• Alastair Majury

Data Science Trends for 2020

The three main data science trends for 2020 involve intelligence, new data formats and scalability. Machine learning and AI developments in recent years have helped businesses replace redundant tasks with automation, as technology continues to move toward achieving better operational efficiency. Here are some of the most evident data science trends to look for in the coming years.

Emerging Data Science Trends

Businesses should watch out for emerging trends such as augmented analytics, augmented data management, natural language processing (NLP), graph processing, blockchain and more. As data collection speeds up, businesses need to move toward an architecture that facilitates the sharing of agile analytics. Infrastructures of the future will be designed to support constant change with easy integration features. The increasing use of machine learning and AI is helping companies reduce routine workloads.

Intelligence Collection, New Formats and Scalability

Business intelligence is becoming increasing important to any operation, how it affects decision-making and storage. Data is shaping business insights more than ever before, which is why managers are turning to augmented analytics, a new type of software technology that enhances data sharing. It's based on machine learning and NLP, which provides an easy interface for queries and insights. This software can detect patterns and unusual trends.

One of the solutions that many businesses dealing with big data need is a better way to collect intelligence from various sources such as email, social media and marketing campaigns. After data is collected it must then be analyzed and delivered to the organization with reports on insights and useful plans. Companies will have greater ability to analyze data using augmented data management tools to meet compliance, adjust cost models and utilize data more effectively. They will further need technology that allows for collection of "continuous intelligence."

Graph processing and graph databases provide more visual ways to explore complex data and are expected to grow through 2022. Another growing trend businesses should be noticing is that commercial AI and ML platforms will eventually overshadow open source developers when it comes to new technology. There is a growing need for "Explainable AI" by specialists who know how to reduce brand and reputation risks involving privacy protection.

As data becomes more distributed, there is less need to store all of it in one place. Compartmentalizing data, known as "data fabric design," will become more important for scalability purposes. There will also be a need for "persistent memory servers" that allow for much larger memory and lower costs.

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