Top 5 Data Science trends to watch in 2020
With 2020 reaching us at pace, data experts have started to investigate and predict the upcoming trends that will help data scientists use the finest form of collected data. Find below, the list of top 5 data science trends we can hope to witness in 2020.
Automated Data Analysis
Automation over the years has turned out to be more effective in terms of business growth. By 2020, we can expect to witness over 40% of data-based tasks to be automated. Data Analysis automation is expected to pace the productivity of resident data scientists. Automation has been intensely favored in the world of digitalization and is trusted more than manpower.
It has turned out to be an exceptionally supported element that helps large as well as small enterprises to use the collected data effectively for better growth and advancement. Automation directly helps heads of an organization to effectively observe, plan and implement the strategies accordingly, helping in pushing their company ahead with the right analytics to drive choices.
In-memory Computing
Another trend that we can hope to witness bloom in 2020 is in-memory computing (IMC). Since the expense of memory has reduced lately, in-memory computing has turned out to be a mainstream technological solution for an assortment of advantages in the world of analytics.
The expenses and intricacy involved in taking up IMC are being reduced by the newly launched persistent memory innovations, which is another memory tier that is arranged between NAND flash memory and dynamic random-access memory. This IMC provides exceptionally powerful mass-memory to help to manage high-performance tasks at hand. This is exceptionally profitable to companies as they require a lot faster CPU performance, yet additionally faster storage and larger amounts of memory.
Graph Analytics
The ultimate utilization of graph processing and graph databases are expected to develop at 100% every year through 2022 to constantly quicken data planning and empower progressively intricate and adaptive data science.
Graph analytics comprises models that decide the “connectedness” across data points. Improved, scalable, and lower-cost processing alternatives, for example, the cloud and GPUs are making graph analytics and databases prime possibility for accelerated deployment.
Internet of Things
By 2020, we are hoping to see more than 20 billion active IoT (Internet of Things) devices, which simply imply that there will be more devices from which the organization will be able to gather more data for analysis.
Accordingly, we are going to witness a lot more analytics solutions for IoT gadgets providing important information as well as transparency to give a broader picture. Also, a minimum of 75% of huge organizations will be repressed from achieving the full advantages of IoT because of the lack of experts in the field of data scientists.
Continuous Intelligence
By 2020, Continuous Intelligence is expected to become a thing in order to utilize real-time context data to enhance decisions. CI is a design pattern, wherein real-time analytics are integrated inside any of the business activities, preparing present-day and historical information to support activities because of events. This design pattern gives the luxury of decision automation.
