Top 10 Must Follow Data Trends in 2022

 In Big Data, Data Analytics

Time passes, technology advances and our lives improve. So, what’s fueling all this growth? The obvious answer is the latest data trends emerging nowadays.

The market for data analytics is exploding. According to IDC researchers, businesses spend $215 billion on big data and business analytics solutions in 2021, a 10% increase over 2020.

The demand for modern data analytics specialists is also skyrocketing, with the US Bureau of Labor Statistics projecting a 31% increase in data science jobs by 2030. Also this year, almost every company value information as a “key corporate asset” and analytics as a “vital capability.”

Here we shall discuss the upcoming innovations in artificial intelligence & big data Trends in 2022.

Top 10 Data Trends in 2022 

1. Automated Artificial Intelligence

Automation, artificial intelligence, and machine learning are changing the game for businesses worldwide. AI is rapidly progressing, particularly in data analytics, where it aids in the extraction of greater economic value. The epidemic and remote work have boosted chances to track and measure data quality, resulting in a business-oriented data-driven culture. 

2. Augmented Data Analytics

Composable data analytics is a method for organizations to aggregate and consume analytics capabilities. Specifically businesses gather data from various data sources to make better informed and successful decisions.

Although traditional approaches may not be as flexible as these solutions. But they have reusable, swappable modules that may be installed anywhere. According to Gartner experts, by 2023, 60% of companies would construct business applications using components from analytics solutions.

3. Standardized Data Fabric 

According to a Forrester analyst, between 60 and 73 percent of company data is not leveraged for analytics. Data fabric is an excellent approach to the problem of combining diverse data sets for analytics. Suppose IT can provide a unified data architecture as an integrated layer connecting data endpoints and processes.

In that case, it can make mission-critical cloud data more discoverable and reusable across all environments of an organization. Hence, the primary value of data fabric design is ensuring uniform data management. Also, it helps in making it simple for users to analyze data in various scenarios.

4. AnalyticsOps

DataOps was introduced to the Gartner Hype Cycle for Data Management in 2018. Collaboration, automation, testability, and curation of data processes can all benefit from DataOps, especially when putting these processes into production.

Since then, there’s been a surge of interest, and DataOps suppliers have witnessed sky-high valuations. There will be an emerging trend in 2022 toward building an overarching practice called “AnalyticsOps.” It can make it easier to deliver composable analytics and manage the data fabric using a cloud storage.

5. Data Democratization

Data analytics is no longer seen as an afterthought or a supplementary activity. Businesses are already embracing data analytics as a crucial business engine. Thus helping in informed decision-making. Also, it is now an essential component at the outset of any new project related to public cloud.

Companies may want to make analytics available to all employees, not only business analysts. However, the additional workloads and concurrency required are a factor to consider. According to Gartner researchers, by 2025, 80% of data analytics activities focused on business results would be considered a critical business competency.

6. Analytics In Edge Computing

The edge computing market grows at a stunning 19 percent compound annual growth rate (CAGR). It’s also expected to increase from $36.5 billion in 2021 to $87.3 billion in 2026. Since computing power advances to the edge, technologies that support it are increasingly likely to be located near physical assets.

As a result this change offers higher speed, agility, and flexibility. At the same time it allows for real-time analytics and autonomous behavior for IoT devices. According to Gartner researchers, data created, maintained, and analyzed in edge environments will account for 50% of data analytics leaders’ responsibilities by 2023.

7. No-Code Technology

Companies are starting to employ out-of-the-box foundation models. It is because they begin to integrate AI into the sector. It cuts the time it takes to develop AI solutions in language, vision, and other areas by a significant amount. Artificial intelligence (AI) will have a tremendous impact on citizen development.

Thanks to AI advancements in low-code technologies, because everyone will be able to become a citizen developer. As a result citizen coders will be able to communicate the problem they’re trying to solve in plain English. Also, conversational AI will generate the necessary code.

8. Focus on Actionable Insights

Actionable data, which blends big data with business processes to help you make the best decisions possible, is the focus. Purchasing expensive data warehouse software will generate no results until organizations examine the data and derive actionable insights. These insights help you better understand market trends, challenges, and opportunities.

Also, you can make smarter decisions and do what’s best for your company with actionable data. By structuring activities/jobs in the company, improving workflows, and allocating projects among teams, actionable data insights may help you enhance your organization’s overall efficiency.

9. Natural Language Processing

Natural Language Processing is frequently used in corporate operations to evaluate data and discover patterns and trends. In 2022, natural language processing (NLP) is predicted to be used to obtain data from data repositories.

It will have access to high-quality data, resulting in high-quality insights. Areas, where NLP will see more usage, are Sentiment Analysis, Twitter Analytics, understanding Customer Satisfaction, etc.

10. Automated Data Cleaning

Many duplicate data with no structure or format, inaccurate data, and lots of data redundancy have to cleaned from public cloud. . As a result, the data retrieval operation is slowed. It means a direct loss of time and money for enterprises, which might be in the millions on a large scale.

Many academics and businesses are looking for ways to automate data cleansing and scrubbing to improve big data analytics and gain more trustworthy insights. AI & MI will play a significant role in data cleaning automation using analytics tools.


In 2022, data alone will not be enough for practical analyses. Thanks to breakthrough data lake technologies, data has never been more accessible and valuable to enterprises of all types than it is now.

The data science and AI developments mentioned in this article will help you understand the market’s new core aims, including automation, accessibility, and intuition. Data science will continue to be a hot topic in the coming years.

In 2022, companies who successfully extract meaningful insights from data will be able to innovate faster and strategy more effectively. Deep learning, along with natural language processing, is just one of the technologies arising from the development of Data Science. It has benefited the development of machine learning to attain artificial intelligence in general.

We’ve seen how businesses have progressed over time, adopting cutting-edge technologies to increase productivity and return on investment. Big data, artificial intelligence, and data science are buzzwords these days.

Businesses want to employ data-driven models to streamline their operations and make better data-driven decisions. And, for this purpose cloud inspecting is a must. Thinklayer supports you and your business using all these trends in 2022.

References –

Recommended Posts

Leave a Comment