Traditional Culture Encyclopedia - Traditional culture - What are the trends in data analytics?
What are the trends in data analytics?
I. Smarter, Responsible, and Scalable AI
Artificial Intelligence and Machine Learning are delivering a greater impact, requiring organizations to adopt new technologies to build smarter, less data-consuming, ethical, and more resilient AI solutions. By deploying smarter, more accountable, and more scalable AI, enterprise organizations will bring greater impact to their business using learning algorithms and explainable systems to accelerate value realization.
Two, composable data and analytics
Open, containerized analytics architectures make data analytics capabilities more composable. Composable data analytics helps data analytics leaders connect insights and actions by leveraging components from multiple data, analytics, and AI solutions to quickly build flexible and user-friendly intelligent applications. As the center of gravity of data shifts to the cloud, composable data analytics will become a more agile way to develop analytic applications that support cloud marketplaces, low-code and no-code solutions.
Three, Data Architecture is Fundamental
Higher levels of digitization and no longer-constrained consumers are driving the increasing use of data architectures by data analytics leaders to a on the increasing diversity, distribution, scale, and complexity of an enterprise organization's data assets. Data architectures leverage analytics to continuously monitor data pipelines and support the design, deployment, and use of a wide variety of data by continuously analyzing data assets, reducing integration time by 30%, deployment time by 30%, and maintenance time by 70%.
Fourth, from big data to small and wide data
The extreme changes that the epidemic has brought to the enterprise have resulted in machine learning and AI models that are based on large amounts of historical data becoming less important. At the same time, decisions made by humans and AI have become more complex and demanding, requiring data analytics leaders to have a wider variety of data to better understand the posture.
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