- July 6, 2021
- Posted by: Aanchal Iyer
- Category: Big Data Analytics
Before the pandemic hit, most businesses relied on big or small data and the valuable insights derived from the data available. After the pandemic hit, various organizations that were dependent on huge amounts of historical data understood one important thing: most of these models are no longer applicable. Fundamentally, the pandemic changed almost everything, rendering a lot of data useless.
Now, forward-looking analytics and data teams are turning away from traditional Artificial Intelligence (AI) techniques relying on “big” data to a set of analytics that requires less and more varied data. Thus, transitioning from big data to small and varied (wide) data is one of the top data and analytics trends for 2021.
Top Big Data and Analytics Trends of 2021
The following data and analytics trends can encourage businesses and society to deal with the disruptive change, radical uncertainty that the pandemic has bought:
More Scalable, Responsible and Smarter AI
More responsible, scalable and smarter AI will encourage better learning algorithms, shorter time to value, and interpretable systems. Businesses will begin to need a lot more from AI systems and they will need to learn how improve the technology that they have been working with — something that up to this point has been a challenge. Thus, AI technology must be able to function with less data through “small data” techniques and adaptive Machine Learning (ML). These AI systems must also comply with federal regulations, protect privacy, and minimize bias to support an ethical AI.
Composable data and analytics
The objective of composable data and analytics is to utilize components from various data, analytics, and AI solutions for a flexible and user-friendly experience that will encourage leaders to link data insights to business actions. Most large organizations have multiple business intelligence tools and “enterprise standard” analytics. Thus, composing new applications from the bundled business capabilities of each promotes agility and productivity. Not only will composable analytics and data enable collaboration and develop the analytics capabilities of the organization, it will also increase access to analytics.
Hybrid Cloud and the Edge
Cloud computing had a huge impact on the way Big Data analytics are carried out. The facility to access massive data stores and act on real-time data without expensive infrastructure fuelled the rise of start-ups and apps that offer data-driven services on-demand. However, depending entirely on public cloud providers is not the best approach. Many companies now find themselves focussing on hybrid cloud systems, where some data is held on Microsoft Azure, Amazon Web Service, or Google Cloud servers, while other, perhaps sensitive data, remains in the proprietary walled garden. Cloud providers are now offering “cloud-on-premises” solutions that offer all features and robustness of public cloud while providing data owners full custody of their data.
The goal of XOps (data, ML, platform, model) is to achieve economies and efficiencies of scale using DevOps best practices — and to ensure reusability, reliability, and repeatability while decreasing the duplication of technology and processes and encouraging automation. Such technologies will encourage the scaling of prototypes and provide a flexible design and agile arrangement of governed decision-making systems. Overall, XOps will encourage organizations to operationalize analytics and data to drive business value.
COVID-19 has enhanced digitization, setting a new norm of doing business. Data is now, more than ever, a key supporter of the industry. The new year will see continued efforts in bridging the gap between data analytics and industry needs.