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We can only be as effective as the information we have available to us allows.

 

Effective thinking processes will fail to produce the desired results if the processes are applied to incorrect or out-of-date information.

 

How has training changed to reflect the importance of accurate information?

 

How will it change in the future?

 

What is the role of mobile devices in the classroom? Outside of the classroom?

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My conversation on November 7, 2013 with Brad Kolar inspired me to seek what Drucker might have said about "data" - which brought me to this posting.

 

Drucker encourages us to move from "data literacy to information literacy" by answering two basic questions:

 

"What information do I need?"

"What information does my enterprise need?"

 

Data without context is likely to be subject to wide interpretation with prejudice

Data + context = Information

Information + Predictable Results = Knowledge

Knowledge + Character = Wisdom

Great post, Paul.  I think if Drucker were alive today, he'd probably even take it one step further and suggest that we move from information literacy to contextual literacy.

The ironic thing about this new world of Big Data and information is that the value isn't actually in the data.  It's in the relationships that can be found through the data.  By finding patterns we can learn more about our business, our customers, our learners, and the world in general.  But frankly, that's the easy part.  The successful leaders are the ones who can figure out how those patterns relate to their business. And, more importantly, determine which patterns don’t matter.  As Nicholas Taleb pointed out, it’s true that we find a lot more insightful connections as we combine more and more data.  But the downside is that we also find even more connections that are completely random or don’t make sense.  We need to train people how to tell the difference and not just act on the “data”   

Unfortunately, no calculation can answer the question of what is relevant to the business.  Leaders need to generate the final set of patterns and relationships by using their understanding and experience to connect the output of the analyses to their specific business. 

For example, a classic (and old) "big data" story is about a convenience store that through a market-basket analysis discovered that beer and diapers were commonly bought together.  That's an interesting and unexpected relationship found in the data.  But it begs the question, “where should you place the diapers and beer relative to one another?”  The answer can't be found in the data.  It is found in your business strategy.  If you are Wal-mart you put them at opposite ends of the store.  If you are Nordstrom, you'd put them right next to each other.  Of course, another consideration is where the beer and diapers currently are located.  If you are Wal-Mart and the diapers and beer are already separated, then the data actually tells you not to do anything (or more accurately, tells you not to make any changes).  Without a good understanding of your business, how it makes money, and its strategy, having the result of the data analysis is worthless. It might even cause you to do the wrong thing or take action when no action is needed.

The challenge is that context takes time to learn and often it is the first thing to get dropped when we realize that our eight hour course has twelve hours of content packed into it (or more realistically our one hour virtual session has three hours of content in it).  In recent years, for a number of reasons, training has become very transaction and fact-based.  Yet, the transactions and facts are what are changing the fastest.  We need to find ways to help people understand and learn to think critically about their context so that they are ready to handle whatever data and facts come their way.

Brad, the concept of contextual literacy makes good sense. Your closing paragraph is a critical message for learning leaders and instructional designers.

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