is called resilient delivery. Resilience means "the ability of a substance to return to its usual shape after being bent, stretched, or pressed." While companies focused in the past years on optimized, efficient operations, COVID-19, and the current recession hit them hard in their fragile processes. So, technology-driven resilience is the new focus to recover fast.

7. Intelligent composable business

What it is: While rebuilding the business and processes, a design that enables better access to information, augment it with new insights, is composable, modular, and can change and respond more quickly to decisions and disruption is needed; a so-called intelligent composable business. The focus is on the autonomy of decision making, the democratization of applications, and business capabilities. The plasticity of a company is key.

My opinion on the impact: That description of the trend is a bit abstract. My interpretation is the following: During phases of change, people and organizations must be enabled to make real-time, relevant, and contextual business decisions. That cannot be done anymore with centralized decision-makers. With relevant data and insights, the decisions must be made decentralized, and nearly simultaneously, the capabilities must adapt to implement them. So, people in an organization need to be empowered for that. The impact will be that everybody in the organizations should be a citizen data scientist, "a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics." On the one hand, people from outside of the classical data science tracks enter and perform these tasks together with a lot of automation.

On the other hand, data scientists need a clear differentiating profile to be recognized as experts to develop and implement advanced applications. The data science end-to-end process will be more fragmented by automation, citizen data scientists, and specialized data scientists. Business decisions and communications skills of the data scientist become more critical than ever.

My advice for action: Data science automation will evolve. So, you as a data scientist, make sure with education in advance topics that you stay relevant. Start with advanced training now, and achieve especially cloud-related certificates or specializations. Citizen data scientists will perform mid-level complex tasks. Also, get trained in business decision making and communication.

Second, for not yet data science people, it opens up many entry opportunities. You should start with data science foundation education and sound business analytics skills. You do not need to be a coding expert but should be able to work with tools like R or Tableau.

8. AI engineering

What it is: According to Gartner, the performance, scalability, interpretability, and reliability of AI models need robust AI engineering. "Without AI engineering, most organizations will fail to move AI projects beyond proofs of concept and prototypes to full-scale production." The three pillars of AI engineering are DataOps, ModelOps, and DevOps. DevOps deals mainly with high-speed code changes, but AI projects experience dynamic changes in code, models, and data, and all must be improved. Organizations must apply DevOps principles across the data pipeline and the machine learning model pipeline.

My opinion on the impact: Currently, still 80–85% of AI projects do not deliver the intended outcome. So, this is a trend where neither the companies nor you do have any other choice. It is a must. Successful tech companies are already working with this mindset. All others need that, too, to stay relevant.

My advice for action: My advice is concise: learn it. Apply it. And use all the corresponding productivity tools that are associated with it.

One last word: DevOps, DataOps, ModelOps, and MLOps are not a tool, not a technology, not a framework, and not a methodology. It is a way, a mindset, a culture, a philosophy of working, and most importantly, learning. Bear that always in mind.

9. Hyperautomation

What it is: Gartner says that "hyperautomation is a process in which businesses automate as many business and IT processes as possible using tools like AI, machine learning, event-driven software, robotic process automation, and other types of decision process and task automation tools." The end-to-end digitalization ensures not only seamless remote work but also digital operational excellence and operational resilience.

My opinion on the impact: More and more companies move to data-driven business models with the need for a fast reaction to the market and customers. The companies are already working on it. Reasons: speed to market, competitive advantages, lack of resources like data scientists, and the dependencies on them. Hyperautomation shifts the tasks of data scientists. They move from low-level business analytics and data analyst work to automation and outcome-oriented tasks. Your duties include end-to-end (quality) controlling and oversight, working with automation tools, full integration into business processes, and providing the corresponding AI and machine learning support.

My advice for action: This trend requires you to develop your skills in two directions. Get familiar with end-to-end platforms (KDnuggets has a summary of Gartner’s Magic Quadrant), working frameworks (see above no. 8), and programming languages like C/C++, Java, Go, Rust, etc. Python is not a language for hyperautomation. Second, understand the business side, what drives customer experience, and learn how to do "oversight" instead of only "implementation." You will be the "air traffic controller," not the pilot.


Connecting the Dots

We are in exciting times for data science, machine learning, and AI. All technology trends need the close involvement of these experts. The trends will last at least a decade and give job security. You never had more choices for your career.

It is also the best time to enter the data science field. The lack of experts in the job market will intensify. Companies move to more entry-level and internship hiring strategies and educate the people internally. That is a big chance for all technical and non-technical people willing to invest in their skills to enter the data technology field.

My advice for action:

#1: Select one or two trends to focus on based on the four criteria: 1) your technical background, 2) your interest, 3) the job availability in your preferred location, and 4) your possible time investment to move into a specific area.

#2: Learn new skills. Data and data engineering trainings are found in section F, and G. Big Data and cloud resources can be found in section H, and productivity tools in J. Business and communication skills educations are found in section L. All the advanced topics are listed in the sections M, Q, and R, and data privacy, explainable AI and ethics courses in sections P and S. Beginners can go from the top to the bottom.

Non-technical people who want to transfer into this field should first start reading about these topics or watch videos on YouTube channels and get familiar with the areas, terminology, and methods. You do not need to be a deep expert in these topics to get started building up your brand and move into this field.

#3: Become your own entrepreneur and start building up your own brand in the chosen niches. Technical people can create and expand their GitHub portfolio. Technical and non-technical people can start writing blogs and articles or give presentations. Or why not start doing this in your local community? For kids? For people over 50? For single mothers to provide them with career perspectives? In Meetup meetings? For people with a non-technical background who want to enter that field? You do not need to be an in-depth expert with 10+ years of experience for that. You need an own sound understanding, a good self-reflection that you are not overselling your own skills, honesty about what you know and what not, and curiosity.

#4: Apply it. Apply it to your current job or look for a position where you can apply it. Take an internship. Give courses. Start a part-time remote job. The job market has a tremendous shortage of these skills, and there are numerous possibilities to start.

And now, start surfing on the waves of trends and have fun!


Do you like my story? Here you can find more.

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The Ultimate Guide on the AI Professional Certificates on edX 2021

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