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BIG DATA BRAINSTORMThe Results!
YourName   You will see a star with your name                        on.                For 10 Seconds!       This is your...
You have                                                                 1.30‘‘!                        Our InsightOur Rea...
BertHendricks
who is interested in big datawhy the interest in big data
every organization that want to be/stay competitive SHOULD be interested in big data.This does not only concern product br...
Gregg      KarinFraley   Jorgensen
Our key insight was it would take a HYBRID approach, blending quantitativetools (such as search engines and text processin...
Vartika
90 Sec InsightQualitative analysis can work on BIG datato :•Understand the ‘what’s’•Derive the ‘so what’s’•Hypothesize the...
Q &A• Who is interested in Big data ? What are they  interested in?• Anyone (marketer, researcher, brand  custodian, organ...
What helped us derive this ?• As a user, would prefer investing (money,  time, energy) on ‘future’, rather than existing  ...
Someone fromPieter Paul’s Table
Otomi
Key insights :Qualitative research makes it possible to sort BIG data into differentblocks of attitudes and motivations.Wh...
CiceroBaggio
Give Big Data Face!What led us:People do not have a choice regarding privacy (if they want to be connectedto for example s...
Lookingat the Borders
Key insight :For new insights and trends on your   market, don’t look at the most   typical representatives of yourcluster...
Lisa Elder
Key insight:     Qualitative ‘techniques’ are not enough – the challenges presented by Big     Data can only be turned int...
Indy &  Co
What is qualitative research•   Focus groups, in-depth interviews, ethnographics•   Understand the why & how, quant more f...
What is qualitative research• Insight:Both, big data and qualitative research is aboutfinding patterns, results are words ...
Erin &Scott
How can we apply qualitative techniques to the                challenges of BIG Data?Key Insight:                  How can...
Jonathan  Gable
How are Big Datapeople differentfrom Qual people?
“I can find the needle in      a haystack.”
“I make sense out ofunstructured data.”
“At the start of a project, Iknow what I’m looking for.”
“I can use what Ilearn as a valid basis    for decisions.”
Bonus Question: Mac or PC?
Thank You!
Big Data Brainstorm
Big Data Brainstorm
Big Data Brainstorm
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Big Data Brainstorm

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The resulting slides from the Big Data Brainstorm at ESOMAR's Qualitative Research 2012 conference in Amsterdam.

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Transcript of "Big Data Brainstorm "

  1. 1. BIG DATA BRAINSTORMThe Results!
  2. 2. YourName You will see a star with your name on. For 10 Seconds! This is your cue to step up to the mike ;)
  3. 3. You have 1.30‘‘! Our InsightOur 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…)
  4. 4. BertHendricks
  5. 5. who is interested in big datawhy the interest in big data
  6. 6. 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 hastaken 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 observationsunderstand the story of the customerQual 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 todays 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.
  7. 7. Gregg KarinFraley Jorgensen
  8. 8. Our key insight was it would take a HYBRID approach, blending quantitativetools (such as search engines and text processing engines) with open endedquestions such as those used in qualitative.Clearly, we all need to tolerate ambiguity and de-mystify BIG DATA in order to moveforward with actually using it.What led us thereOur concerns were related to context, sorting out the trash, finding gems, and anonymity, Andour 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...wouldnt it be nice if clear business success stories were created.
  9. 9. Vartika
  10. 10. 90 Sec InsightQualitative analysis can work on BIG datato :•Understand the ‘what’s’•Derive the ‘so what’s’•Hypothesize the ‘now what’s’
  11. 11. 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
  12. 12. 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
  13. 13. Someone fromPieter Paul’s Table
  14. 14. Otomi
  15. 15. Key insights :Qualitative research makes it possible to sort BIG data into differentblocks 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” / anticipatingName of presenter : Ottomie
  16. 16. CiceroBaggio
  17. 17. Give Big Data Face!What led us:People do not have a choice regarding privacy (if they want to be connectedto for example social media). People have to pay the price of privacy in orderto connect with their friends (facebook) or search on the internet (google).Transparancy: tell me that you take my dateWe need to be capable of seeing the bigger picture.Less = more.
  18. 18. Lookingat the Borders
  19. 19. Key insight :For new insights and trends on your market, don’t look at the most typical representatives of yourclusters , but look at the borders and dive deeper at that point
  20. 20. Lisa Elder
  21. 21. 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
  22. 22. Indy & Co
  23. 23. 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”
  24. 24. What is qualitative research• Insight:Both, big data and qualitative research is aboutfinding patterns, results are words and notnumbers. In the end it’s about eliminating the“noise” and drawing relevant conclusions.Presenter: Indy Neogy
  25. 25. Erin &Scott
  26. 26. 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.
  27. 27. Jonathan Gable
  28. 28. How are Big Datapeople differentfrom Qual people?
  29. 29. “I can find the needle in a haystack.”
  30. 30. “I make sense out ofunstructured data.”
  31. 31. “At the start of a project, Iknow what I’m looking for.”
  32. 32. “I can use what Ilearn as a valid basis for decisions.”
  33. 33. Bonus Question: Mac or PC?
  34. 34. Thank You!
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