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Five reasons the cloud makes sense for big data projects


Gartner research published late last year suggested that more than three-quarters of companies were either already investing in big data, or planned to invest in it over the following two years. This suggests big data is changing the way businesses operate and delivering measurable benefits.

The emergence of the data-driven business is now something that traditional organisations increasingly need to contend with. When used well, big data analytics lets organisations make better decisions faster, engage with customers in a fluid and dynamic way, keep a better handle on business processes, and even shave the cost of business processes.

In short, big data can be powerful, giving organisations extremely detailed insight from what would otherwise be meaningless data-sets. In today’s business world, which now relies so heavily on digital processes for just about everything, the volume of accessible data is exploding exponentially. This means that there is potential for greater insight than ever before.

Without the right tools, however, this data is virtually meaningless. Even with the right tools, it is often hard to make sense of the morass of data. Big data also generally requires big processing power and storage facilities. As a result, cloud architecture is increasingly becoming the go-to solution for big data needs.

Cloud-based data analytics platforms and applications can easily deliver the resources businesses could never hope to summon internally for challenging big data projects.

There are five key ways a cloud architecture can help make big data projects successful:

1. Flexible, elastic, and scalable infrastructure

The individual elements of big data infrastructure should have the flexibility to support multiple methods of integration with each other and external elements. Elastic infrastructure helps to ensure that projects can scale up and down as required, and cloud infrastructure is currently the best option for such elasticity. Cloud makes it relatively simple to add or remove data analytics capacity on demand.

2. Infrastructure availability and reliability

As big data becomes an integral component of organisations’ everyday customer relationship management activities, it needs to maintain a level of availability and reliability that previously may not have been necessary. Big data is being wound into more and more business activities and, like all core business systems, it needs to be available at all times. If it fails, revenue could take a hit.

3. High-performance computing with low latency

Big data workloads often function differently from traditional enterprise applications, and speed is essential in almost every deployment. Infrastructure needs to support these speed requirements. High-bandwidth, low-latency connectivity is ideal to deliver the required speeds. Cloud infrastructure can usually deliver this kind of capacity.

4. Secure, compliant big data infrastructure

Big data requires big security and, often, stringent information management rules to meet regulatory requirements. The ramifications of falling short on either of these fronts can be serious and far-reaching. However, cloud-based big data infrastructure can usually deliver a consistent level of data security and information management standards to ensure all requirements are met.

5. A manageable big data ecosystem

Most cloud management tools offer infrastructure managers the ability to handle big data efficiently. These tools make it possible for managers to stay on top of shifting big data configurations to help ensure performance and efficient infrastructure use.


About the Author

Stuart-MillsStuart Mills is regional director, ANZ, CenturyLink