Data Analytics

Business Data Analytics Solutions

Get more value from your data with an easy-to-deploy, high-performance data experience, delivering real-time analytics on an all-flash distributed platform.

A Modern Data Experience with Modern Analytics

Pure delivers a modern experience that accelerates decision-making, protects your innovation investments with adaptable architectures, and simplifies technology adoption with easy-to-use systems and software. Get ESG's report on the five ways data-driven analytics investments have digitally transformed businesses.

Accelerate Time to Insights

Power rapid insights from log analytics and more. Supercharge data operations and search. Optimise bursty indexers. Reduce operational overhead up to 10x as you scale. Speed analytics at any scale, up to tens of billions of files and objects.

Unlock More Data

Adapt to any workload and power every analytics tool from ingest to visualization. Prepare for big data challenges with cloud-optimised, unified file and object support. Deploy on-prem infrastructure with cloud-like consumption models with Pure as-a-Service™.

Increase Productivity

Simplify with easy install, easy upgrades, automatic performance tuning, and efficient, disaggregated storage. Plan for capacity and performance with Pure1® management. Subscribe to a superior choice and the only storage TCO that lowers over time.

What is Data Analytics?

Data analytics is the process of extracting actionable insights from raw data. For companies, it’s about applying the right analytical techniques to identify hidden patterns and trends that you can leverage to improve your business.

People also ask:

1. What are the different types of data analytics?

The four main types of data analytics are:

  • Descriptive analytics: Used to characterize what has happened over time (e.g., tracking the mileage on your car) 

  • Diagnostic analytics: Used to determine why something has happened or is occurring (e.g., leveraging event log files to determine the root cause of a crash)

  • Predictive analytics: Used to predict or forecast future events (e.g., predicting bottlenecks in your data storage needs)

  • Prescriptive analytics: Used to prescribe a course of action based on forecasted scenarios (e.g., predicting a future storage bottleneck and suggesting the purchase of additional capacity to prevent it)

2. What benefits can data analytics bring to your data storage system?

The benefits of monitoring and analysing your data storage system include: 

  • Improved decision-making: Predictive analytics can help you make better decisions for improvements and upgrades, preventing storage issues before they occur.

  • Enhanced security: Granular visibility of the health and performance of your data storage system can help you improve security.

  • Streamlined provisioning: Data analytics can yield actionable insights that can be leveraged to efficiently provision capacity when you need it, saving you money and resources.

3. What is log analytics?

Measurement is the key to improvement, which is why everything from apps and IoT devices to servers and cloud services automatically generate log files containing useful metrics that report on the operational status of the machine or program’s behavior during runtime. Log analytics is the practice of analysing these log files to extract actionable insights.

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“Splunk on Pure Storage returns answers in seconds; it’s very fast. Our DevOps team is evangelizing Splunk on Pure throughout the organisation, to show how powerful a tool it can be.”

Tony Garza
VP, Server and Storage Manager

“Pure Storage technologies offer speed, stability, and security for analysing medical data.”

Chang-Fu Kuo
Director of Centre for AI in Medicine
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