demian

It has the same problem as "Like".

We have come full circle.

I hope some visionaries VCs will start thinking in those terms, and start investing in this kind of high-impact life-time ventures.

The problem with that approach is metrics. How can to specify that the softwere "works"? How can you measure if it's "well done"?

I worked on a project using that approach and most of the time was spent being basically lawyers with specs instead of contracts. Horrible experience.

Migrating and integrating data is incredibly expensive. When you implement a new system you either have to migrate the data from the older system (= expensive) OR having both systems working in parallel one for "older" data and one for newer (= really expensive)

Currently the established risk-averse corporations, the owners of most of the non- social media and internet data, seem to be still testing enterprise Hadoop distributions like Cloudera and Hortonworks for daily enterprise data operations, and maybie have some projects on R&D for harnessing new "types" ok data (like sensor data from a factory floor or high granularity transport data for supply chain).

Still, I hope the best of the new tech can get a place inside the modern corporations that work on important problems like Energy and Healthcare.

IMHO I tend not to agree with the hole "Sharing Economy is The Next Capitalist Revolution" thesis.

The difference between VC-backed-startups vs traditional R&D departments is that the first one seems more short sighted, and with a tendency towards a narrow subset of IT problems with high scalability and disruptive potential, while the second one works on a wide array of industries and applications where the parent company already has the benefits of economies of scale.

Most of leading tools are HORRIBLE as programming enviroments and also are incredibly expensive.