Data Science Trends of the Future 2022
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Data Science is an exciting field for knowledge workers because it increasingly intersects with the future of how industries, society, governance and policy will function. While it’s one of those vague terms thrown around a lot for students, it’s actually fairly simple to define.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is thus related to an explosion of Big Data and optimizing it for human progress, machine learning and AI systems.
I’m not an expert in the field by any means, just a futurist analyst, and what I see is an explosion in data science jobs globally and new talent getting into the field, people who will build the companies of tomorrow. Many of those jobs will actually be in companies that do not exist yet in South and South-East Asia and China.
Data science is thus where science meets an AI, a holy grail for career aspirants and students alike. Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills.
This article will attempt to outline a brief overview of some of what’s going on and is by no means exhaustive or a final take on the topic. It’s also going to focus more on policy futurism rather than technical aspects of data science, since those are readily covered in our other articles on an on-going basis.
Augmented Data Management
In a future AI-human hybrid workforce, how people deal with data will be more integrated. Gartner sees this as a pervasive trend. For example, augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powerful dynamic systems.
Essentially augmented data management will enable active metadata to simplify and consolidate architectures and also increase automation in redundant data management tasks. As Big Data optimization takes place, automation will become more possible in several human fields, reducing task loads and creating AI-human architectures of human activity.
Hybrid Forms of Automation
Automation is one of those buzz words but things like RPA are really moving quite swiftly in the evolution of software. To put it another way, in terms of data, this has a predictable path to optimization. High-value business outcomes start with high data quality. But with the scale and complexity of modern data, the only way to truly harness its value is to automate the process of data discovery, preparation and the blending of disparate data.
This digitally transforms the very way industries function and do their business. You can see this in nearly every sector where efficiency with data is key. Industries such as manufacturing, retail, financial services and travel and hospitality are benefiting from this trend. The retail industry, for example, has undergone multiple pivots in recent years. What happens at the intersection of consumer behavior with something like DeFi and the future of data on the blockchain?
Automation produces human convenience from the consumer side, but also new machine learning systems that become more important in certain industries. The rise of E-commerce, video streaming, FinTech and tons of other meta-trends in business all depend upon this kind of automation and data-optimization processes.
As data science evolves, AI and machine learning begins to influence every sector. According to Nvidia there are around 12,000 AI startups in the world. This is important to recognize in the 2020s. There’s an AI explosion of potential that will lead to scalable AI and behavior modification at scale in humans adjusting to this new reality.
The new kinds of capitalism around scalable AI can be called augmented surveillance capitalism. This is important because the way we relate to data is transformative. The Internet of Things becomes embedded in all human systems and activities.
Big Data and “small data” unite and as AI integrates with many new and old aspects of society, something new emerges in how we are able to monitor the flow of data in real time and predict for outcomes instantaneously. It doesn’t just create a more connected society, but a living lens into the data of everything that also stimulates innovation, new companies and, potentially, incredible economic growth. Scalable AI is one of the reasons students get into data science. They realize the end game is beautiful and does improve human existence.
Augmented Consumer Interfaces
When I say ACI I mean how the consumer processes data in the shopping of the future, either online or in a physical store. In the future it’s highly unlikely you will be served by human staff. Instead there will be an AI-agent that you relate to or a new form of the interface of the exchange, such as buying and reviewing products in VR, getting a product overview synopsis audially in your ear buds or other augmented consumer interfaces.
The rise of the augmented consumer takes many forms, from AR on mobile to new methods of potential communication such as a Brain-Computer Interface (BCI). All of these interfaces have consumer, retail and global implications for the future of AI in consumerism.
As companies like Facebook, Microsoft and Amazon race to create a metaverse for the workplace or retail space, even what we think of Zoom meetings or E-commerce interfaces of today will be replaced by new augmented consumer interfaces. Things like a video meeting or an E-commerce platform front page may become outdated. This is also because the data they provide may be sub-optimal for the future of work productivity and data based consumerism.
Essentially VR, IoT, BCI, AR, AI speakers, AI agents, chatbots and so forth all evolve into a new paradigm of augmented consumer interfaces where artificial intelligence is the likely intermediary and people live in the real world but also in a corporate and retail metaverse with different layers. As Facebook tries to bring the workplace into its conception of the metaverse, other companies will create new interfaces for ACI efficiency.
