Your SlideShare is downloading. ×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Big Data and The Future of Insight - Future Foundation

9,257
views

Published on

As Big Data sweeps through consumer-facing businesses, we ask: …

As Big Data sweeps through consumer-facing businesses, we ask:

- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?

For more information please contact info@futurefoundation.net or visit www.futurefoundation.net


1 Comment
45 Likes
Statistics
Notes
No Downloads
Views
Total Views
9,257
On Slideshare
0
From Embeds
0
Number of Embeds
33
Actions
Shares
0
Downloads
274
Comments
1
Likes
45
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • When did you first hear the term ‘Big Data’?Do you remember what your gut told you about it. Excitement? Hype? Or just another attention seeking tweet?
  • And searching for data mining in Google long before big data joined the party. So what has happened here?Essentially, big data has become the lay man's umbrella term for real world data analysis. We’ve glorified data and made it ‘sexy’. The world of data science had been branded.About Google Trends (http://www.google.com/trends) Google Trends analyzes a percentage of Google web searches to determine how many searches have been done for the terms you've entered compared to the total numberof Google searches done during that time.The numbers on the graph reflect how many searches have been done for a particular term, relative to the total number of searches done on Google over time. They don't represent absolute search volume numbers, because the data is normalized and presented on a scale from 0-100. Each point on the graph is divided by the highest point, or 100. When we don't have enough data, 0 is shown. A downward trending line means that a search term's popularity is decreasing. It doesn't mean that the absolute, or total, number of searches for that term is decreasing. Trends data is relative, not absolute.Just because two regions show the same number for a particular search term doesn't mean that their absolute, or total, search volumes are the same. Data from two regions with significant differences in search volumes can be compared equally because the data has been normalized by the total searches from each region.Source : About Google Trends, 2014.
  • There are some fundamental differences in the data we’re able to collect now and how it can be used. There is real BIG data out there and it typically consists of the three V’sVariety.Volume.Velocity.And the allure of big data is that with all this new information we have a opportunity to draw Value.Let’s start with velocity:
  • The options here are nearly endless. Your geolocation through mapping services, or even walking past wifi connections. With imminent iBeacon technology your location can be tracked within feet for use in stores. Companies like Euclid are already envisioning a world where we can tell exactly what store displays you are looking at.Everything you search for online.Your credit card – what you’ve been buying and when. Your cinnamon latte morning coffeeEverything you say on Social networks... Every retweet, like and comment.Your medical records, prescriptions, aliments, genome.Everything you buy with a store card. Everything you buy online. Everything you almost buy online then decide not to at the last minute.The prices of flights, the location of flights.Every email we send and receive Where we drive, and even how we driveOur compatible traits for love... So mainly location when it comes to TinderThey way we move, the way we sit.Literature can be datafied.How much energy we use, and when. And even the things we own in our homes. The internet of things is expected to revolutionise our ability to collect personal data, allowing machines to interact without human intervention. Google’s acquisition of Nest is an obvious indicator of where the top dogs think this is heading.But... (and there’s always a but) with Big data we have messy data. It’s approximated that at least 80% of the data we have produced is unstructured. It doesn’t always fit neatly into rows and columns which doesn’t make things easy. But we want to be able to look at the unstructured data out there. This is where great opportunities for consumer insight are held. This is where REAL is. Luckily, we can profile and normalise this data and best thing about this is that our ability to normalise with machine learning algorithms is improving as we collect even more data.
  • One area however that does benefit from increased variety of variables is our predictive power with big data. The company I previously mentioned, EDITD, is able to make 6-8 week forecasts for popularity of different fashion trends. It’s analysis like this that is able to help Asos push up sales 37% in the last quarter of 2013. We’re also able to predict crime hot spots based on variables like past arrest patterns, paydays, sporting events, rainfall and holidays. From this stations can deploy officers there in advance.http://www.sciencedaily.com/releases/2014/01/140110142212.htmFarsite Forecast were even able to predict the Oscar winners for 2014, based on variables like nominees previous winning performances, nominations, results from other awards, as well as industry gossip and, too lesser extent for poor Leonardo DiCaprio, social media buzz.In all these examples you’ll notice that the variable inputs are things being measured today that have a real and expected effect on the future. And generally, as long as some of tomorrow is contained in a measured part of today we can make predictions. This is amazing stuff but, as with all forecasts and predictive models it's never 100%. 5 of 6 Oscars were predicted correctly in 2013. And Amazon, well they keep predicting that I would like to purchase items I already own, but just didn't buy from them. Based on the data they have, they’re doing a perfect job here.
  • Photo © Marc Smith 2013
  • Transcript

