Top 12 Big Data Trends That Will Shape AI in 2023
While “big data” can be a misunderstood buzzword in tech, there’s no denying that the recent AI and machine learning push is dependent on the labeling and synthesis of huge amounts of training data. The global big data and business analytics market size was valued at $198.08 billion in 2020, and is projected to reach $684.12 billion by 2030, growing at a CAGR of 13.5% from 2021 to 2030.
So what do data insiders see happening in the coming year? Here are 12 big data trends to watch in 2022, from the experts.
1. AI and machine learning will increase the need for for big data analytics
There’s no question that the AI boom depends on data labeling and analysis. “Machine learning has really come along,” said Carla Gentry, a data scientist in Louisville, KY. “2022 will be the year we see more expertise, but still it will struggle, with understanding, proper usage and talent.”
2. Self-service big data tools hitting the web
With advances in data processing and cloud applications, there is a plethora of free data platforms online that make organizing and synthesizing data easy—even for beginners.
3. Analytics are still struggling to keep up
But even with all the great tools and data warehouses, analytics remain complicated. “Even with giant data warehouses now available on Big Data like Hadoop and Spark, companies still struggle to transfer data from operational systems to analytical systems,” said Zweben. “that gap and enable the seamless combination of both workloads.”
4. Data cleansing becoming an industry
In order to get training data into machine learning systems, it must first be cleansed, which means making sure that the information in a database has been checked for errors in format, duplications, etcetera. “Machine learning systems are only as good as the data they train on,” said Zweben, “and the secret is transforming raw operational data into learnable features.” The fact that someone visited an online shoe retailer, for instance, “is useful,” he said. “But knowing they went there today is invaluable.”
5. Democratization of data
Server-less, micro-service architectures are making it increasingly easy for these silo-owners to access, analyze, and manage their data without racking servers, configuring virtual machines, or even paying by the hour. Going serverless allows data owners to focus on their data application and pay just for what they use–by the minute.
6. Business Intelligence
Business intelligence or BI is already bringing changes in multiple sectors, namely marketing, consumer services, customer experiences, and the entire eCommerce segment. The value of the global BI and analytics software market is expected to be 17.6 bn USD by 2024.
The flawless and efficient data processing capabilities of BI software help companies around the world to accomplish their corporate and data goals without any hassle.
7. Predictive Analytics
Big data is empowering business organizations and data analytics stakeholders with its fundamental approach for quite a time now. It helps them to gain a competitive edge and accomplish their goals, such as better services, more sales, more customers, happier customers, and so on.
Business organizations use multiple tools to achieve these goals and predictive analysis is a common feature of these tools.
8. Cloud-Native Analytics Will Become Necessary
Gartner says that by 2022, public cloud services will assume a mandatory stance for 90% of data analytics innovation and processes. As data analytics will move to the cloud, cloud-native analytics will become a necessity for all the leaders and industry stakeholders.
Cloud-native analytics will empower the data analysts to align the right services with the right use cases, which might give birth to governance and integration overheads. Apart from an in-depth analysis of the cost and pricing models, the data and analytics leaders will also be required to prioritize workloads to exploit cloud capabilities.
9. Digital Transformation
Digital transformation stems from the ability of an organization to combine both automation and digitization. As the global business landscape becomes more competitive, more sophisticated, and extremely data-centric, Big Data emerges as one of the key drivers of digital transformation. Businesses across the globe utilize huge chunks of unstructured data to discover the hidden patterns in relation to their business models and Big Data becomes all the more important.
10. Medical Cures and Pandemic Control
In the pandemic-struck world, big data analytics and artificial intelligence assumed an extremely reliable stance in procuring the most reliable information at all times. Apart from helping in the research and development of novel treatment procedures, Big Data offered possible opportunities and sources of the right information, such as patient records, COVID tally, patient-reported travel, etc.
Medical experts use the term “precision medicine“, where medical experts are able to design a highly precise treatment procedure via big data analytics.
11. Data-as-a-Service (DaaS)
Data-as-a-Service (DaaS) is not an entirely novel concept, as it has been in use for quite some time now. DaaS refers to a data management strategy using the cloud for delivering multiple services, such as integration, storage, processing, and analytics. All these services are delivered over a network connection.
However, earlier delivering these services was a daunting task as the network bandwidth was a limiting factor, and data processing capabilities were also limited.
With Big Data analytics, Data-as-a-Service is gaining momentum, and by 2023 the market size is predicted to hit the 10.7 bn USD mark.
12. Augmented Data Management
Deloitte and Gartner cite Augmented Data Management as the latest technology trend and suggest that coupling it with AI and ML can unlock various benefits for data management.
ADM or Augmented Data Management is an application that enhances the ability to automate data management tasks. Hence, ADM is a worth-betting trendsetter in the big data landscape in 2022.