| | March 20158CIOReviewAnalyzing the Cloud ArchitectureOver the next few pages, I would like to cover perhaps two of the buzziest buzzwords in the entire IT lexicon: cloud and a big data. After reading these pages, I hope you will find some of the practices, anti-patterns, and hard-won learning's from the life sciences industry to be of some use as you consider the role of cloud and big data in your organization. Starting with the cloud, let's explore value, architecture, and security. One of the main justifications I have heard for moving to the cloud is to save money, to manage the peaks and valleys of use and turn fixed cost into operational cost. All fine goals; however, they miss perhaps the greatest values the cloud provides: speed and agility. The cloud allows organizations from Fortune 500 pharma companies to start-up biotechs to spin up a super-computer or terabytes of data analytics in an hour to address an emerging opportunity or challenge. Likewise, if a business needs to pivot efforts from a compute-intensive in-silico chemistry screening campaign to a data-intensive genomic mining capability, delete one virtual private cloud and create another. The cloud's ability to confer speed and agility to an organization is the primary value, and yes, you save some money too.Architecture takes a different shape in the cloud. It is still deathly important to consider architectural pillars such as data models, service-oriented architecture, integration, and application layers - these are constants whether you are on-premise or in a cloud. However, something new to consider in cloud architecture is the ecosystem of applications, utilities, and solutions associated with any cloud selection. Whether you choose Azure, Force.com, or Amazon, you are also choosing a constellation of cloud-based partners and an extended cloud application stack. So when considering a cloud solution, also consider the network effect of cloud partners as you assess the suitability of a given cloud solution and architecture.Finally, the perennial question around any cloud solution: security. This is of particular concern in pharmaceutical and biotech companies where intellectual property is of paramount importance. Let me address this point with a story. I was once briefing the senior leadership of a pharmaceutical company on cloud-based solutions and fielding more than a few questions on security. I shared with them a thought experiment. "Let's suppose we build a data center and secure it with all the cyber security experts we are able to hire. Note: we are a pharmaceutical company. At the same time we challenge our friends at Amazon to build a data center in one of their virtual private clouds and secure it with their cyber security experts. Now let's have our friends at Amazon hide some data in their data center and we'll hide some data in our data center and we will play a game of capture-the-flag. It proceeds with our selecting a mutually agreed external white-hat 'friendly hacker' team that is challenged to find security vulnerabilities and breach our data centers to find the flag. Given this friendly game, which organization would be able to defend their flag from most effectively from the white hats?" Invariably, and rather quickly, the answers were uniformly that the team at Amazon would have the upper hand over a pharmaceutical company. This of course leads to the question, "So where then is the safest place to store our data?" After the chorus of, "hey-you-tricked-us!" died down, rather productive conversations ensued.The last cloudy thought I would like to leave with you is a bit provocative. I received my black belt in Tae Kwon Do By John Reynders, VP, R&D Bioinformatics, Alexion PharmaceuticalsThe waterfall, Big Bang approach to building the Delphic oracle is an unfortunate big data anti-pattern that seems to rear its head far too often. Instead, start with a clear set of focused questions, and chart your big data journey with small and iterative steps In My opinion
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