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Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive
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imcampos
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imcampos,
User Rank: Apprentice
1/17/2014 | 11:22:43 AM
Descriptive, Inductive plus Embedded, Non-Embedded?
Folks, 

I beg to argue the following:
  • inductive analytics is a better denomination than predictive, for the seemingly obvious reason that algorithms induce values from known data. That is what statistics and DM algorithms do.
  • embedded analytics is a better denomination than prescriptive. A specific analytics can be either descriptive or inductive, and the relevant fact here is that any of those two kinds can be embedded into an application and be used (together with "business rules" specific to the app) to take action on something.

In summary, these are orthogonal qualities, and one can visualize a 2 by 2 matrix wherreby in the rows are (descrtiptive, inductive) and the columns are (non-embedded, embedded).

Ivan Moura Campos

Belo Horizonte, MG, Brazil

 
sushantsaraswat
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sushantsaraswat,
User Rank: Strategist
1/13/2014 | 2:19:46 AM
Prototypes of Big Data analytics system in cloud
Big data analytics is going to be mainstream with increased adoption among every industry and forma virtuous cycle with more people wanting access to even bigger data.

However, often the requirements for big data analysis are really not well understood by the developers and business owners, thus creating an undesirable product.

For organizations to not waste precious time and money and manpower over these issues, there is a need to develop expertise and process of creating small scale prototypes quickly and test them to demonstrate its correctness, matching with business goals.

Following up on this, I came across and registered for a webinar on Deploy Big Data solutions Rapidly in Cloud through Harbinger's ABC model (Agile-Big Data-Cloud), it looks a promising one http://j.mp/19xJ6ew
Ali Kafel
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Ali Kafel,
User Rank: Apprentice
1/4/2014 | 11:19:44 AM
Context provides meaning and relevance
A very interesting topic!

Another way of interpreting descriptive data is as "content without context" ie. facts that describe a particular phenomenon, without meaning or relevance.

To have meaning or relevance,  context must be included.

For example, knowing that 30% of our employees use twitter at least 3 times a day is descriptive data. Not much meaning or relevance though

However, if we also know that those 30% of employees also have higher Klout scores THAN the others then that becomes predictive information - the data has meaning within a context (in this case Klout score). It says employees that tweet more are likely to have higher Klout scores

Context with multiple dimensions such as time, location, and device type (PC vs Tablet vs smartphone) help convert descriptive data into predictive or even prescriptive analysis
D. Henschen
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D. Henschen,
User Rank: Author
1/2/2014 | 8:55:43 AM
Better examples for predictive analysis
The distinctions between descriptive, predictive and prescriptive are important, particularly since so many vendors are now throwing the term "analytics" around for things like reporting that really qualify as business intelligence. On predictive, a sentiment-analysis score isn't strictly descriptive as you're calculating a value that is your best guess at an individual's sentiment. But you're not really predicting something is going to happen, as in more classic examples of prediction such as customer churn or risk. Will X customer leave or will Y customer pay off a loan? Now that's a prediction. Prescriptive analytics go one step further, telling us what type of offer to sent to which churn candidate to promote retention or what loans to approve to maintain an acceptable level of risk.  
Laurianne
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Laurianne,
User Rank: Author
12/31/2013 | 10:43:22 AM
Prescriptive anlaytics
The way Wu defines prescriptive anlaytics could be especially helpful for describing this concept to line-of-business colleagues.


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