25
Big Data opportunities for Market Research

Big data may 2012

Embed Size (px)

DESCRIPTION

Presentation to BIG conference from May 2012

Citation preview

Page 1: Big data may 2012

Big Dataopportunities forMarket Research

Page 2: Big data may 2012

Q. How big

is Big Data ?

Page 3: Big data may 2012

Byte B 100 1Kilobyte KB 103 1,000Megabyte MB 106 1,000,000Gigabyte GB 109 1,000,000,000Terabyte TB 1012 1,000,000,000,000Petabyte PB 1015 1,000,000,000,000,000Exabyte EB 1018 1,000,000,000,000,000,000

Computer Science 101

Page 4: Big data may 2012

A. Bigger than Shakespeare?

t1B

x 1000 =

1 KB

x 1000 =

1 MB

x 5 =

5 MB

Page 5: Big data may 2012

A. Bigger than your pocket?

x 100 =

500 MB

x 2 =

1 GB

x 60 =

60 GB

5 MB

Page 6: Big data may 2012

A. Bigger than the known universe?

60GB

1 TB

x 140 =

140TB

x 20 =

Page 7: Big data may 2012

A. Bigger than a day at Google?

2.5PB

x 5-10 =

140TB

x 11 =

13PB/year

1.5PB

x 1.5 =

20PB/day

or

Page 8: Big data may 2012

A. Bigger than the sum of human knowledge?

x 250 =

All words ever uttered by the human race since the beginning of time

5EB

20PB/day

Page 9: Big data may 2012

A. Bigger than the Internet?

x 100 =

All words ever uttered by the human race since the beginning of time

5EB

500EB

All data to flow across the Internet this year

Page 10: Big data may 2012

Pause to think…

These were the biggest data sets I could find statistics for

and both would be good raw material for Market Research

if we could find a big enough table to put them in

Page 11: Big data may 2012

There is a simpler answer

Q. How big is Big Data?

A. Bigger than we can easily handle

(and usually unstructured)

Page 12: Big data may 2012

Why now?

More activities are digital, creating “data exhaust”

More sensor devices creating digital data: “chips with everything”

More connectivity: data can be networked

Storage is cheap and getting cheaper

Page 13: Big data may 2012

Big Data means different things

Scientists: new frontiers of knowledge

IT industry: projects > 1 PB

Investors: opportunity for growth

Commerce: efficiency, decision-making

Google: business as usual

Page 14: Big data may 2012

Market leaders in commercial Big Data

Data ownership

Data Analytics

Data Storage

Page 15: Big data may 2012

Commercial applications for Big Data

Micro-segmentation / mass customisation

Predictive propensity modelling

Digital marketingPricing optimisation

Operational performance improvement

Forecasting

Product improvement / development

Page 16: Big data may 2012

The Big Data hypothesis for Market Research

“The availability of large quantities of consumer data

will allow us to generate new and/or lower cost

consumer insights through analysis of that data”

Page 17: Big data may 2012

Big Data sets for Consumer Insight

Social media

Web traffic

Transactional

Geodemographic& geolocation

Page 18: Big data may 2012

And let’s not forget qual and ethnography

Social media

Blogs

Page 19: Big data may 2012

A change in research process and mindset

Controllable sample

Extendable conclusions

Data on real world outcomes

Statistics

Analytics

Actionable insights

Hypothesis-led / inductive

Fact-led /Deductive

Page 20: Big data may 2012

Transferable Research skills

Understanding client needs

Asking/framing the right questions

Knowing what to look for

Interpretation

Synthesising insights

Page 21: Big data may 2012

And researchers have a grasp of statistical techniques used in data analysis

Pattern recognition

Trend analysis

Classification

Cluster analysis

Regression analysis

Page 22: Big data may 2012

Big Data firms want a piece of our action

Google Consumer Surveys

Facebook research

Dunnhumby entered the Honomichl 100

Nectar are launching an online panel

Big Data tells us what, but

not why

Page 23: Big data may 2012

How can Researchers respond?

1) Find a friendly data scientist

2) Get involved: understand available data sets

3) Talk to clients: what data? what needs?

4) Get creative: how could the data meet client needs?

5) Experiment (with your friendly data scientist)

6) Complement data with traditional research

Page 24: Big data may 2012

Success for Market Research in Big Data =

ResearcherData

scientistTechnology

+ x

Page 25: Big data may 2012

No-one is doing this well yet:

there is an open goal for whoever gets it right