From legacy to cloud – the latest in the world of data

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Cat Mules

The reasons for going cloud with your data really do stack up.

A century ago, companies generated their own electricity. It was common for there to be a VP of electricity. As the world industrialised, electricity became a commodity and companies began to pay for it as a utility.

The public cloud is reaching the same point of maturity. Excluding the largest organisations, it now makes little sense to keep data on premise. The management, upkeep, storage, backup, and costs, and risk associated with on-premise data no longer make business sense.

Questions of security and cost

In the legacy world of data, private data centers and on-premise servers were more secure. Cloud data was insecure, expensive, or completely unavailable. This is the world many of today’s IT leaders have grown up in. So, many still prefer the security and control of keeping data on-premise.

Yet combining cloud, on-premise storage and private data centers is expensive and inefficient. A local team would have to be called on to support on-premise or data center storage – and they would have to be sufficiently trained and have the right resources at hand to do so.

Access to global best practice

Today’s cloud infrastructure enables businesses to be part of a global data community that covers analytics, threat protection, threat hunting, monitoring and risk mitigation. Companies like Microsoft have economies of scale that no small organisation can match.

Like electricity, data storage is delivered best, most cost effectively and securely, when it’s managed by large scale providers.

The investment in public community infrastructure is at massive scale – measured in trillions of dollars. The data generated on the cloud is epic too.

With security, for example, large vendors such as Microsoft receive trillions of security signals each day. Artificial intelligence and machine learning are drawn on to detect anomalies in traffic patterns on an ever more responsive basis, and counter “bad actors” in the folds of cloud data. Often malicious activity can be found and halted prior to even reaching your data.

Encryption and safety

Data encryption is understood as among the most powerful ways you can approach your data security. Encrypting data means that even if it falls into the wrong hands, it can’t be used. A decryption key is required without which data is unreadable and essentially meaningless.

Business users of Microsoft, for example, can have their digital data protected across all the levels of services – SaaS, PaaS and IaaS. You can maintain partial of full control of the keys, and determine access at different levels, including file and drive.

Cloud data is also structured for safety, and the risk of losing data is minimised with networked backups. One of the many precautions available on the cloud is to set up an additional hard drive, or an entire system, that is a replica of important information, and have it sitting on standby.

Accelerated productivity

Cloud-based data and applications are virtually accessible from any internet-connected device. The virtual private networks (‘VPNs’) used on the cloud extend the business across a public network, enabling users to share data directly.

Applications can be also be prototyped and developed at expanding speeds in the cloud, enabling users to respond to market needs quickly. For example, designers, developers, partners and customers can already have access to the same insights and a variety of tools for varying skillsets.

Live collaboration by default

Whether it’s in real-time or asynchronously through collaborated team workspaces, having insights shared and able to be talked about at any time simplifies collaboration significantly. Microsoft examples, like Microsoft 365’s OneDrive and SharePoint, are one-stop-shops – ridding us of time and energy tolls spent sifting through records and thinking about storage.

Other industries are going all-in on the cloud. For those who do, they can be reassured that they’re drawing on the collective intelligence and economics of the cloud security community.

To understand how shifting your data to the cloud might help your business, Mobile Mentor breaks the cloud data journey into tangible and actionable steps such as Mobile Device Management Health Check and 365 Accelerator. Look to their Umbrellar Connect Profile for answers on the essentials – Mobile Mentor

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Cat Mules

Umbrellar's Digital Journalist, coming from a background in tech reporting and research. Cat's inspired by the epic potential of tech and helping kiwi innovators share their success stories.

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Artificial Intelligence and Machine Learning are the terms of computer science. Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore it is an intelligence where we want to add all the capabilities to machine that human contain. Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”

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