Rendering Knowledge Reuseable

 
Until not long time ago our challenge was to find the relevant information for decision making. These days our challenge is to cope with information overload in decision making!

Not only the access to information has opened up, but also our universe of available "options" and "selection parameters" have expanded in all possible dimensions.

To provide context, lets first look at the volume of data we are talking about:

• In entire human history, until 2003, 5 exabytes of information was created. (one exabyte equals 10^18 bytes).
   
• During 2006, approximately 161 exabytes of information were created.
   
• In 2010 over 900 exabytes of info got created or more than 2 exabytes every Day!! 
  (That includes over 200 billion emails and more than 28,000 hours of YouTube video uploads per day)

• And in 2020 we expect to create 35,000 exabytes of data, or 35 Zettabyte.

Along the way subject matter experts are adjusting and learning how to filter noises. But for everybody else decision making is getting harder (a.k.a decision paralysis syndrome!).

This to me is another reason why knowledge modeling is going to become more and more important. Knowledge Models can focus on what matters and learn to filter noise, as experts do.

Would you allow a computer to pick movies for you to watch?

Going through Netflix movie list I was thinking about the idea of delegating my selection process to a Knowledge Model.

This is actually similar to Netflix initiative in 2006 for creating a new recommendation engine, however in a more personalized way.
(Read more about Netflix competition with $1 million prize and the winner here: http://www.netflixprize.com//community/viewtopic.php?id=1537)

So I started by monitoring my own behavior during selection process.

In the first iteration I captured all the key attributes that I used when selecting a movie, such as: genres, main Actors/Actresses, year the movie was made, director and user ratings.
 

Then I started with capturing situation based parameters such as day of the week, my mood and so on.

For example I would normally pick a comedy when I am tired (e.g. during the week) and an action movie during holidays. 
 

So far so good … I just need to pass those additional parameters to the model.

But then I noticed my selection changes dramatically, when I have company. Not only my own preferences, but also my perception of their personality would impact the selection. For example I wouldn't pick a Mystery movie with someone who can't sit quiet!

Gradually few more variables popped up and the model was getting more complicated, however still manageable.

But ultimately I decided to consider the selection process as part of the entertainment and dropped automating it.

Nevertheless that was a fun exercise …

What is Information and what is Knowledge? There are many different views on that.

Let me first start with an example:
 

Think of your reaction if you were just told that:

- Your WBC is 20,000

- My DTI is 0

- Or the girl you just met online is 1.3 Fathom

Well, these facts by themselves will not mean much if you can't put them in context.

You wouldn't be able to make any sense of it, even if I were to tell you that for example WBC stands for "White Blood Count".
But (hopefully) your physician would react to this piece of "information" and send you immediately for further tests.

Now, the situation would have changed if you "knew" that the "average" WBC is 7000.

Although this could be considered another piece of information, it will give you enough context to decide on a reaction.

In the second scenario it should be enough to know DTI stands for "Debt To Income". In this case you would be able to infer a ratio and conclude that my financials seems to be in a good shape.

And in the last example in addition to knowing Fathom is a unit for height, you will need to the conversion ratio to a familiar unit to conclude anything.

Now, what makes any of these Information vs. Knowledge?

Here is my answer to that:

Knowledge is ability to make information actionable.  Another way to put it: Knowledge is a collection of Information along with a process (cognition) that allows to make decisions, answer questions or solve problems. The key is that Knowledge is tied to information processing.  Accordingly:
 

"3 x 3 = 9" is information

while ability to do multiplication is knowledge.

Now the question is can I externalize or outsource that ability?

The answer is clearly yes. That is what you do on daily basis when using financial advisors, Physicians or lawyers. The same can be at system level by applying knowledge models for specific use cases. 

Think of a calculator and its embedded Knowledge Model …