will struggle with how to successfully adopt this approach to software development, and it’s up to open source solution providers to commit to making the transition as painless as possible.
Operating systems have always served two primary purposes: to enable software and developers to consume and take advantage of hardware innovations as they become available and to deliver a stable foundation on which applications can run. Moving forward, operating systems will continue to evolve in these ways to power the cloud. Take Linux as an example. Linux was developed on and for the internet and has evolved to support 8 out of every 10 cloud-based applications today. This is because it’s portable, secure and reliable.
The cloud demands choice and flexibility, and we believe that’s what will maintain Linux the cloud operating system well into the future. As organisations move to the cloud, the OS will continue to deliver a critical foundation. The question is which ones are the best fit—those based on a traditional, walled-garden approach that fosters vendor lock-in, or the ones built on open source, that originated on the internet and are tailor-made for the cloud.
Global data is estimated to increase 50-fold by 2020, and our customers recognise that they need to harness the increased volume, variety and speed of data if they are going to succeed. Not only are they concerned about how to store tremendous amounts of data; they’re also struggling with how to analyse it, because data is only valuable when you can gain insights from it to make decisions.
While businesses have always run on information, big data introduces data sets so large and complex that storing them for easy retrieval is cumbersome. This data comes from a variety of structured and unstructured sources, including business transactions, sensor data, audio, video, click streams, log files and more. IT must ensure that big data is an asset and not a cost by supporting the ability to store, aggregate, normalise, and integrate it from all sources across multiple systems.
But storing the data is only valuable if you can use it. Big data also circumvents our ability to apply a traditional business intelligence approach to working with the data to make decisions. While batch versus real-time data analytics is currently split, companies are putting even greater focus on shifting more analytics to be done in real time. IT must continue to invest in the right transactional and big data