Artificial intelligence represents a revolution in data storage. Discover 5 ways AI is transforming Data Storage.
The volume of data generated by humanity is exploding. In 2020, according to a study conducted by Domo, each human being will generate an average of 1.7 MB of data per second.
In the face of this upheaval, traditional data storage methods are no longer suitable. It is necessary to use new techniques to manage and store data.
In this context, artificial intelligence could play a major role for the future of data management. Discover 5 ways AI is already transforming data storage.
AI enables scaling of data pipelines
To accommodate the large volumes of data, which can reach a pod of several exabytes, it is the Data Pipelines from the Edge to the Cloud need to scale up.
Gold, Artificial intelligence can ease this transition by allowing this scaling. In addition, robotics combined with AI could automate equipment maintenance and repairs.
AI Increases Data Storage Performance
In order for a system to be able to process several petabytes of data, it is necessary to that input/output (I/O) devices are capable of transmitting large volumes of data. AI can significantly increase the performance of these devices.
In addition, automation via artificial intelligence can help implement a self-service model to increase storage space. AI also enables a self-healing environment in which the software could potentially write or rewrite itself in order to avoid failures and ensure maximum availability.
Artificial intelligence forces a change in architecture
If a company decides to use an artificial intelligence software framework, it is important that it ensures that that its infrastructure meets the new requirements.
The IT architecture must not only be able to run the AI software, but must also allow scaling from the pilot stage to the production phase.
From the Edge to the Cloud
The data are generated and reside in multiple locations. In fact, it is increasingly difficult to transport them to the Cloud or to the core of the network. As the volume of data increases, the task becomes more complicated.
The end point sensorsThe new technologies, such as those for autonomous cars for example, also allow decisions to be made directly at the end points. In fact, transporting data to the core or the Cloud is a waste of time.
In this context, AI can help to collect and process data in offline environments and then transfer them seamlessly to an on-site data center or to the Cloud.
AI allows support for different data sources
As the diversity of data sources increases, it is necessary that the storage infrastructure allows for support a wide variety of workloads.
For example, a storage system may need to operate with different workloads such as SAP, Oracle, Hadoop, DB2, MongoDB or with unstructured data. Artificial intelligence can be used to train a system on a wide range of data sources.
Even if companies can continue to manage their data storage needs without using AI, the lack of speed and scaling will be felt. Thus, companies that refuse to adopt AI risk being lost to competitors.