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Data is critical to every aspect of running a modern business. Today, data is available in several types of media, such as text, images, logs, graphs, and more. Not only does data come in different forms, but it also comes in from various sources, such as social media, edge devices, IoT, sensors, and more.
These types of data generated and collected by businesses have expanded significantly. It is increasingly clear that rapid growth in volume, velocity, and variety of available data is straining traditional strategies for data management, analysis, and machine learning.
Data analytics is the process of analyzing raw, structured, and unstructured data to identify trends and answer questions. This type of analysis allows business to interpret and communicate meaningful data patterns. Various approaches to data analytics include:
Analytics workloads and machine learning models can be applied to business data to describe, predict, and improve business processes and outcomes.
In the digital age, organizations rely on data analytics and machine learning models to make well-informed and data-driven decisions. Having the right infrastructure in place to run such workloads speeds up the analytics and machine learning processes and provides a robust architecture to prevent any unplanned outages. Data analytics infrastructure needs to be powerful, highly available (HA), and flexible, as does any infrastructure designed for business-critical applications.