Big data refers to large volumes of structured and unstructured data that businesses receive daily. While the amount of data is large, it is what organizations do with the data that matters, as big data can be analyzed for insights to make better decisions and strategic moves. The potential of big data has received significant attention, but predictive analytics using big data may only provide marginal improvements over previous methods. Examples show that big data has helped Netflix improve movie recommendations and helped companies better target web advertising, though predictions are still often incorrect or ignored. For managers, while big data may improve predictions, its biggest impact will be enabling new areas through artificial intelligence.
2. Big data is a term that describes the large volume of
data – both structured and unstructured – that
inundates a business on a day-to-day basis. But it’s not
the amount of data that’s important. It’s what
organizations do with the data that matters. Big data
can be analyzed for insights that lead to better
decisions and strategic business moves.
What is Big data?
3.
4. The potential of “big data”
has been receiving
tremendous attention of
countless articles, meetings
and conferences.
5. Big Data at Prediction
By doing analysis on the past and
present data Decision makers
want to understand consumers
action and therefore to predict
the future.
6. will we see much improvement on the
predictions of previous-generation
methods?
Now the question is….
7. Case 1 : Film ratings
Netflix routinely serves up personalized
recommendations to customers based
on their feedback on films they’ve
already viewed.
8. Netflix launched a competition to improve on the
Cinematch algorithm it had developed over many years.
It released a record-large (for 2007) dataset, with about
480,000 anonymized users, 17,770 movies, and
user/movie ratings ranging from 1 to 5 (stars).
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.” The Netflix Prize
of $1 million would go to the first algorithm to reduce that
error by just 10%, to about 0.86.
In just two weeks, several teams had beaten the Netflix
algorithm, although by very small amounts, but after that,
progress was surprisingly slow.
9. Case 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.
10. A wireless provider has a churn rate of 2% per
month. If an algorithm can learn indicators of
customer defection, and generate a list of the
subscribers most likely to leave, and 8% of
those subsequently do leave, then this list has
a lift of 4 . Such a list would be very valuable,
given the costs of the marketing and
inducements it would save. But still, it is 92%
wrong.
Measurement using LIFT
11. Case 3: Web
advertising response
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.
We should exclude search advertising,
where the ad is always related to user
intent, and focus on the rates for display
ads.
12. 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. Google, for example, can
be considered one of the
first successes of big
data; Google’s ability to
target ads based on
queries is responsible for
over 95% of its revenue.
15. Big data analytics can
improve predictions, but
the biggest effect of big
data in managerial purpose
will be in creating wholly
new areas.
16. Big data will see its biggest and
most important application in the
realm of artificial intelligence.
IBM Watson has beaten the best
human players in Jeopardy
games. Apple’s Siri has been
conversing, with some success,
with millions of people. Google
has made significant steps
towards AI with its Knowledge
Graph.
