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In this note, I will continue with the fifth and the sixth steps in “Identifying and implementing Solutions”.  These steps are, “Extracting, Transforming, and Loading for analysis”, and “Analyzing data”.

“Extracting, Transforming, and Loading for analysis”, is also known as ETL.   From a general enterprise architecture point of view, to start with, the following from wikipedia is a great process flow model.


More detailed information is provided here:  http://en.wikipedia.org/wiki/Extract,_transform,_load.

More to come…
 
 
Locating the right data and defining the right measurement to work on the verifiable hypotheses are not equally loaded in our favor.

The search for data has been a cognitive process much longer in our psyche than carefully understanding and using right measure.  We are easily fooled by the argument of how we explain by quoting or not quoting the data source provided by others, often not even knowing the veracity of the source, and willingly or easily believe.  However, we hardly spend enough time to question, how is the measure defined?

The famous problems of people getting confused in regard to use of joint probability vs. conditional probability, or when to use mean, median or mode, when to use arithmetic mean vs. geometric mean vs. harmonic mean are enough to make people pass clueless, and often times even above college level educated people.

This is complaining about people.  The point I am driving is that it is a special cognitive process that has to be trained.


Being analytical is the next higher level of cognition and it requires systematic thinking and systematic statistical principles and concepts.  While every one would like to be analytical in thinking, deciding with least bias and knowing and keeping the amount of error in prediction to be minimal is a deeper cognitive process.


 
 

I will go through the seven steps you will end up using in doing your analytics projects. These steps will help understand how to identify and prioritize research questions, convince collection of high priority doable hypotheses, and define and acquire right data.

Business opportunities abound as the speed of how the consumers act and react to the markets and how the markets acts and reacts to consumers’ behaviors feed each other.  These opportunities have their own life cycles , of creation and destruction.

There are two major challenges organizations face, and analytics as a strategy comes to the rescue.

(1) The complexity of business processes are connected to the changing environments of how consumers act and react, and in turn contributing to the new life cycles of value generation (consumer dynamics), the creation of new generations of products arising out of the old ones (product dynamics), and together how organizations, governments, and consumers push and pull to receive their share of the value in the market place (market dynamics). Yet organizations have to abide by the ethical considerations of operations.

(2) While these complex interactions are going on in the market,  the previous generations of concepts of organizational and market efficiencies are becoming standard best practices in all organizations, eliminating any differences in uniqueness of products and services.



 
 
Even Math Professors Fail in This Simple Game – Why ?
The title is a funny disparaging statement and people say that because it is sensational and to add to that, the experiment explaining person actually works with a professor to prove this point.

The truth is it knocks off even highly trained mathematicians and statisticians, and yes even mathematics and statistics professors because some times we trust our intuition or do not listen extremely carefully.

Most of the times our intuition works, but not always. Of course, never undermine the power of careful listening.  It is better said than followed.

You will love this little video and see why you should also rely on mathematics (am i excited to say, rely on probability and statistics).  This is called ‘Monty Hall Problem’.