Real-time Data Analytics Predictions for Businesses
Advanced data management and analytics will enable new automated and AI solutions for real-time business performances. For their business growth and survival, brands are beginning to revamp their e-commerce and in-store channels. Data science, visual analytics and AI-enabled business processes come together as the race for business optimization expedites. Data science and analytics have started […]
Advanced data management and analytics will enable new automated and AI solutions for real-time business performances.
For their business growth and survival, brands are beginning to revamp their e-commerce and in-store channels. Data science, visual analytics and AI-enabled business processes come together as the race for business optimization expedites. Data science and analytics have started to become imminent factors for crucial business decisions.
To address the new and unseen challenges that companies face due to the pandemic, experts anticipate an environment for the exponential development of AI-based applications – A data hub that would connect at-rest and in-motion data assets along with insightful collaborations among data scientists, DevOps and ModelOps developers in 2021.
The convergence of data science and DevOps will incorporate new ML engineers and cloud vendor services with open-source and home-grown tooling. As a result, a holistic workflow and the development of event-enabled business solutions in hybrid clouds will integrate businesses. This major collaboration of entities will urge brands to enhance their data literacy across functional areas.
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Initial data that surfaces at business events is integrated on event streams and companies monitor trends and take decisions based on this real-time data-in-motion. As we advance into this year, experts predict a combination of data-in-motion and data-at-rest feeding self-learning systems in operations. A downward trend in solutions entailing moving data and an upward trend in solutions analysed on event streams is expected.
Another prediction by data experts revolves around the factor that AI-infused apps in multi-clouds collect updates from various sources, which fuel the underlying data. Consequently, a traverse between cloud and on-premise data assets is expected. Advances in data technologies will transform sources of data through cloud integration services and administer real-time data to manage situational awareness.
With new ad-hoc combinations, advanced data virtualisation and master data management will be the founding technologies. It would combine at-rest and real-time data sources to update crucial analyses, resulting in a confluence of data virtualisation.
At the same time, WFH culture continues to grow in organisations causing DevOps and ModelOps developers to manage data in several environments. As a result, a hybrid machine learning engineer is expected to join the system. While working closely with data scientists, business users and design teams, their job role would be to manage data science workflow in hybrid cloud environments.
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Experts warn businesses to not deploy and manage models in different production houses. Models can cause a credit or cash risk and also reputation risk. To manage such dangers, model durability and deployment with rules-based systems will provide security. CCAR, CECL, CCPA, and other emerging guidelines on AI should be followed.
While the business economy takes new shape in this pandemic, and brands are uncertain about their level of progress, experts strongly believe advanced data management and analytics would be crucial for survival in the years to come. This would also cause data literacy to be of vital importance across all business platforms.
