Adapt or Drown – How to Swim in an Ocean of Big Data | IIeX Asia Pacific 2014
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Presentation given as part of the IIeX APAC conference in Sydney, Dec 2014. Instead of attempting to upskill market researchers or integrate data science into MR operations, what if we could translate big data into what researchers know and love -- raw data? Synopsis: In the ocean of data that surrounds and floods commercial organisations it’s time to question, what is ‘rich’ data? How can we better enable businesses to make sense of it & build strategies that drive growth. Technology has already enabled us to generate more data, this session discusses how it can be leveraged to help us to find deeper insights by connecting data with greater efficiency. Ultimately providing a framework to convert what has become a constant stream of consumer consciousness into useful, reliable and actionable outcomes for our clients.
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Adapt or Drown – How to Swim in an Ocean of Big Data | IIeX Asia Pacific 2014
- 1. How to Swim in an Ocean of Big Data ADAPT OR DROWN
- 5. Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it... Dan Ariely Behavioural Economist
- 6. Gina Lagos Director, Quantitative Research
- 7. We are working in an increasingly competitive, fragmented and risky environment The road ahead has never been less clear THE CONTEXT
- 8. Never has there been as much ‘data’ available to us • Primary research • Business intel • Transactional • Customer THE CHALLENGE • Loyalty • Ratings • Web analytics • Social listening
- 9. THE INSIGHT Ultimately we need to ‘connect the dots’ in an efficient and most importantly, meaningful way That means applying validated thinking frameworks to explain the complexity of human behaviour…Quickly
- 10. Embrace the power of innovation when it comes to data collection, but more importantly collide thinking with technology Quickly discover the story, based on an iterative and customised view of the consumer THE OUTCOME
- 13. Personalised Banner Ads Age, Gender, Location Marketing world Research world VS
- 14. TOO UNSTRUCTURED TOO CONFUSING TOO EXPENSIVE
- 16. SO WHAT DO WE DO?
- 17. Big Data
- 18. Raw Data
- 19. Crosstabs
- 20. Dashboards
- 21. Extract data from multiple sources with varying formats and structures 1
- 22. Extract data from multiple sources with varying formats and structures Transform it into a structure that can be stored and queried 1 2
- 23. Extract data from multiple sources with varying formats and structures Transform it into a structure that can be stored and queried Code and rename into sensical, useable frameworks 1 2 3
- 24. Extract data from multiple sources with varying formats and structures Transform it into a structure that can be stored and queried Code and rename into sensical, useable frameworks Load it into our data warehouses to be able to translate it into raw data 1 2 3 4
- 34. 25-29 Female Happy Built around the individual over time Profile architecture Demographics
- 35. 25-29 Female Happy 3004 At work 3000 Built around the individual over time Profile architecture Place + time = where Demographics
- 36. 25-29 Female Happy At work Work Run Drink coffee 3004 3000 Built around the individual over time Profile architecture Activities = do Place + time = where Demographics
- 37. 25-29 Female Happy At work Work Run Drink coffeeFitbit iPhone Car Groceries 3004 3000 Built around the individual over time Profile architecture Products = use + buy Activities = do Place + time = where Demographics
- 38. 25-29 Female Happy At work Work Run Drink coffee Baking Design iPhone Car Groceries 3004 3000 Fitbit Built around the individual over time Profile architecture Interests = focus Products = use + buy Activities = do Place + time = where Demographics
- 39. 25-29 Female Happy At work Work Run Drink coffee Design Internet Radio TV Online video Email Media = content, messaging Interests = focus Products = use + buy Activities = do Place + time = where Demographics iPhone Car Groceries Baking 3004 3000 Fitbit Built around the individual over time Profile architecture
- 40. Gina Lagos Director, Quantitative Research lobrien@pureprofile.com gina@galkal.com.au Luke O’Brien Commercial Director THANK YOU!
Editor's Notes
- Thanks for visiting our track Swimming lesson
- Now, this meme… ‘big hype’
- Hype cycle chart for emerging technologies Peak of inflated expectation
- Trough of disillusionment What’s worse…
- I’m really excited to not just talk about why Capture, interpret, interrogate big data to drive tangible, actionable, timely Big hype to the real deal
- Originally… Someone that is living and breathing insights every day
- How does one actually go about doing this? Why is it critical that the market research industry is involved?
- Let’s take a trip to media industry for a minute.
- Down the hall… Disconnect
- Don’t want to dwell These are all good reasons Unless you have very deep pockets investment doesn’t justify return
- Instead of pain of grappling What if we could look at the problem a different way?
- ETL
- Engineers not good at…. Not thinking of what we’re trying to achieve