This is a topic which quite a few of us would have been thinking about … what are the implications of cloud for IT service providers? The reason this question gains importance is because with cloud paradigm, the levers of value for customers become different from what they have been. The days of mega implementations, for example, having 500 people teams working for 4 years to deliver a project are no longer to be seen. With cloud coming into the mainstream of technology, project profiles are changing further. Release cycles are much shorter, with larger number of releases coming out in quick succession. Project lifecycles are much shorter too, as is the scope of development or customization.
One is the fact that it is no longer possible for companies to differentiate themselves on the basis of IT as technology becomes commoditized. The paradox is that when IT was a specialized space, IT was almost an afterthought in organizational strategy, while today is becoming centre-stage in the strategy landscape.
As IT becomes more commoditized and more and more of the technology components in the organization, there is more reason for organizations to oursource more of their IT functions.
For enterprise apps, for instance, the cloud era seems to be one of short implementation lifecycles, far less customization, agile development, and accelerators. This means that for services organizations, this is a whole new paradigm, with the sales folks not keen on selling these engagements as the revenue potential from these is much less, and yet, organizations have more focus on cloud engagements. Services organizations would need to change the engagement model, probably with more shared-delivery in implementation projects, and reducing the distinction between implementation and support engagements from a delivery perspective.
For quite a while, I have been thinking that maybe I am the only one who doesnt understand what these words mean. I mean, with the buzz around these concepts (and here I mean the concepts, not the technology), these must be complex concepts to define, but the definitions that I was able to understand were all quite simple.
Big data is just that … BIG! There are essentially 3 things which define it:
1. Theres lots of it! Much more than we had imagined maybe even a couple of years ago.
2. The form of this data is too diverse. There text, images, videos, and what have you. Theres structured data and unstructured data, and data comes with its own context which makes it even more complex to handle.
3. Its being generated at a very fast pace. In fact, writing this blog is adding to this big data, as is your tweet, and those pictures you post on facebook, or those status updates that you like.
I was looking for whether this definition is correct or not, and I came across this video from Ericsson Research, which describes it quite simply with an example. If you want to get past the buzz and get to understand the concept, I would suggest you watch this.
So where does analytics come into the picture? Well, if theres so much of data, theres also the fact that its very difficult to build any coherent picture from this mass of data, and this problem is addressed by the analytics domain. Analytics helps us make sense of big data!
So what does Big Data Analytics need?
1. It requires infrastructure which is able to scale up or down based on the demands of the those who are generating this data, and those who are analyzing it. This means that the infrastructure needs to be flexible, and this can be handled much more easily with cloud solutions, and this is where cloud comes into the picture of big data and analytics.
2. It requires the applications which gather this data. A lot of this data is being generated by automated systems like sensors, and through mobile devices. With the scenario of equipment communicating with other equipment, the concept of the internet of things comes into the picture. Also, with the mobile device explosion, the importance of mobile applications and mobility solutions as an integral part of the picture also becomes apparent.
3. It requires the statistical and technology foundation which will help users or systems to make sense of this data. This is the analytics piece of the picture.
Heres a nice video about an IBM study on analytics.
This is how the picture gets a little clearer, and we can see how the cloud, internet of things, mobility, big data, and analytics are coming together to create a whole new technology paradigm.