Qualitative techniques can be used on big data to understand the "whats", derive the "so whats", and hypothesize the "now whats". What led to this insight was that qualitative research allows for sorting big data into different blocks of attitudes and motivations to provide context and explanations. A hybrid approach combining quantitative and qualitative methods is needed to generate narratives and stories from large datasets.

1. BIG DATA BRAINSTORM
The Results!

4. Your
Name You will see a star with your name
on.
For 10 Seconds!
This is your cue to step up to the
mike ;)

5. You have
1.30‘‘!
Our Insight
Our Reasons Why… On the home run!
If you haven‘t And more reasons why…
Still time to go!
Feels like ages?
saidseconds
(15 it now…
(60 seconds…)
(30
to go…)

6. Bert
Hendricks

7. who is interested in big data
why the interest in big data

8. every organization that want to be/stay competitive SHOULD be interested in big data.
This does not only concern product brands, but as well employer brands for example.
So 'WHO' - every organization, 'WHY' - to be/stay competitive, as the consumer has
taken over the control about the brand name / brand community / brand image.
qual analytical techniques can be used on big data
- split massive data into smaller observations
understand the story of the customer
Qual research connects the dots where big data informs without explanation. Qual gives you the why behind the story.
By combining qual and big data I can picture today's story faster and better, so I can spend more time & budget on the story of
tomorrow (ideation).
key: qual techniques can use big data to set a stage/current context and qual analysis can help set the whats & so whats to build
hypotesis for now whats.

9. Gregg Karin
Fraley Jorgensen

10. Our key insight was it would take a HYBRID approach, blending quantitative
tools (such as search engines and text processing engines) with open ended
questions such as those used in qualitative.
Clearly, we all need to tolerate ambiguity and de-mystify BIG DATA in order to move
forward with actually using it.
What led us there
Our concerns were related to context, sorting out the trash, finding gems, and anonymity, And
our answers to address those concerns had to do with following up the massive scans with qual
"verification studies" where traditional qual techniques can be used.
Ultimately, a narrative, a story needs to be the output, a result of the hybrid approach.
And...wouldn't it be nice if clear business success stories were created.

11. Vartika

12. 90 Sec Insight
Qualitative analysis can work on BIG data
to :
•Understand the ‘what’s’
•Derive the ‘so what’s’
•Hypothesize the ‘now what’s’

13. Q &A
• Who is interested in Big data ? What are they
interested in?
• Anyone (marketer, researcher, brand
custodian, organisation, businesses), who wish
to make informed decisions, in order to stay
competitive

14. What helped us derive this ?
• As a user, would prefer investing (money,
time, energy) on ‘future’, rather than existing
scenarios
• Current scenario and historic context
– Loads of data out in the open
– Define efficient starting points
– No need to re invent the wheel

15. Someone from
Pieter Paul’s Table

17. Otomi

18. Key insights :
Qualitative research makes it possible to sort BIG data into different
blocks of attitudes and motivations.
What led us here :
- Emotional aspects
- Psychological effects
- Short term : understand needs (+)
manipulation (-)
- Long term : human beings are not objective (+)
- value of personality (-)
- Understand “Why” / anticipating
Name of presenter : Ottomie

19. Cicero
Baggio

20. Give Big Data Face!
What led us:
People do not have a choice regarding privacy (if they want to be connected
to for example social media). People have to pay the price of privacy in order
to connect with their friends (facebook) or search on the internet (google).
Transparancy: tell me that you take my date
We need to be capable of seeing the bigger picture.
Less = more.

21. Looking
at the Borders

22. Key insight :
For new insights and trends on your
market, don’t look at the most
typical representatives of your
clusters , but look at the borders and
dive deeper at that point

23. Lisa Elder

24. Key insight:
Qualitative ‘techniques’ are not enough – the challenges presented by Big
Data can only be turned into opportunities with qualitative skills.
{It is not the tools you use but how you use them.}
What led us here:
• We have unique skills to apply to data:
• The skill of collaboration to create action plans.
• The skill to bring together diverse sources of information to identify
themes of learning.
• The skill to transfer learning into meaning within the human condition.
Presenter name: Lisa Elder

25. Indy &
Co

26. What is qualitative research
• Focus groups, in-depth interviews, ethnographics
• Understand the why & how, quant more for the “what, where & when”
• Discussion & Observations
• Analyzing and interpretation
• Understand the reason why people are behaving or thinking in a certain way and transforming it to action
• Finding pattern from small piece of data
• Getting to the emotion of people, getting to the subconsious of the people
• Understand the motivations of people
• More words than numbers, as in big data
• Getting deeper details
• Understanding what’s behind it
• Small sample, often local
• Transforms questions into meaningful hypothesis
• Find out what is relevant, eliminating the “noise”

27. What is qualitative research
• Insight:
Both, big data and qualitative research is about
finding patterns, results are words and not
numbers. In the end it’s about eliminating the
“noise” and drawing relevant conclusions.
Presenter: Indy Neogy

28. Erin &
Scott

29. How can we apply qualitative techniques to the
challenges of BIG Data?
Key Insight:
How can we generate big data insights?
• There is a place for qualitative researchers in Big Data analysis because of our intuitive
nature, explorative orientation , process, and mindset.
What Lead Us Here:
• Fulfill a need
• Cast a wide net
• See emotional & rational patterns
• Open to exploratory
• Insist on context, “the why”, not just satisfied with the “what”
• Flexibility - useful in various stages
“Seeing the tree through the woods”
Presented by: Erin Althage, Sommer Consulting
& Scott Hayward, heads up! research inc.

30. Jonathan
Gable

31. How are Big Data
people different
from Qual people?

32. “I can find the needle in
a haystack.”

33. “I make sense out of
unstructured data.”

34. “At the start of a project, I
know what I’m looking for.”

35. “I can use what I
learn as a valid basis
for decisions.”

36. Bonus Question:
Mac or PC?

37. Thank You!