Agile Analytics: Expand the Reach and Value of Data

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Companies that embrace agile analytics are better positioned to compete. In this article we discuss this smart strategy that improves and streamlines decision-making.

What is the impact of cloud analytics applications? Migration to the cloud and hybrid platforms has changed the way companies manage data analysis, giving rise to a new agile analytics characterized by greater flexibility, scalability and speed.

A recent study by Ventana Research found that 74% of organizations are currently using or planning to use the cloud for data and analytics.

In addition to improving agility, the cloud reduces an organization's systems management needs by enabling access to data applications from the office, the field, or anywhere and from a variety of devices.

Organizations can also offload some of the systems management and administration to the service provider, allowing them to focus more on the company's core activities.

The cloud also facilitates the implementation of large-scale switzerland phone number lead Artificial Intelligence and Machine Learning models to obtain deeper insights from data.

However, with cloud adoption and the requirements that come with that transition, data must be integrated, automated and secure , which is why data governance capabilities are central to any analytics platform, especially in a multi-tenant cloud environment.

Cloud analytics applications enable greater data access and easier, more cost-effective analytics scaling .

Furthermore, using such an architecture helps to streamline the monitoring and management of data required for analysis, ensuring that data flows correctly through the system.

Agile analytics expands the reach and value of data
Analytics is vital for organizations because it fosters the ability to communicate, function effectively, and gain competitive advantage. Yet many organizations still report under- or under-utilizing their analytics capabilities.

For decades, data analysis has been performed using read-only systems with no connection to operational systems. This means that information is provided, but without guidance on how to proceed to obtain results. This is why, for many, the analysis process can be cumbersome, inefficient and resource-intensive.

AI/ML-enhanced integrated analytics operating in a cloud environment results in a more informed and productive workforce.

Integrating analytics into everyday applications helps address the lack of reliable and intelligent data to assist in business decision-making.

AI and machine learning-assisted analytics can guide people as they examine data across their organizations, and no special expertise is required to use them.

This approach is nothing more or less than an agile cloud analytics strategy that collects information from different sources, reduces ad hoc work within applications, and uses repeatable data pipelines.

This vision of continuous integration and deployment creates automated and repeatable processes that eliminate manual, slow and error-prone processes, generating efficiency and optimized data analysis work.