Data analytics trends and predictions for 2022

Data science has evolved dramatically over the last 10 years. However, very few organizations have experienced the full business impact or competitive advantage from their advanced analytics, despite significant investments in data science and machine learning (ML).

Many of the tools needed to scale ML are too complicated, and necessary skill sets are in short supply. But change is now afoot. Recent technology advancements are poised to significantly impact the way in which data scientists and data analysts work.

In 2022, six trends have the potential to accelerate ML and move organizations from descriptive and diagnostic analytics (explaining what happened and why) toward predictive and prescriptive analytics that forecast what will happen and provide powerful pointers on how to change the future

Higher Adoption of Business Intelligence Tools

Industries, including manufacturing, business services, consumer services, and retail, will increase the adoption of business intelligence tools and technologies in 2022 and beyond. This is because these tools transform the way organisations approach data analytics.

Business intelligence tools make big data more available for use as they decrease the level of computation and expertise required to interpret data. Even if users are not from the IT or data mining background, they can still execute analytical functions such as exploring data sets or performing data-mining tasks. The only prerequisite required is to know how best to use these tools.

Growing Cloud-Based Solutions

As leading enterprises and small and medium enterprises go remote due to the onslaught of the pandemic, more cloud-based technologies are facilitating this shift, and helping companies save costs associated with legacy tools and bottlenecks.

Cloud-based Solutions have become mainstream and this trend will continue. Several companies will prefer cloud-native analytics solutions to gain a competitive edge with streamlined analytics and business intelligence.

‘Right data’ analytics will surpass Big Data analytics as a key 2022 trend

Big Data is almost too big and is creating data swamps that are hard to leverage. Precisely finding the right data in place no matter where it was created and ingesting it for data analytics is a game-changer because it will save ample time and manual effort while delivering more relevant analysis. So, instead of Big Data, a new trend will be the development of so-called “right data” analytics.

Data analytics ‘in place’ will dominate

Some prognosticators say that the cloud data lake will be the ultimate place where data will be collected and processed for different research activities. While cloud data lakes will assuredly gain traction, data is piling up everywhere: on the edge, in the cloud, and in on-premises storage. This calls for the need to in some cases process and analyzes data where it is, versus moving it into a central location because it’s faster and cheaper to do so. How can you not only search for data at the edge, but also process a lot of it locally, before even sending it to the cloud? You might use cloud-based analytics tools for larger, more complex projects. We will see more “edge clouds”, where the compute comes to the edge of the datacenter instead of the data going to the cloud.

Storage-agnostic data management will become a critical component of the modern data fabric

A data fabric is an architecture that provides visibility of data and the ability to move, replicate and access data across hybrid storage and cloud resources. Through near real-time analytics, it puts data owners in control of where their data lives across clouds and storage so that data can reside in the right place at the right time. IT and storage managers will choose data fabric architectures to unlock data from storage and enable data-centric vs. storage-centric management. For example, instead of storing all medical images on the same NAS, storage pros can use analytics and user feedback to segment these files, such as by copying medical images for access by machine learning in a clinical study or moving critical data to immutable cloud storage to defend against ransomware.

Blockchain in Data Science

While blockchain has become a part of FinTech and healthcare industries, it’s now entering the IT industry. So how does blockchain help with data science? Data scientists have to structure the information in a centralized manner to make it ready for data analytics. This process is still time-consuming and requires effort from data scientists. Blockchain can solve the issue effectively.

Focus on Edge Intelligence

Edge computing will become a mainstream process in 2022. Edge computing or edge intelligence is where data analysis and data aggregation are done close to the network. Industries wish to take advantage of the internet of things (IoT) and data transformation services to incorporate edge computing into business systems. It is one of the crucial data science trends to look out for in 2022.

Improved Cybersecurity

As most businesses have been forced to invest in an increased online presence during the pandemic, improved cybersecurity will be one of the top data science trends for 2022. A single cyber-attack can completely derail a business, but how can companies track potential points of failure without massive cost and time investment? The answer to this burning question lies in excellent modeling and a commitment to understanding risk.

Small Data and Scalable AI

As a greater amount of the world moves on the web, the capacity to make versatile AI in light of more extensive datasets is a higher priority than at any other time. Albeit the utilization of large information that shows up rapidly is as yet key for making successful AI models, it’s little information that enhances client investigation. It is not necessarily the case that enormous data does not have esteem, however, it is exceedingly difficult to choose significant patterns from such datasets.

Democratizing AI and Data Science

People have already seen how DaaS is becoming famous. The equivalent is currently being applied to AI models also. On account of the expansion popularity for cloud administrations, AI and ML models are simpler to be presented as a piece of distributed computing administrations and devices. It is one of the crucial data science trends to look out for in 2022.