Top Data Science Trends of 2022

Posted by Ash Steinfeld on July 8th, 2022

You might have heard of the buzzword, data science, many times. It's the trendiest topic in almost every era. Here, we'll take a look at several data science trends that are expected to take off in 2023.

1.  Big Data on the Cloud 

There is already an excess of data being produced. The challenge is assembling, categorizing, cleaning, organizing, formatting, and evaluating this enormous amount of data in one location. How do you gather data? Where may it be processed and stored? How do we communicate the revelations to others?

Artificial intelligence and data science models save the day. Data storage is still a problem, though. Businesses increasingly use cloud services for data storage, processing, and delivery. According to research, almost 45% of businesses have shifted their big data to cloud platforms. The usage of public and private cloud services for big data and data analytics will be one of the most significant trends in data management in 2022.

2. Emphasis on Actionable Data 

What use is it if you don't know what to do with data in its complex, unstructured, and raw state? The focus is on actionable data that combines big data and business processes to support your decision-making.

Purchasing pricey data software won't help much until the data is processed to produce insights that can be used. These insights aid in comprehending your company's existing position in the industry, market trends, difficulties, opportunities, etc. You are better able to make decisions and act in the best interests of the company when you have access to actionable data. Actionable data insights assist you in maximizing business efficiency by optimizing workflows, allocating projects among teams, and organizing activities and jobs across the firm.

3.  Data as a Service- Data Exchange in Marketplaces 

Today, data is also available as a service. How is that even doable?

You've probably encountered websites incorporating Covid-19 data to display statistics like the number of cases or fatalities in a given area. Enterprises can use this data as a component of their operational procedures. Other businesses that supply data as a service are the ones that provide this data.

4.  Use of Augmented Analytics 

How do augmented analytics work? Using AI, machine learning, and natural language processing, AA automates the examination of enormous amounts of data. Insight delivery that a data scientist would typically do is now automated.

5.  Cloud Automation and Hybrid Cloud Services

Providing improved data protection, scalability, a centralized database, governance structure, and data ownership at a cheap cost is changing how businesses see big data and cloud services. Artificial intelligence and machine learning are used to automate cloud computing services for both public and private clouds. AI for IT operations is known as AIOps.

6.  Focus on Edge Intelligence 

According to Gartner and Forrester, edge computing will likely become a common practice by 2022. Data analysis and aggregation that takes place close to the network is known as edge computing or edge intelligence. Industries want to integrate edge computing into business systems by utilizing the internet of things (IoT) and data transformation services.

7.  Hyperautomation 

Hyper-automation, which started in 2020, will also be a significant trend in data science in 2022. According to Brian Burke, Research Vice President at Gartner, hyper-automation is unavoidable and irreversible, and everything that can be automated should be automated in order to increase productivity.

8.  Use of Big Data in the Internet of Things (IoT)

A network of actual objects that are connected by software, sensors, and cutting-edge technology is known as the Internet of Things (IoT). This makes it possible for various networked devices to interact with one another and share data online. You may raise the system's adaptability and boost the precision of the machine learning algorithm's answers by connecting the Internet of Things with machine learning and data analytics.

9. Automation of Data Cleaning 

In 2022, having data alone won't be enough for advanced analytics. Big data is useless if it can't be cleaned up to the necessary standards for analytics, as we have indicated in the preceding sections. Inaccurate, redundant, and duplicate data with no organization or format are also included.

1. Increase in Use of Natural Language Processing (NLP)

It started as a subset of artificial intelligence and is now infamously known as NLP. Finding patterns and trends in data is now regarded as being a part of business processes. In 2022, it is predicted that NLP will be used to retrieve data from data sources quickly. Natural Language Processing will have access to high-caliber data, producing high-caliber insights.

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Conclusion 

In the years to come, data science will remain a hot topic. More of these advancements and breakthroughs are coming. Data scientists, data analysts, and AI engineers will be in more demand. Hiring a data analytics company is the simplest way to implement the most recent changes in your company. Furthermore, if you want to learn more about data science tools and techniques, visit a data science course in Mumbaioffered by Learnbay. Become IBM certified data scientist in today’s data-driven world.