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8 Data Science Trends for 2022— and 2 Potential Pitfalls

8 Data Science Trends for 2022—and 2 Potential Pitfalls (Updated)

by Austin Willis

Data science and data analysis continue to become more and more complex as technology evolves and artificial intelligence makes new strides every day. It’s hard to predict exactly what 2022 will look like for the world of data science, but we can take some educated guesses based on current trends.

Here are our top guesses about the future of data science as we move deeper into 2022. 

Data Science Trends for 2022

1. Automated Machine Learning Maturity

Automated Machine Learning (AutoML) is the poster child of the artificial intelligence revolution. The field of machine learning has matured rapidly in recent years, thanks to big leaps in hardware processing power, cloud-based computing, and droves of new data sources due to the proliferation of the Internet of Things(IoT).

With all this new data comes increased complexity—and now, thankfully, automation. Automation in data science aims to transform existing repetitive work into reusable pipelines, allowing data scientists to focus on new complex ideas. 

 

2. Predictive Analysis in Widespread Sectors

Trends such as predictive maintenance are likely to become more widespread, with more businesses automating ML applications for operational decision-making. Read more about the data science applications of Robotic Process Automation and Intelligent Process Automation in our RPA white paper here.

 

3. Deep Learning and Neural Networks Moving to GANs

Deep learning/neural networks continue to grow more complex. Convolutional neural networks have led us into Generative Adversarial Networks (GANs), which can allow for a deep neural network that learns how to generate imagery based on images it’s been shown.

Conceptualizing what a GAN looks like is tricky at best—which is why online tools and demonstrations like Google’s Inceptionism exist. These types of tools will increase in popularity, especially if GANs lead to advances or rapid innovations in commercial applications (for example, image recognition).  

 

4. Enhanced Open Data Access

Over time, more government data will find its way onto public databases where open-source code developers can create products and services, not just enterprise software. 

 

5. Ambient AI

Artificial intelligence isn’t limited to the screen in front of you anymore. Increasingly, AI works without needing a device at all.

Ambient AI responds to voice commands via smart speakers. Speech and conversation systems by ecommerce providers use AI chatbots to answer questions without requiring help desks. Machine vision, facial recognition, navigation and motion tracking work even when users aren't directly engaged. An AI system might detect a child's growing frustration through physiological cues like increased heart rate or eye dilation, and deliver an educational distraction before they reach for their smartphone in response. Or it could monitor a clinical patient for signs of distress and alert a nurse before a crisis occurs.

Any number of potential applications for Ambient AI exist, but mitigating people’s concerns about privacy loss or potential manipulation is imperative to deploying those use cases successfully. Solutions to these concerns will have to be addressed on social, corporate, and legal stages.

6. Rapidly Developing AI/ML Talent Pool

As organizations across industries begin to see value in deploying artificial intelligence throughout their operations, they will need access to skilled people. According to consulting firm McKinsey & Company, artificial intelligence could boost global GDP by $13 trillion dollars between now and 2030. This means job opportunities and newly created industries for anyone interested inbuilding expertise around machine learning technologies.

Many new positions require advanced degrees, but not necessarily—some tech start-ups have made entry level jobs available on sites like HireArt. Knowledgeable employees who can train existing workers and keep existing skills fresh are valued additions at every level of any organization. 

 

7. 3D and Automation in Data Visualization 

One of the key trends for Data Visualization in 2022 is 3D Visualization, which will help users see more data in any given visualization. In fact, there are quite a few 3D Visualization tools out there that you can use today. Those will continue to grow in functionality and usage.

Another trend in visualization is automation. Like many other areas, automation is revolutionizing this space by allowing low-code and no-code creations of complex visualizations just by pointing tools at the data you want to see better. Tools such as Tableau or R Shiny, which make it easy enough that someone who doesn’t consider themselves a data scientist can create these types of visuals, will become even more prolific.

 

8. Customization and Collation in Data Preparation 

Preparing data is one of the most common aspects of data science, and it’s likely to see some major changes in 2022. The demand for customized datasets will continue to grow, meaning that business analysts (not just data scientists) will need easy ways to create custom datasets. Also, with more variables being introduced into datasets every day, software developers will need more powerful tools for data preparation.

It won’t be enough to simply load a file into R or Python and run commands—that process has become slow and clunky. Instead, users will want more access to interactive development environments (IDEs) that make it quick and easy to prepare a dataset from beginning to end. Think spreadsheet-like drag-and-drop functionality.

Traditionally made for coding tasks, these data-intensive IDEs will allow easier access to data prep tasks. That way, instead of each step of preparing a dataset being an isolated task, users can perform complex operations directly on their dataset as if they were working in a traditional Excel spreadsheet.

Potential Pitfalls for Data Science in 2022

With a changing landscape comes new or enhanced challenges in data science. Here are two things to watch out for:

1. Emerging AI/ML Ethical Concerns

Further exploration is needed to grasp exactly what types of legal and ethical dilemmas ML presents because so much of it is new, but some trends are becoming apparent. (Certainly there are myriad scenarios and fears in people’s imaginations, some realistic…some less so.) With computer programs capable of learning human language, we must determine which ones are covered by free speech laws, and who is responsible for keeping algorithms and AI products in compliance.

 2. Data Privacy and Usage Concerns

If AI can be used to profile individuals based on preferences and interests, will governments have to re-write data-privacy laws or tighten up restrictions on access for law enforcement? What about encryption—should researchers crack previously uncrackable encryption methods to improve security or create a back door for government access? These issues will become even more critical as we move further into the year. 

Summing Up: Overall Data Science Predictions for 2022 

With a changing landscape comes new or enhanced challenges in data science. Here are two things to watch out for:

2. Inconsistent internal practices point to a greater issue. 

On top of these specific trends, big changes are coming to how we think about data itself. 

The trajectory of data science has been exponential growth, and that's a trend that will continue in 2022. We're already seeing a major demand for data scientists as companies catch on to machine learning, artificial intelligence, and all their potential benefits. But what will be different by the end of this year?

There will be more data scientists than ever before—but they won't all be employed by big companies like Google or Facebook (although those companies will certainly have plenty). Instead, individuals and small startups will need people with skills in data science. In the past, we’ve referred to this proliferation as ‘Citizen Data Scientists.’ The current trend lines instead point to more citizens becoming true data scientists, as the demand is simply too high for a part-time skillset.

Luckily, the infrastructure and tools to turn out this next generation seem to be keeping pace with demand. That new generation of data scientists is the key to seeing these data science trends through, and imperatively, avoiding the potential pitfalls.

Austin Willis is a Senior Data Scientist at Launch Consulting and a self-professed lifelong nerd. He focuses on digital transformation using Artificial Intelligence and Machine Learning technologies, with a passion for emerging technology in the computing space. Data science is opening opportunities for transformation in every industry. Learn more about our Data Studio by emailing hello@launchcg.com.

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