Saturday, April 6, 2013

Does Data Have Shape, Color & Taste?

Data mining has been studying and solving complex problems while taking shape and more into account.

When you think about the fact that the human brain is processing "data" all the time you can begin to see that data is... everything... Sure the core data is just 1s and 0s... but the contextual data is colors, shapes, movement, temperature, smells, tastes. etc.

Data are the "given things", in the form of the numeric records of some aspect of events. The value of data lies in generating the cues for intelligent action. Data may have shapes, colors, dimensions, taste, etc. to reflect what the nature world look like. The more contextual data you can process, the more valuable your results will be - no matter what you're trying to accomplish.

1.   Does Data Have Shape or Shapeless? 

To quote TS Eliot: "Shape without form, shade without color..." indeed, data does have a shape,  either as “S” curve or “T” shape knowledge, “U” shape turn around or “P” shape perception, or three “V” shape-Volume, Velocity, and Variety. Big Data touches the topology and inspires poetry because it is about real life and not just abstractions.

Data mining have been studying and solving complex problems while taking shape and more into account (not just numbers or attributes), they have been doing it within the communities of graph mining, link analysis, structure mining, etc, and they have long realized that any data set can be represented as a graph, simply by mapping objects to nodes and distances or similarity measures to edge weights between pairs of nodes. One example of such data is movie rating data that is transformed into graphs or networks and then used in recommender system algorithms

As one of the most valuable assets in business today, data shape will directly shape the future of business. 

2.   Does Data Have Color?

Thinking about those colorful decision hats, the purpose of Big Data is to make data-driven decision

  • “White Data”: Big Data is about reading the future, Big Data is also like a whiteboard, how should you start with? Focus on the data available. Look at the information you have, and see what you can learn from it. Look for gaps in your knowledge, and either try to fill them or take account of them.  
  • “Red Data”: Looking at those data which seems to show the red light, think fast (using intuition, gut reaction) and think slow (take advantage of Big Data), solve problems by leveraging data and intuition. 
  • Black Data”: Examine data quality; look at data cautiously, as data quality will directly impact the quality of data analysis. Try to see why it might not work. 
  • Yellow Data” Those Big Data can bring you positive signs, It is the optimistic viewpoint from data that helps you to see all the benefits of the decision and the value in, such as social network mining, customer’s positive feedback like yellow data to signal what they need for the next. 
  • Green Data” presents creativity. This is where you may develop creative solutions to a problem, data is not always about logic, there’s creativity emerging from it. 
  • Blue Data”: There’s a process to tackle Big Data, and there’re a lot of applied math programs that touch on topology that can shed light on. Blue does not always mean blue sky.

3.   Does Data have Taste? 

Data may also have tastes, for discovering sweet spots, learning some bitter lessons and catching red hot trends. 

  • “Fresh” Data: like salad, the fresher, the better taste; well-mixed, newly created data is valuable and timely; 
  • “Cool” Data: like ice cream, cool, but tasty, those specialized data can solve specific problems; 
  • “Hot” Data: like spicy food, it may not be everybody’s favorite, but it spots on the red hot trend; 
  • “Juicy” Data: like fruit juice, flowing and nutritious, it can capture the business essentials. 
Perhaps there’s also salty data, bitter data, or sour data., etc.

Data is shapeable, colorful, and tasteful, the most important thing is: Think data as the key ingredient of the world, not just abstract signal, they are the building bricks for analytic techniques to generate useful insights through the patterns, they are meaningful and even purposeful.


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