I’ve been recently very interested in what can bussiness do to make employees (like me:D) feel passionate about the work we do. We spent almost half of hour lives working, so I think it’s quite important feeling passion in our job, finding a meaning on it and avoiding the boredom with lot of creativity and autonomy.
Here I leave you with the article ”The Twelve Attributes of a Truly Great Place to work” by Tony Schwartz, president and CEO of the Energy project.
Sense paraules.
Look at this interesting post in KDnugets (a forum for data-mining). It’s about 20 free data repositories integrated with R for instance access. The list:
As Vincent Granville from AnalyticBridge remarked, one of the next step and challenge to go further with bussiness analytics is to integrate all the inside data with the massive bunch of “outside” data to develop more complex and acurate models.
I’ve just discovered this incredible web page:
The idea of this icelandic starup it’s simple: they try to satisfy the current needs of information centralizing in one place data from many different sources. They present the data as time series and tables. They provide information from 14,000 data sets and have collected more then 100 million time series! Just as an example, have you ever asked if it’s true that in New Zeland they have more sheeps than humans?
It’s surprising that I’ve been so much time not knowing of its existence. It’s even more surpring the way it can look for the data you request and how can combine lots of different fonts to give you a great bunch of time series. The level of detail is also remarkable. For example, here we can observe the evolution of the apple trees since 2002 (in hectares) for the Golden and Fuji variety. In this case, the engine has also found 2 values for the input selection.
If this was not enough, they have develop an extremely quite simple system to integrate this glorious data-base into R for instance access to all their time series. With the new package rdatamarket, it’s trivially easy to import those time series into R for charting, analysis, or anything. Here’s what you need to do:
L’altre dia un amic meu es volia canviar de mòbil i ens va preguntar quin terminal agafar. Ja portava temps fent-me una idea del que jo creia que era el món dels mòbils i aprofito aquest post per compartir-ho amb qui pugui resultar-li útil.
Lo primer que s’ha d’entendre és la diferència entre Android (de Google) i iPhone (de Apple). L’Android és un sistema operatiu mentre que iPhone és terminal i sistema operatiu. Què vol dir aquesta flipadeta? És exactament el mateix que passa amb els ordinadors: dir que vols un apple, saps que només podràs comprar ordenadors de la marca apple, mentre que si volem un “windows” tenim molts ordinadors diferents a elegir, marques, models i prestacions.
Un cop explicat això,
I have no doubt. This is going to be one successfull future bussiness. Just take a look at their presentation video, it’s quit unbelievable. Can you just imagine the grat amount of doors that this new industry of 3d-printing can open? Is it possible than in 10 years time all of us have one of those in our homes, and downloading all kind of objects directly from the net?
And here is the prove that other people also think that this invention it’s not just a child game.
Recently Midwest ISO, together with Paragon decision technology, won the Edelman award for their achievements in handling the complexity of the production and transportation of electric power. To imagine where is the complexity of such a job, just say that they are the responsible for the daily delivery of energy in 14 US states and another one in Canada. They have to manage over a 1.000 power plants and nearly 60.000 miles of high voltage transportation lines to deliver electric power to 40 million end user customers! So just try to make a mental picture of such a graph… If this was not enough, add to this problem the fact that the energy demand is a time dependent resource witch varies in many ways along weeks, month and years. Their job has been to made this puzzle as much efficient as possible, trying to satisfy demand and not waste energy in all the nodes of the graph. It has been estimated that Midwest realized a cumulative saving from 2007 through 2010 of at least $2.1 billion!!
After such complexity and good results, it’s understandable that if they had done a good job, they diserve such a distinction of the OR world.
It’s incredible to watch comlex system and locate in them points where they can be optimized. The electrical supplyoing graph is just an extreme example, but everywhere there are lots of other systems that can also be optimized. Many of them work as “old manners” and nodes insist in doing evrything by their own instead of analyzing the problem as a hole.
