Data Analytics: New Edge for Success
Digitizing the Enterprise by Leveraging Analytics and Modern...
Promise of Data Analytics
Business and Data Analytics to Achieve Productive Outcomes
Data as a Business Resource
Jamie Adams, CIO, Mspark
Soaring cloud costs Five ideas for effective data platform cost...
Sean Xu, Vice President -- Information Management, MGM Resorts International
Accelerating Product Development with Big Data
Kyle Cline and Andrei Khurshudov, Caterpillar Inc.
Where can You Use Quantum Computing in Data Analytics?
Aaron McClendon, data scientist and practice lead, Aimpoint Digital
Thank you for Subscribing to CIO Applications Weekly Brief
Top Big Data Analytics Trends to Watch for in 2022
Until recently, most qualitative insights had to be gleaned from reams of quantitative data by data scientists or analysts. With augmented data, however, systems can use artificial intelligence and machine learning to suggest insights in advance.
Fremont, CA: The overall significance of data and information within organizations has grown. Companies have also witnessed the continued rise of megatrends such as IoT, big data – even too much data – and, of course, machine learning. This is in addition to the ongoing maturation of other, perhaps less well-known, but equally important data initiatives, such as cloud governance and integration.
Herer are some trends gaining momentum in 2022:
IoT and the Growth of Digital Twins: Even though the Internet of Things was on everyone's lips in 2021, the buzz surrounding the digitization of our surroundings and the implications for data isn't going away. The frenzied growth of IoT data, as well as many organizations' continued inability to handle or make sense of all that data with traditional data warehouses, remained a major theme in 2021. And it is expected to gain traction in 2022, presenting very real business opportunities for more organizations.
The ongoing growth of digital twins, which are digital replicas of physical objects, places, people, and systems powered by real-time data collected by sensors, is adding fuel to this ever-expanding fire. According to some estimates, by 2020, there will be more than 20 billion connected sensors, potentially powering billions of digital twins. To realize the full value of that data, it must be integrated into a modern data platform via an automated data integration solution that performs de-duplication, data cleaning, and unification from disparate and unstructured sources.
Augmented Analytics: Until recently, most qualitative insights had to be gleaned from reams of quantitative data by data scientists or analysts. With augmented data, however, systems can use artificial intelligence and machine learning to suggest insights in advance. This is expected to become a common feature of data preparation, management, analytics, and business process management, resulting in more citizen data scientists as entry barriers fall – especially when combined with natural language processing, which allows users to query their data using normal speech and phrases.