Review on the article- Big Data Hype (and Reality)

1. REVIEW ON THE ARTICLE
Big Data Hype
(and Reality)

2. WHAT IS BIG DATA?

3. MAJOR APPLICATION OF DATA
MINING AND ANALYSIS:
BETTER PREDICTIONS
BETTER FUTURE

4. Three areas where
better prediction of
consumer behaviour
would clearly be
valuable 

5. 1. FILM RATINGS
As a company that thrives when people consume more
content, Netflix routinely serves up personalized
recommendations to customers based on their feedback on
films they’ve already viewed. This is a prediction challenge.
Famously, five years ago, the company launched a
competition to improve on the Cinematch algorithm it had
developed over many years.

6. Before the competition, the error of Netflix’s own
algorithm was about 0.95, meaning that its predictions
tended to be off by almost a full “star.”
With three years of effort by some of the world’s best data
mining scientists, the average prediction of how a viewer
would rate a film improved by less than 0.1 star.

7. Netflix Price Competition Progress

8. 2. CUSTOMER ATTRITION
If predictive analytics drawing on big data could accurately
point to who in particular was about to jump ship, direct
marketing dollars could be efficiently deployed to intervene,
perhaps by offering those wavering customers new benefits
or discounts. Analysts measure how accurate the list of
potential churners is by using a measure called “lift.”
With the benefit of big data, will marketers get much better
prediction accuracy?

10. A study suggested that the answer is no.
Very similar lift curves have been reported in other work.
All this suggests a limiting factor to prediction accuracy for
consumer behavior such as churn.

12. 3. WEB ADVERTISING RESPONSE
Let’s turn to the challenge of predicting the click-thru rate
(CTR%) of an online ad — clearly a valuable thing to get
right, given the sums changing hands in that business.
The average CTR% for display ads has been reported as
low as 0.1-0.2%. Behavioral and targeted advertising have
been able to improve on that significantly, with
researchers reporting up to seven-fold improvements. But
note that a seven-fold improvement from 0.2% amounts
to 1.4% — meaning that today’s best targeted advertising
is ignored 98.6% of the time.

13. Predictive analytics can figure out
how to land on Mars, but not who
will buy a Mars bar.

14. If you’re counting on Big Data to make
people much more predictable, you’re
expecting too much.

15. Randomness inherent in human behavior is the limiting
factor to consumer modeling success.
Marginal gains can perhaps be made thanks to big
data, but breakthroughs will be elusive as long as
human behavior remains inconsistent, impulsive,
dynamic, and subtle.

16. Big data analytics can improve predictions, but the biggest
effects of big data will be in creating wholly new areas.
So you should expect big data to have big impact. And you
can bet that it will help machines interact more usefully
with our unstructured, changing, and sometimes downright
confused human ways.
But if you’re counting on it to make people much
more predictable, you’re expecting too much.

17. BY- TANAY KARNIK