As we leave 2021 behind, we discuss 5 data science trends that will rise in 2022 and beyond.
Data science is an interdisciplinary field that uses a variety of methods, processes, algorithms, and systems to capture information and insights from structured and unstructured data for many different fields and purposes. When data meet with advanced technology, it provides businesses to access greater information and insights, higher performance, revenue growth, and the power to adapt to innovations quickly. Therefore, developments in machine learning and artificial intelligence technologies have an important contribution to data science. These developments mean more accurate decisions and faster solutions by supporting data to be actionable in real-time. Previously, having the right data analysis system was considered complementary, but today, it is crucial for all businesses.
In our article, we discuss the trends that we will talk about more frequently as of 2022.
1. The Rise of Self-Service Analytics
We are already aware that making the most effective decision at the soonest time possible is crucial for data-driven companies. At this point, self-service analytics systems that support data democratization* come into play. It is predicted that self-service analysis systems*, which enable all business users to access data and conduct their own analysis, will be adopted more by institutions by 2022. The proliferation of self-service analytics systems, which will enable business users to securely access data and gather insights from data, brings greater efficiency, lower cost, and ultimately more accurate decisions for organizations.
2. Data-Driven Customer Experience
This trend is about how businesses evaluate, interpret, and turn customer data into solutions to provide their customers with a better experience. Our time is limited, and we need to make our decisions as faster as possible. At this point, for example, e-commerce sites need to offer us user-friendly interfaces or to help our decision-making with relevant suggestions. Data-driven customer experience is provided by analyzing the user movements, activities, and feedbacks.
3. Generative Artificial Intelligence and Synthetic Data
One of the compelling elements in data analytics processes is the problem of using real data. The use of real data in processes such as system development and testing cause privacy issues and problems. At this point, the importance given to the creation of logical-related synthetic data is increasing. By 2022, this trend is expected to spread to many industries and use cases. Advances in artificial intelligence support work on generating and reproducing logically related data.
4. Augmented Data Management
Augmented data management, a trend highlighted by Gartner in 2020 data analytics trends*, uses machine learning and artificial intelligence techniques to optimize and improve operations. The goal is to make data accessibility faster and more efficient. Augmented data management automates many processes such as data discovery, outlier detection, missing values handling, and error detection. It also improves data quality, detects relationships between data, recommends the best action for cleaning, and makes smart decisions. Augmented data management techniques are necessary to simplify and consolidate the architectures of active metadata and increase automation in redundant data management tasks.
Augmented data management technologies can examine large samples of operational data, including actual queries, performance data, and schemas. A system developed for this purpose can adjust operations and optimize configuration, security, and performance using available usage and business data.
5. Automated Machine Learning (AutoML)
Automated machine learning (AutoML) is a trend that supports the concept of democratization of data. With this trend, the goal is to automate time-consuming machine learning tasks. The aim is to ensure that any business user who has a problem to solve or test can benefit from machine learning with simple and user-friendly interfaces. In this way, we can define AutoML as the use of machine learning systems by business users for data analytics.
As Datateam, we follow all data science trends and take advantage of the latest technologies for our products and services. Learn more about our solutions for your data-driven, and contact us for detailed information!
Resources: