Watch this space: Curiosity drives cloud computing
I like asking questions and I like getting good answers even better. It is because of that, I now have a love / hate relationship with search engines. Most of the time they give me a 50% answer, a kind of direction, a suggestion, a kind of coaching to the real answer. It is like the joke about the consultant; “the right answer must be in there somewhere, because he or she gives me so many responses”.
In spite of all kind of promises, search engines have not really increased their intelligence. Complex questions with multiple variables are still nearly impossible to get answered and the suggestions to improve my question are mostly about my spelling or because the search engine would have liked a different subject to be questioned on.
So nothing really good is coming from search engines then? Well most arguably search engines have brought us cloud computing and a very powerful access to lots and lots and lots of data, otherwise known as ‘the world wide web’.
No wonder I envision that powerful access and cloud computing are the two most important values we want to keep while increasing the capacity and intelligence to do real analytics on large data sets.
In an upcoming whitepaper of the Atos Scientific Community, these 2 elements are explored in great depth:
• Data Analytics needs cloud computing to create an “Analytics as a Service” - model because that model addresses in the best way how people and organizations want to use analytics.
• This Data Analytics as a Service – model (DAaaS) should not behave as an application, but it should be available as a platform for application development.
The first statement on the cloud computing needs suggests we can expect analytics to become easily deployed, widely accessible and not depending on deep investments by single organizations; ‘as a service’ implies relatively low cost and certainly a flexible usage model.
The second statement about the platform capability of data analytics however, has far reaching consequences for the way we implement and build the analytic capabilities for large data collections.
“Architecturally, and due to the intrinsic complexities of analytical processes, the implementation of DAaaS represents an important set of challenges, as it is more similar to a flexible Platform as a Service (PaaS) solution than a more “fixed” Software as a Service (SaaS) application”
It is relatively easy to implement a single application that will give you an answer to a complex question; many of the applications for mobile devices are built on this model (take for example the many applications for public transport departure, arrival times and connections).
This “1-application-1-question” approach is in my opinion not a sustainable business model for business environments; we need some kind of workbench and toolkit that is based on a stable and well defined service.
The white paper describes a proof of concept that has explored such an environment for re-usability, cloud aspects and flexibility. It also points to the technology used and how the technology can work together to create ‘Data Analytics as a Service’.