Amazon recently announced that it is planning to open large physical retail stores in the United States that will function as departments stores and sell a variety of goods including clothes, household items and electronics. Yet you can expect Amazon’s various physical spaces to also have more consumer data capture to incentive purchasing or even completely automate the store such as famously their smallish Amazon Go Stores. A more seamless shopping experience creates new expectations for the consumer that finally leads to new augmented consumer interfaces, that will just become the new normal.
With data science growing and machine learning evolving, more B2B and AI-as-Service platforms and services will now become possible. This will gradually help democratize artificial intelligence expertise and capabilities so even the smaller entpreneurs will have access to incredible tools.
Platforms like Shopify, Square, Lightspeed and others are heading this direction to enable new small businesses optimized with AI to grow faster. Meanwhile more bigger technology firms are entering the B2B market with their own spin on AI products that other businesses might need.
China’s ByteDance really opened BytePlus which enables a wide variety of intelligent platform services for other businesses. Market leaders like Google, Baidu and Microsoft and Amazon in the Cloud have a significant ability to produce AI-As-a-Service at scale for customers in new ways that are always evolving for the news of industry. The NLP-As-a-Service firms and their progress is also an example of this movement.
The AI-as-a-Service platform growth in the 2020s is as I see it one of the biggest growth curves in data science for years to come generally speaking.
The Democratization of AI
As data science talent becomes more common in the world in highly populated countries, a slight re-balancing of the business benefits takes place in more countries. The democratization of AI will take many decades, but eventually data science will be more equally distributed around the world leading to more social equality, wealth equality, access to business and economic opportunities and AI for Good. We are however a long ways away from this goal.
Wages for data science and machine learning knowledge workers are vastly different in different regions of the planet. A company in Africa or South America may not have the same access to data capabilities, AI and talent as one in “more developed” regions of the Earth. This however will slowly change.
Democratization is the idea that everyone gets the opportunities and benefits of a particular resource. AI is currently fairly centralized however ideas of decentralization, especially in finance, crypto and blockchain may trickle down into how AI is eventually managed and distributed more fairly. AI as a tool is a resource of significant national importance and not all countries have similar per capita budgets to invest in it, in its R&D and in producing companies that harness it the best.
Yet for humanity, the democratization of AI is one of the most important collective goals of the 21st century. It is pivotal if we want to live in a world where social justice, wealth equality and opportunity for all matters in fields such as healthcare, education, commerce and fundamental human rights in an increasingly technological and data-driven world.
The most basic element of the democratization of AI is however that anybody anywhere could become a student of data science and has access to becoming a programmer or a knowledge worker who works with data, machine learning, AI and related disciplines. It all starts with access to education in this sense.
Improved Data Regulation By Design
If datascience is fueling a world full of data, analytics, predictive analytics and Big Data optimization the way we handle data needs to improve and this means better cybersecurity, data privacy protection and a whole range of things.
China’s BigTech regulatory crackdown also has to do with better data regulation. For instance just recently in August, 2021 China has passed the Personal Information Protection Law (PIPL), which lays out for the first time a comprehensive set of rules around data collection.
In augmented surveillance capitalism human rights need to be implemented by design and that means better protection of consumer data especially as AI moves into healthcare, sensitive EMR and patient data. Overall the trend of Data privacy by design incorporates a safer and more proactive approach to collecting and handling user data while training your machine learning model on it.
How we move and build in the Cloud also needs scrutiny from a policy regulation standpoint. Currently data science is moving faster than we can regulate data privacy and make sure the rights of individuals, consumers and patients are respected in the process. Even as data science and Big Data explode, rule of law needs to be maintained otherwise our AI systems and data architecture could lead to rather drastic consequences.
Top Programing Language of Data Science is Still Python
Even though I do not code I’m pretty interested in how programming languages are used and evolving. Python continues to be a defacto winner here. Data science and machine learning professionals have driven adoption of the Python programming language.
Python’s libraries, community and support system online is just incredible to behold and displays how data science is a global community of learners and practitioners. This fosters the collaborative spirit of the internet towards improved data and AI systems in society.
Python as such, is not just a tool but it’s also a culture. Python comes stacked with integrations for numerous programming languages and libraries and is thus the likely entry point of getting into data science and the AI world as a whole. What we have to realize is the programming culture is agile but also highly collaborative making it incrementally easier for new programmers and data science talent to emerge.
