6 Data Science Trends to Watch in 2021

By Data Science Salon

In this tumultuous year, we’ve seen how digital and data-centric companies have been growing and flourishing amid the pandemic, which encouraged many other organizations to follow in their footsteps.

The year 2020 presented a few key events that highlighted the progress of data science, artificial intelligence (AI) and machine learning (ML). OpenAI developed the GPT-3 model, a natural language generation program that can do incredible things like generating news articles, creating stories, and even writing code. DeepMind was able to crack the protein folding problem with AlphaFold, a revolutionary breakthrough in biology. To face the COVID-19 pandemic, data science and AI were instrumental in fast-tracking the development of a vaccine and will continue to bring advancements in the biotech field.

With new technological advances and data growing at an ever-increasing rate, the data science revolution will continue to evolve. Thus, here are 6 key data science trends in 2021.

1. Operations — DataOps and MLOps

Operations in data science, mainly DataOps and MLOps, is essentially bringing AI models and theoretical results into the production landscape, generating real business value for companies. These operations are derived from the practice of DevOps, which unifies software development (Dev) and software operations (Ops) and aims to increase an organization’s ability to deliver applications and services at a fast pace.

DataOps means applying DevOps techniques to improve the integration and automation of data flow between data managers and users across a company. This ensures data quality and easy access, which is central to improving the performances of ML models in production. On the other hand, MLOps utilizes DevOps techniques to deploy and maintain ML models in the IT system of an organization.  These operations aim to improve and automate the delivery of ML models, increasing reliability and productivity. 

Combining these operations, you get an efficient and sustainable data science pipeline in production, ensuring collaboration, reproducibility, and automation. As many of the data science tools and frameworks become more democratized, these operations will be key components for organizations to apply data science solutions and generate value for their businesses.

2. Data Democratization

At the start of the data revolution, the skill of extracting insights from data with programming languages and tools were exclusive to data analysts. With data democratization, technologies and services have made data easily accessible to everyone in the company, allowing even business owners to have quick access to insights to make snappy data-driven decisions.

In a world where data democratization is the norm, organizations will have data all aggregated in one place, allowing all sectors to access the data. Each sector will only receive data that is relevant to their needs and can make data-driven decisions by working with the self-service analytics tools provided with minimum effort.

With data democratization, every individual in the company can be empowered to make decisions based on insights gained from data analytics rather than intuition only. 1 creates an entire workforce that is data-centric and can elevate the company’s performance and productivity as a whole.

3. Hyperautomation

Hyperautomation is envisaged to be a major trend in 2021. It’s essentially combining technologies like AI, augmented analytics, and IoT that augments Robotic Process Automation (RPA), further extending its capabilities.

The main purpose of hyperautomation is to automate most of the monotonous tasks of a business to increase efficiency, and free resources and capital for companies to invest in other areas. With it, everyone in the organization can be empowered and contribute towards a digitized organization. By effectively integrating hyperautomation in the company, the workforce can start with automating simple tasks and even apply them to complex ones.  Businesses are also able to observe the efficiency of their operations and monitor performance metrics, and gradually innovate their business processes and provide growth opportunities.

As the technology matures in the company, it will gradually improve many of the processes in the business, and form a comprehensive system that can do many things. This eliminates the need to invest in multiple systems and can streamline the workflow of the entire company.

4. Low-code no-code (LCNC) Data Science 

The Low-code no-code revolution is also becoming more popular in the data science world. These LCNC platforms are commonly drag-and-drop and come with fully automated ML services, suited for even non-experts. Some examples of tools that are low-code include Pycaret, H20 AutoML, Auto-ViML. And no-code tools include CreateML, Google Cloud AutoML, Teachable Machine, and Uber AI.

Driven by a mission to fast-track operations, organizations are quick to adopt these LCNC tools to allow the workforce to innovate, regardless of their role or technical skills. These tools will create a low entry barrier for many companies to start applying AI in their businesses. This can eliminate the need to build their ML platforms from the ground up, which can be very costly, and instead apply these tools to their business problems. What’s great about low-code tools is they can be customized to generate the right models to fit certain needs for the business.

For companies that already have a team of data scientists working on business problems, they can be beneficial in reducing development process cost and time, along with automating many processes and reducing their workload. This can especially accelerate smaller data science teams with beginners.

5. Cloud-Native ML  

Containers like Kubernetes and the cloud go hand in hand, and a cloud-native deployment has been rising in popularity as it offers businesses a seamless development process that is efficient, portable, scalable, and secure. Combine containers with the cloud, you get the best of both worlds.

With containers, ML development and deployment is just a matter of minutes due to its working with microservice-based architecture. This creates individual microservices that exist and operate independently, with APIs developed to integrate them suited for the business needs.

With the cloud, growth isn’t restricted by the company’s resources, instead, you have an elastic architecture that scales according to its usage. You get access to huge amounts of data (data lakes) and automated training in ML, along with debugging and evaluation in the cloud. Companies can also leverage the cloud for their existing models, shifting the entire architecture to the cloud to save resources.

6. Explainable and Responsible AI

AI techniques based on algorithms and big data are increasingly being used in making important decisions. And with issues like misinformation and racial bias being the spotlight in 2020, there will be growing stipulations and regulations on how AI is trained and developed.

To tackle biases in AI would require a shift in focus to model explainability. Not only does this create trust from stakeholders and regulators to the users to better understand the models, but it would also help pave the way for responsible AI. A responsible AI will be more ethical and will be more trusted by the general populace.

AI is being leveraged against major global problems like climate change and the economy, to ensure diversity and innovation. At the same time, a focus on explainability will be crucial, and emphasis on the ethics of AI will be important to organizations across industries and society as a whole.


With these 6 data science trends, it’s evident that we’ll continue to see the significant developments of the data science revolution across different industries.

As data science becomes more democratized and automated, more and more businesses can integrate it into their businesses. With the no-code low-code revolution, more and more people can glean insights from data with minimal technical skills.

More importantly, the integration of data science operations – DataOps and MLOps will be crucial for applying ML models in practice and streamlining the deployment process into production, generating real value for businesses and companies.

There are still many problems that AI and data science cannot solve, but by being more data-skilled and well-informed, and investing in the right infrastructures, businesses around the world can apply the current technologies and solutions to bring their companies to the edge of innovation.

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