Data Science Trends 2021
This year has shown how important it is to act quickly and adapt immediately to new circumstances. It turned out that the ability to harness digital technologies was and always will be an important issue. The use of modern technologies was not just an option, but became an absolute necessity. One thing the enormous changes have brought with them is the acceleration of digitalization.
To be on the pulse of time, we have identified 5 top data science topics for you in 2021. To forecast the data science trends 2021, we have combined our insights from our exchanges with customers and partners with our intuition for the upcoming topics of our industry, which has matured by over 10 years now.
1. Industrialization / operationalization of AI platforms
Industrialization of AI platforms enables reusability, scalability and security of AI, accelerating its adoption and growth. AI requires platforms that accelerate as well as automate the process of AI development and implementation to make AI accessible to the masses. By training models and developing algorithms, AI is able to process mountains of data in a very short time and can reach conclusions faster than humans. Planning, reporting, analysis, and dashboarding – All of these options in a common and integrated platform are a critical factor in the sustained success of performance management and analytics. Thus, embedding AI platforms into operational systems is one of the most relevant trends in the market.
2. Decision Intelligence / Decision Automation
By 2023, more than 33% of large enterprises will have analysts practicing decision intelligence. Decision intelligence combines data science, social science and management science into one cohesive field. This method of making complex decisions goes beyond the scientific framework of mathematical calculations and machine learning algorithms to help people or organizations achieve better work outcomes. Decision automation enables organizations to automate the decision-making process in many different areas (i.e., within a process, application, department, etc.). For example, automated decisions increase productivity and reduce risk and error rates in a decision-making process. Decision automation is typically applicable to routine, repetitive decisions that occur in the day-to-day activities of an organization.
3. Data Labeling / Annotation Services
If data is not good enough, errors can occur, but in some cases there is no margin for these errors – such as in self-driving cars. Data labeling is an important part of data pre-processing for machine learning. This labeled data should be of very high and very precise quality for a model to work. Data that comes in different formats such as text, images, audio or video files are labeled for specific purposes using specific techniques to provide context. This allows machine learning models to understand and analyze information and provide appropriate results. In this way, machine learning algorithms are trained, patterns are recognized and eventually remembered for predictions.
Data labeling enables machines to accurately understand real-world conditions and opens up opportunities for businesses and entire industries.
4. Augmented Intelligence
Human-machine collaboration – Augmented intelligence is still an important topic and will remain in 2021. At the forefront is the ability to work more efficiently and successfully. Automated routine tasks create more freedom for more demanding tasks that exclusively require human intelligence.
5. Data science infrastructures
For companies to be able to work efficiently, a suitable and stable IT infrastructure is crucial. It is the linchpin for the productive use of data science and AI in the company. In order to be able to use the potential of data science and AI sustainably and optimally in times of digitalization, a seamless implementation of analytics solutions in the company processes is required to ensure smooth interaction. High availability, scalability and load balancing – whether in the cloud or on premises – IT infrastructures were and remain a top trend.
Further and interesting articles on the topic:
We transform trend topics into sustainable added value for you: Consulting, projects, training, software: We are specialized in the field of data science and are happy support you. Learn more now or contact us directly.
Vanessa Roy - Posted on 15.12.2020
Vanessa Roy studiert Business Studies an der Universität Kassel und unterstützt das Marketing bei der eoda GmbH. Neben den abwechslungsreichen Tätigkeiten in diesem Bereich, schätzt sie ebenfalls die tiefgründigen Einblicke in die facettenreichen Entwicklungen im Rahmen von Data Science.