Cloud Computing with Exponential AI
The turning point of companies moving into the Cloud is hard to calculate in how exponential it’s leading the emergence of the AI revolution in the 21st century. This is frankly leading to an astonishing array and improvement in the services offered by Cloud computing providers.
Think of the impact of AWS marketplace and their equivalents in Azure, Google, Alibaba, Huawei and others and you get some scope of how Cloud computing and machine learning are in-it-together.
This creates countless jobs, adds value and harnesses the power of data science, machine learning and Big Data for businesses all around the world. The intersection of the Cloud, datascience and AI is truly not just a business but a technological convergence point, it’s own kind of micro singularity if you will.
The depth of the features offered by AWS and Azure is continually self-optimizing to such an extent that something like Google Cloud could harness quantum computing for a whole new era of data science, AI and predictive analytics and value creation.
Cloud automation, Hybrid Cloud solutions, Edge Intelligence, and so much of the technical stuff happening in data science today happens in the Cloud and its happy marriage with AI and datascience services. The increased use of NLP in business, quantum computing at scale, next-generation reinforcement learning – it’s all possible because the Cloud is evolving at such a rapid pace.
The Analytics Revolution
For data to become truly ubiquitous in society and business one thing is needed. The automation and accessibility that is made possible when analytics becomes a core business function. Better data and analytics means better business decisions. This is a bit what Square enables for small businesses with augmented analytics at the point of sale or real-time analysis of a small businesses finances that ensure their long-term survival and more rapid adaptation than would be possible without that data, analytics and insights.
The analytics revolution allows data to become actionable in society in-real time and unlocks the true value of datascience for merchants, businesses, smart cities, countries and public institutions at scale. Imagine if our healthcare, education and governance were all embodied analytics and data like our entertainment, E-commerce and mobile systems to today? Imagine the human good that would accomplish.
When data analytics becomes a core of a business, the value it unlocks achieves gains at every stage of its business cycle. Many businesses that were once used to approach analytics as a nice-to-have support function , gradually now embracing it as mission critical. This is what FinTech can do for consumers in a way that Bank cannot and eventually it’s a disruptive force. The analytics revolution is datascience at work in society with AI driving new value chains and optimizing existing ones.
The Impact of Natural Language Processing in the Future of Data Science
In 2021 there’s been a lot of “bling” about NLP systems of scale. It’s not just happening at OpenAI with GPT-3, it’s occurring all over the world. NLP and conversational analytics also will one day allow for AI to be more human-like and this opens up the door for a more living world of machine learning that’s not just routine algorithms, but more personalized and humanized.
Language is the key for people, so NLP will make AI more life-like. Your smart speaker and smart assistance, chat bots and automated customer service are only going to get smarter. That smart OS in movies such as Her, will soon be closer to being a reality. AI as a form of human companionship will exist within our lifetimes, probably a lot sooner.
NLP has hugely helped us progress into an era where computers and humans can communicate in common natural language, enabling a constant and fluent conversation between the two. Voice based search is becoming more common, smart appliances have voice options, IoT and NLP will have babies, it won’t be about redundant chatbots and digital assistants you never use, but about actionable NLP in the real-world that work.
While I believe using an AI assistant to help you code is a bit far-fetched it’s interesting to see what Microsoft is doing with OpenAI and GitHub. OpenAI’s codex is a good water cooler topic, and the AI-human hybrid buddy system may eventually be coming to the future of coding and programming. One thing is for certain in 2021, NLP in datascience is a hot topic full of brimming potential for knowledge workers and keen entpreneurs.
When Microsoft acquired Nuance, you knew NLP was coming to Healthcare at scale. Nuance is a pioneer and a leading provider of conversational AI and cloud-based ambient clinical intelligence for healthcare providers. The synergies of NLP companies and the Cloud are obvious.
The applications of NLP in the Cloud and in society is one of the greatest explosions of AI entering the human world we will perhaps ever see. What happens to society when AI becomes more human-like and capable of conversation? On Tesla’s AI day, they declared they were building an AI humanoid called Optimus. I suppose it will have pretty sophisticated NLP capabilities.
Conclusion and Future Ideals of the Impact of Data Science
I hope you enjoyed the article and I tried to bring a few fresh points of view to the wonderful future of datascience and highlight how mission critical it is for a new wave of talented knowledge workers to enter data science, programming and machine learning to help transform human systems for the uplifting of the quality of life for all of us on planet Earth.
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