But that can change quickly, history has lots of examples of how asymmetric power relations between the owners of the means of production and the workforce can end in conflict and a heavy push for a regulatory framework.
I can attest to the fact that people from the US does not dress more casual than people from the Southern Cone of South America (Argentinians/Chileans/Uruguayans).
Organizational Software is, in essence, bureaucratic automation. And if you try to impose bureaucracy, control, tracking and/or regulaiton inside a deeply "brotherly" organization, you are bound to have issues.
It seems to me we are in the same place Front-End Web was a couple of years ago: There is an explosion of new tech and frameworks. BUT there seems to be a bump for the adoption of new tech for data engineering, the HIGH cost of migrating terabytes and terabytes of the company's data to a new system.
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.
Some people argue that Uber and AirBNB are not actually changing the way we use capital on a fundamental economic level, but are instead using new channels in a way, and with a magnitude, never used before.
IMHO I tend not to agree with the hole "Sharing Economy is The Next Capitalist Revolution" thesis.
By which metric would you measure this "efficiency"?
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.
Tool-level data integration is supported by tools like Spotfire, but for analyzing high volume of data you need to mantain a data warehouse using ETL code to integrate the data from all the diferent sources.
For better performance you may also need to implement an OLAP tool.
Marx was a tech writer in a way, he wrote about the emerging power asymmetries between the people that owned the machines and the people that operated them. I know you guys are not big for socialists authors in the states, but you should at least read the classics.
On the contrary, quick iterations are about reducing the risk of building the wrong thing. Of course in the normal "blaming" corporate culture this won't work, because departments are always competing over budget and deliverables that are not "perfect" will be used as ammunition against the department where the product came from.