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10 Big Data Trends You Should Know About For 2022
From predictive analytics and data fabric architecture to data observability and data governance software, here’s a look at 10 big data trends and technologies that solution and service providers need to be aware of in the new year.
Big Developments In Big Data
Businesses and organizations have long used business reporting and data analytics on a tactical basis, answering such questions as “just what were sales in Wisconsin in 2021?” But in recent years big data management and analytics has become more strategic, spurred by digital transformation initiatives, efforts to leverage data for competitive advantage and even moves to monetize data assets.
More immediately, with the COVID-19 pandemic and its economic disruptions, businesses now realize the need to better utilize data for such tasks as managing supply chains and retaining employees. And the wave of cybersecurity incidents making headlines has brought home the importance of stepping up their data governance operations.
All this is changing how businesses collect, manage, utilize and analyze their growing volumes of data. Here‘s a look at 10 big data trends that the channel should keep an eye on in 2022.
Analyzing Data Across Multiple Clouds
Businesses and organizations are increasingly storing data in cloud platforms like Amazon Web Services, Snowflake and Microsoft Azure, even creating networks that distribute data storage across multiple clouds. But business analytics initiatives can be a challenge when data is scattered across on-premises and multi-cloud platforms.
In 2022, we’ll see increased use of new software tools such as the Alluxio Data Orchestration Platform, Qlik Forts and Starburst Galaxy that provide a unified view of data scattered across multiple on-premises and cloud systems, and access that data – wherever it resides – for data analytics tasks.
Gaining a virtual view of dispersed data and being able to access it with everyday business intelligence tools is increasingly seen as a viable alternative to the traditional data warehouse where data is collected from multiple sources and managed in a central location.