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Top 8 Data Science Trends 2022

The past two years were transformational to how businesses around the world operate. Retailers and other service providers were forced to perform a fast digital transformation to present their offers to customers. The unusual global health situation has sped up digitalization in businesses and led to an extensive amount of digital data.

Naturally, data scientist and data analyst positions have only skyrocketed now that businesses realize that data-driven intelligence is the main driver of success. As a result, we’re seeing the emergence of numerous data trends across industries. Many of them started way before the global changes emerged but will see an additional impact in 2022 and beyond.

We focus on eight trends that you should know if you’re a data scientist or work with data in your business role.

Python Domination

Python is the programming language of choice for deep learning algorithm application development. This makes it more popular in 2022 since deep learning apps are slowly becoming a hot trend. In fact, this language saw a massive increase in use last year, when it topped Java and C as a top programming language. A recent TIOBE publication showed that Python has the majority of shares in the industry with nearly 13% ratings.

Python is growing in popularity, scalability, and versatility. It serves to create web applications, GUI applications, Machine Learning solutions, and more. It’s also a cross-platform interpreted programming language, meaning that developers can run programs on different operating systems and platforms. Finally, it has straightforward and easily readable code, which is not the case with some more advanced languages.

Python finds its use in nearly every modern industry. Some of the world’s most revenue-generating companies like Instagram, Facebook, Uber, Amazon, Pinterest, and others use it for a range of systems and solutions.

The Rise of Deepfake Tech

Deepfake technology refers to using AI to create content that manipulates or misrepresents someone’s image. It can use an image, audio, and also video. It all started in 2019 when an artificial intelligence company deepfaked Joe Rogan’s voice and instantly went viral online. Ever since, deepfake technology has only improved.

It’s important to note that there’s plenty of malicious activity around this approach. For example, criminals can now use advanced AI software to manipulate the voices of chief executives to demand money transfers from employees. One such case was reported by a UK-based energy firm.

Even though there are a lot of concerns about malicious activity related to deepfake, there are also plenty of benefits. This approach can be used to mask the identity of people’s faces or voices for privacy protection. People can create brand-new avatars to express themselves online and expand their ideas and beliefs. It can also be useful for artists, in education, and also to human rights activists and journalists who wish to remain anonymous.

Augmented Data Management (ADM)

Augmented Data Management is part of the AI system that strives to automate or enhance data management tasks. It gains more traction as more companies become aware of its potential. It’s estimated that by the end of 2022, the number of data management tasks performed manually will drop by 45%.

There’s an increasing amount of data businesses must deal with when it comes to volume, velocity, and variety. This urges them to come up with advanced data management solutions to make the job less time-consuming. Augmented data management can help achieve just that.

Notable examples include spotting large dataset anomalies, tracing data from a report back to the origin, resolving issues with data quality, etc. Users can now also leverage their own data management platforms to experiment with ADM. More specifically, this approach can be used to enhance data quality, metadata management, and master data management.

Augmented Data Management (ADM)

If you run a business of any kind, you can consider ADM as a technical capability you can develop to streamline your work processes and bring your data management to new heights.

Explainable AI Platforms

Explainable AI is a set of frameworks and tools that helps users understand and interpret predictions made by their machine learning models. The goal is to improve and debug model performance and help other stakeholders understand the model’s behavior. An explainable AI platform describes model accuracy, transparency, fairness, and outcomes in AI decision-making.

This approach gained importance when “explainability” become an important issue for data processing teams. AI is getting more advanced day by day, so humans are now challenged with retracing and comprehending how algorithms come to results. All users must understand essential business data, and the only way to achieve so is through rule-based logic.

The explainable AI trend is especially popular in sectors like finance which are often risk-oriented.

Some of the benefits of explainable AI include reduced cost of mistakes, reduced impact of model biasing, code compliance and confidence, and more informed decision-making. It also helps banks offer smoother customer experiences, drive profitability and loyalty, and automate more of their processes.

Data Privacy Awareness

More and more businesses and users are becoming aware of how sensitive data collection, storage, and preparation are in daily business operations. Privacy issues, data leaks, and identity theft are becoming more present throughout the online sector regardless of the industry. So, to survive, companies need to become more privacy compliant. The world’s top companies like Apple have invested serious marketing efforts and budgets into promoting privacy as an important asset.

In fact, more states in the U.S. are now trying to push the privacy legislation forward. A few years ago, California was the only state with a privacy law on the books. Soon after, Colorado and Virginia followed, and now other states are following the trend.

The problem with many businesses worldwide is that they still don’t have appropriate systems in place for untangling the personal data mess that resides across their applications and systems. This can lead to frustrating customer experiences as users have a hard time learning how their information is being used. More data privacy work is needed to solve this issue in years to come.

Data Provenance

Data provenance, also known as data lineage, is an approach that seeks the origin of a piece of data. It is now a critical activity across many data science projects, and it’s only going to gain more traction in the following years. You can think of data provenance as a detailed map of all direct and indirect dependencies between data sets in a particular environment.

The immense increase of data has led to slower delivery of predictive insights, lower levels of trust in dashboards and reports, as well as an increased number of data incidents. This is why data lineage is important and will only continue to develop, especially when you consider the major business benefits:

  • No impact analysis incidents
  • Faster incident resolution
  • Data pipeline observability
  • Regulatory compliance
  • Efficient migrations
  • Data virtualization and consolidation
  • Better data comprehension

When it comes to implementation, lineage data is more about logic such as code or instructions rather than tables and columns. It can come in the form of an SQL script, database, Java API call, or an advanced macro Excel spreadsheet. It can be anything that moves data from one location to another, modifies it, and transforms it.

Intelligent Feature Generation

Intelligent feature generation started developing in 2021 and is projected to have a long-term impact in the following years, especially in 2022. Machine learning has now become a vital part of any data analysis. Therefore, intelligent feature development for every unique case is a must for improving the overall accuracy of any learning model.

A feature is determined by what’s considered most important for an individual data set or business. For example, it’s about creating simplified queries to replace more complex ones, transforming a set of data and facts into scenarios that need certain reactions, or measuring the distance between peaks and troughs in a data set.

Benefits of intelligent feature generation include better performance of machine learning algorithms, knowledge about processes, help in visualization, data understanding, data reduction, limiting storage requirements, reducing processing costs, etc.

Intelligent Feature Generation

Customer Personalization

Online shopping has become a dominant trend ever since the pandemic hit. Most businesses had to adapt to the new reality and offer new customer experiences by bringing their products in front of the computer and smartphone screens. However, it is still a challenge for some retailers to bring these new experiences. Given that most retail businesses now have an online presence, it’s not enough to simply offer a product using the latest technology.

Businesses must come up with different ways to personalize their offer to customers to beat the competition. Thus, personalization has become a key marketing tactic that allows marketers to target customers with messaging, ads, and more.

For the most part, personalization refers to using audience and data analytics to meet an individual customer’s needs. The benefits of such an approach are obvious:

  • A better understanding of the consumer
  • Conversion
  • Better customer engagement and feedback
  • Lead nurturing
  • Higher customer retention
  • Higher revenue
  • Brand affinity
  • Social sharing

Catching Up With Data Science Trends

The emerging data science trends in 2022 have a background in the sharp changes that transformed how businesses operate in the wake of the recent world events. If you’re a business owner or data scientist, it’s important to be aware of these trends and start thinking about how to apply the ones that relate to your business.

Implementing these trends will not only help you come in line with the recent market changes, but it will also transform the way your business operates, help streamline processes, and unlock the best data science opportunities for your business to make it future-proof.

By interactivated • on October 15, 2022

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