Cloud computing has become such a key development in the IT industry that many of the major players have been left behind by its sudden rise. The reason for this is simple: there is often a tendency for new technological developments in the field of IT to be regarded as not necessary to an organisation functioning.

While many IT companies will experiment with new technologies, they often fail to give them the credence they deserve as useful and necessary developments. Cloud computing has solved the long-standing problem of device storage by allowing data
to be kept in an Internet-based 'cloud' rather than within the finite storage of computer memories or
external storage devices.

This has solved the vexatious issue of corrupted hard drives by allowing backup copies of data to be uploaded to the cloud, reducing the costs inherent in lost data. However, there is one factor that will continue to drive the cloud-based revolution: necessity.

Cloud-Based Data, Big Data and the Internet of Things

The fact is, data sets in modern industry are simply becoming too big for ordinary storage devices to contain. The 'Internet of things' refers to the reliance on electronic devices in everyday transactions: electronic scanning and stocktaking devices produce real-time data on stocking levels for many companies, and the banking sector alone produces reams of data on financial transactions through card processors, cash points and Internet banking applications.

The proliferation of data from these devices means that data can become unmanageable: if the storage of this data is difficult, processing and using the data is impossible without cloud solutions to help sort the mass of data into relevant sets. This has led to a move away from companies investing in hardware systems and a move towards Software as a Service (SaaS), where the technology also makes the data useful for optimising operational processes.

Therein lies the challenge for Dublin cloud computing: producing cloud solutions that produce useable and useful data without the requirement for large amounts of physical infrastructure and manual input.

Challenges Associated with Cloud Computing

Although there has been a great deal of technological progress made with cloud computing over the last few years, the technology is still very much in its infancy. The issue with cloud computing at present concerns the amount of input required to manage the data: in its most simple form, cloud computing is merely a method of accessing web-based servers with vast amounts of available storage for the data produced by company operations.

However, the objective of collating the data is to enable organisational functions to be streamlined on the basis of analysis and interpretation of that data. Investment in a specific department to sort and analyse the information defeats this objective.

Furthermore, where sensitive financial information has been collated, there is an inherent security risk with cloud-based data that is no longer under the control of the data controller.

The Future of Cloud-Based Software Systems

Cloud solutions are set to undergo a further revolution based on smart systems that self-maintain: machine learning is the newest development in cloud computing whereby the systems themselves learn to process data in such a way that the end product is relevant data that helps to manage organisational processes in real-time.

This negates the need for an additional operational department to manage the software and enables the software to fulfill its objective without incurring extra expense for the company. This 'NoOps' approach to cloud computing also extends to maintenance of the infrastructure: the traditional reliance on hardware and physical servers is giving way to serverless architecture where all the processing systems are contained within cloud-based software.

This couples with smart cloud systems to allow the software to add and remove virtual structures as required, removing the need for a large department of IT specialists and helping to create a fully agile system that is able to produce quality real-time metrics that are invaluable for strategic operational management.

Where data is of a sensitive nature such as in finance and banking, agile machine learning systems can be developed that are fully resistant to cyber attacks, enabling financial institutions to take advantage of cloud-based storage without the inherent security risks associated with data that is kept away from in-house servers. With the potential for technological development, there is every possibility that security in the finance industry can actually be improved, boosting customer confidence.

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