The time when big data was a new business asset is long gone, as more and more organizations adopt a data-driven culture. According to Statista, in 2021, 50% of worldwide companies managed data as a business asset, 37.8% created a data-driven organization, while as much as 64.2% of companies managed to drive innovation with data.
However, while small businesses are still planning to hop on the big data train, larger organizations, which have already implemented big data projects, will focus on enabling better business outcomes through data. But how? Find below the most exciting big data trends we should be looking at in 2022.
ML and Automation for High Quality Data
Owning a lot of data has been the goal for most businesses in the last few years. But CXOs are now learning that having a large amount of data is not enough, and start to realize the importance of owning healthy data. While many organizations have been collecting data in the past years, they have not considered data quality management to be a priority.
This will change in 2022: the focus of businesses will shift from simply storing large volumes of data to using quality data to enable business outcomes.
Leveraging machine learning and automation provides access to a higher quality of data, which results in overall better decision-making at CXO level. Increasing adoption of intelligent automation in Operations has paved the way for new mindsets: artificial intelligence and machine learning algorithms are only as good as the data they are fed. Analyzing healthy data will make the difference between top-tier businesses and SMBs.
In order for AI to deliver substantial results, it needs to be focused on a goal and an outcome. This will lead many business owners to adopt decision intelligence - the commercial application of AI to the decision-making process. This new top trend, endorsed by Gartner, brings a wide range of disciplines and decision-making techniques to work together, such as: decision management, decision support, as well as descriptive, diagnostics and predictive analytics.
This association of disciplines can help unlock hidden value from the high volume of data in a way that was not previously possible. Artificial intelligence and machine learning can help with the acceleration of the decision-making process. Today’s ML technologies can analyze large data sets both faster, and more accurately, and can lead to intelligent courses of action with minimal human intervention, due to:
Data Streaming Real-time Analysis
Quality is not the only aspect we need to look at in 2022. As data is changing faster than ever, analyzing static data is not profitable anymore. Instead of analyzing large amounts of static data once per month or per week, businesses should take away insights from streaming data in real time. Adapting instantly to changing environments, finding new patterns, and acting on them rapidly is a key goal for large organizations where competition is fierce.
Higher Adoption of Business Intelligence Tools & Growing BI Budget
As using business intelligence tools require increasingly less expertise and computation, and becomes easier to learn by employees who don’t have a background in IT or data mining, we will see an increase in the adoption of BI tools and technologies in 2022. Being able to execute analytical functions such as exploring data sets or performing data-mining tasks more easily will transform the way businesses approach data analytics going forward.
Of course, the high number and niched use of BI tools means that the employees must learn how to properly use them. In the long-term, however, large industries like manufacturing, business services, consumer services, and retail, will benefit from having big data more available for use without hiring a different team to do this.
The ease of use of these BI tools also means an overall increase in business intelligence and big data budgets worldwide. Only in the retail sector big data analytics worldwide generated $4.85 billion in 2020, but other sectors, such as financial or technology services will also see an increase. According to Allied Market Research, this number is projected to increase to $25.56 billion by 2028.
Growing Cloud-Based Solutions and Cloud Migration
Migrating business intelligence software to the cloud will be a priority to many companies in 2022. As big data and BI solutions require greater flexibility and scalability, and companies are adopting data-driven approaches, BI vendors are releasing increasingly more cloud-based solutions. These offer a number of advantages over traditional on-premise deployments.
Cloud-based BI solutions work faster, which helps save time and operational resources, and they can be scaled up and down according to each business’ needs. They are also overall easier to deploy and configure, offer collaborative environments, and are more accessible than typical on-premise solutions: employees can access data and analytics from any device, and from anywhere in the world. Given the work-from-home movement we have seen in the past two years, migrating everything to the cloud seems to be the most feasible and forward-looking solution, and cloud-based BI tools are evolving to meet these modern needs.
As more organizations need a solution for managing unstructured data, the notion of data fabric emerged in 2022 more so than in previous years. Data fabric started as a vision but will probably transform into a set of architectural principles of data management since it represents more than just a combination of traditional and modern technologies - it is actually a whole new design concept. This design concept shifts the focus from traditional data management to modern solutions, such as AI-enabled data integration, to reduce human errors and overall costs.
Data fabric helps make data available where it’s needed, no matter where the data lives. It involves integration of data sources across multiple platforms and users and that is why the data fabric technologies need to be open, standard-based, and work across environments. Given that 90% of world data is now unstructured (i.e. videos, log files, sensor data, X-rays, and so on), data fabric aims to bridge unstructured data storage, with file storage and object storage, and ultimately with data analytics platforms (data lakes, ML & NLP, image analytics).
Using a design concept like data fabric means connecting and eliminating silos across different enterprise data storage setups. That is why data fabric should be compatible with various data delivery styles (such as ETL, streaming, replication, messaging, data virtualization or data microservices), and it should support all types of users including IT professionals, for complex integration requirements, and business employees, for self-service data preparation.
As we can see from these main big data trends, the focus in 2022 will be on leveraging different technologies for better data quality, and overall better, more architectural data management. Real-time analysis and scalability will also become key points for organizations trying to adapt instantly to a changing environment and discovering new patterns and insights as quickly as possible. The overall budget for big data and BI technologies is also expected to see an increase throughout 2022, and the trend of migrating to the cloud will help move forward with cloud-based BI solutions.
About the Author
An enthusiastic writing and communication specialist, Andreea Jakab is keen on technology and enjoys writing about cloud platforms, big data, infrastructure, gaming, and more. In her role as Social Media & Content Strategist at eSolutions.tech, she focuses on creating content and developing marketing strategies for eSolutions’ social media platforms to maintain brand consistency.