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jerryking : resource_management   3

What Cars Did for Today's World, Data May Do for Tomorrow's - NYTimes.com
August 10, 2014 | NYT | Quentin Hardy.

General Electric plans to announce Monday that it has created a “data lake” method of analyzing sensor information from industrial machinery in places like railroads, airlines, hospitals and utilities. G.E. has been putting sensors on everything it can for a couple of years, and now it is out to read all that information quickly.

The company, working with an outfit called Pivotal, said that in the last three months it has looked at information from 3.4 million miles of flights by 24 airlines using G.E. jet engines. G.E. said it figured out things like possible defects 2,000 times as fast as it could before.....Databricks, that uses new kinds of software for fast data analysis on a rental basis. Databricks plugs into the one million-plus computer servers inside the global system of Amazon Web Services, and will soon work inside similar-size megacomputing systems from Google and Microsoft....If this growing ecosystem of digital collection, shipment and processing is the new version of cars and highways, what are the unexpected things, the suburbs and fast-food joints that grew from cars and roads?

In these early days, businesses like Uber and Airbnb look like challengers to taxi fleets and hotels. They do it without assets like cars and rooms, instead coordinating data streams about the location of people, cars, and bedrooms. G.E. makes engines, but increasingly it coordinates data about the performance of engines and the location of ground crews. Facebook uses sensor data like location information from smartphones
Quentin_Hardy  data  data_driven  AWS  asset-light  massive_data_sets  resource_allocation  match-making  platforms  resource_management  orchestration  ecosystems  GE  sensors  unexpected  unforeseen  Databricks  Uber  Airbnb  data_coordination  instrumentation_monitoring  efficiencies 
august 2014 by jerryking
Max Levchin talks about data, sensors and the plan for his new startup(s) — Tech News and Analysis
Jan. 30, 2013 | GigaOm |By Om Malik.

“The world of real things is very inefficient: slack resources are abundant, so are the companies trying to rationalize their use. Über, AirBnB, Exec, GetAround, PostMates, ZipCar, Cherry, Housefed, Skyara, ToolSpinner, Snapgoods, Vayable, Swifto…it’s an explosion! What enabled this? Why now? It’s not like we suddenly have a larger surplus of black cars than ever before.

Examine the DNA of these businesses: resource availability and demand requests — highly analog, as this is about cars, drivers, and passengers — is captured at the edge, automatically where possible, then transmitted and stored, then processed centrally. Requests are queued at the smart center, and a marketplace/auction is used to allocate them, matches are made and feedback is given in real time.

A key revolutionary insight here is not that the market-based distribution of resources is a great idea — it is the digitalization of analog data, and its management in a centralized queue to create amazing new efficiencies.”
massive_data_sets  data  Max_Levchin  radical_ideas  sensors  start_ups  incubators  San_Francisco  sharing_economy  analog  efficiencies  meat_space  data_coordination  match-making  platforms  Om_Malik  resource_management  underutilization  resource_allocation  auctions  SMAC_stack  algorithms  digitalization 
february 2013 by jerryking
Googling Growth - WSJ.com
APRIL 9, 2007 | Wall Street Journal | by CHRIS ZOOK. Rapid
shifts in markets and technologies are forcing companies of all sorts to
change direction faster than ever. Many management teams are tempted
by "big bang" solutions: dramatic, transformative mergers or aggressive
leaps into sexy new markets. The success rate for major, life-changing
mergers is only about one in 10. For most companies, reinvention of a
core business doesn't have to involve such high levels of risk. The
solution lies in mining hidden assets -- assets already possessed but
not being tapped for maximum growth potential.
One way to open management's eyes to hidden assets is to identify the
richest hunting grounds, usually camouflaged as hidden business
platforms, untapped customer insights, and underused capabilities.
accelerated_lifecycles  Apple  assets  Bain  big_bang  business_models  Chris_Zook  core_businesses  customer_insights  GE  growth  hidden  high-risk  iPODs  latent  life-changing  M&A  mergers_&_acquisitions  moonshots  Nestlé  Novozymes  rapid_change  reinvention  resource_management  Samsung  success_rates  transformational  underutilization 
february 2010 by jerryking

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