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Definition of Strategic Enterprise Selling. With tips

"The process of engaging early at the most senior level, aligning with political and economic power, in addressing the most serious problems or opportunities. Then architecting solutions with unique compelling value while setting an agenda that disadvantages or eliminates competitors."

Post contains 10 recommendations.  Some highlights:

"A strategy is only as good as the information that leads to it."

"Be humble and seek advice while thinking through the potential consequences of actions.'

"Be first and set the agenda."

"Find and influence the ‘puppet master’."

"Have proof of your claims."

"Solutions must be embedded in the customer’s business processes."

"Anyone on your team afflicted by arrogance or an inability to listen and be briefed represents enormous risk."

"Be positively paranoid (competitively aware) yet not defensive or cynical."

"Anticipate competitor moves and set traps."

#Sales  #Executives   #AnticipateChange  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.

www.CauseAnalytics.com

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BEING AWARE OF 'COGNITIVE BIAS' -

Those of us of do information analysis as a discipline need to always be aware of the types of bias on this very useful list in an attempt to correct them before they happen. The information processed by  #AI  and  #bigdata  systems is by its nature jam-packed with these kinds of biases which we analysts can identify and protect against. I wonder how automated systems deal with this problem?

Citation -

“The biggest problems are the cognitive biases that affect us when we try to sort through information,” Shermer said, outlining four of the most common causes of errors.

They are:
1. Confirmation bias — This is the tendency to look for and find confirming evidence for what you already believe and ignoring the disconfirming evidence.

“You selectively pick out anecdotes or stories or data that seem to fit with what you are already looking for. You can have statistical programs that hopefully weed out your bias in gathering the information, but bias then shows up buried in what questions you are asking and what sort of searches you do.”

“But it’s even worse than that,” Shermer says. “You end up thinking you are being unbiased and that the facts speak for themselves, but, of course, they don’t.”

2. Hindsight bias – This is where people know what has already happened and go in search of causes and explanations for it. “That can lead you to reinterpret information to make it seem to fit what took place. That can be misleading,” Shermer says.

3. Correlation vs. Causation — Just because two observation seem to fit together, such as “everyone drinks water” and “everyone who breaks their arm drinks water” does not mean that drinking water causes broken arms. Those facts happen to line up parallel to one another, but that is different from being connected.

“This is error #1 for beginning psychology students,” Shermer says. “It’s not that one thing causes another, it’s that people who do one thing are also likely to do the second thing.”

4. Too Much Data — “It’s hard to do a reality check when you are buried in the data,” Shermer says. ”The problem is there’s so much data, so massive, that it would be surprising if you found anything that wasn’t significant. You end up with a deluge of things you could study, but which of them really matter?”

5. The Wrong “Big Question” — Not surprisingly, asking the wrong question up front can lead to what are mildly called “disappointing results.” Making sure the answer to the question actually resides in the data is the all-important first step in making big data make sense for business.

“When you have your head buried in the data set, it’s good to pull out sometimes and do a reality check,” Shermer advises.

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Trabaja diferente. Redes corporativas y comunidades profesionales  #eBook  
Publicación electrónica que analiza nuevas maneras de trabajar y pone al día sobre diferentes aspectos del trabajo colaborativo y en red de la mano de diferentes colaboradores del programa. Jesús Martínez, Marcelo Lasagna, +Jordi Graells, +Dolors Reig, +Carlos Merlino y Paco Molinero analizan, desde diferentes puntos de vista, temas relativos a la gestión del conocimiento y al trabajo en comunidades de práctica.

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