    • 1. INSERT IMAGEBig Data and The Future of Insight Summary slides
    • 2. INSERT IMAGE PETABYTES FOR ALL: THE BIG DATA BACK-STORY Georgina Sapsted, Leader of Quantitative Analysis
    • 3. 3Source: Google Trends (www.google.com/trends)/nVision | Base: UK (English speakers), March 2014 Data Analysis: long-lived 0 20 40 60 80 100 2004 100 = Peak searches Searches for "data mining" Searches for "big data" B I G D A T A A N A L Y S I S
    • 4. 4 VARIETY VELOCITY VOLUME VALUE The 3 V’s What makes BIG Data different?
    • 5. 5 80% of big data is raw and unstructured DATAFICATION
    • 6. 6 Predictive power As long as some of tomorrow is contained in a measured part of today we can make predictions.
    • 7. INSERT IMAGE THE BIG DATA EFFECT ON PREVAILING SOCIO- ECONOMIC TRENDS Dom Harrison, Director, Global Trends
    • 8. 8 Retail Reloaded1
    • 9. 9Image: Shoppertrak datafied storesThe promise of
    • 10. 10Source: Apotek responsive spacesThe promise of 10
    • 11. 1111 super serviceThe promise of Photo © Virgin Atlantic
    • 12. 12Image: Advanced Institute of Industrial Technology in Tokyo After mobile: New means of identification
    • 13. 13 Retail Reloaded: the Big Data impact Retail space, messaging, service: all substantially impacted Connected devices: personal data passports and unique identifiers Info security, limits to personalised messaging, service etiquette: barriers
    • 14. 14 Society of Sobriety2
    • 15. 15 Favouring restraint, celebrating control Source: nVision Research, 2014 US “I have consciously made an effort to reduce the amount of alcohol I drink” each week” 51% of US 21-24 year olds agree
    • 16. 16 1 in 6 GB respondents born after 1981 use personal health tracking apps Source: nVision Research, 2014 GB Photo © Future Foundation 16
    • 17. 17 Society of Sobriety: the Big Data impact Datafication of the entire human experience Targeted messaging, incentives and rewards Intensifying pressure on categories with an indulgence component
    • 18. 18 Murdered by Modernity3
    • 19. 19 Memory Skill accumulation (language, directions) Free choice Adventure and discovery Privacy “Ignorance is bliss” The presumption of innocence The old-school football manager Big Data: accelerating the end of…?
    • 20. 20Source: Google Trends (www.google.com/trends)/nVision | Base: Worldwide (English speakers), 2014 The nascent response: Searching for the deep web B I G D A T A A N A L Y S I S 0 10 20 30 40 50 60 70 80 90 100 2008 Searches for "Tor browser" (100 = peak searches) Trendline
    • 21. 21 Murdered by Modernity: Big Data impact Wholesale rejection of precision marketing? A growing need for Big Data CSR Can Big Data re-create old-fashioned service?
    • 22. INSERT IMAGE THE BIG DATA BIOPSY James Murphy, Editorial Director
    • 23. 23 Does the regulator pack any punch at all? “We are undergoing a revolution... The immense volume, diversity and potential value of data... The working group will consider all these issues...” John Podesta, White House Big Data Review Leader Jan 2014
    • 24. 24 Big Data: business partner of insight? “Overwhelmingly, resp ondents are positive about the need to travel for business. Over half (55%) find business trips interesting, 36% find them enjoyable and 17% say business travel is motivating.” Amadeus Business Travel Survey 2014 Sample size: 411 Regular Business Travellers in the UK and Ireland
    • 25. INSERT IMAGE 25 In a short while from now… 25 In our jobs, we will all be using Big Data apps in the way we all use Excel today. Marketing will work in real- time just as in-store sales and inventory management have always done. Data Science guzzles the budgets and pressures conventional techniques to justify themselves. We see: The rise of the Soft Woo + Anticipatory Service Propositions Insight will re-skill itself and de-IT Big Data. Big Data will amplify the claim that it can reduce headcount in every sector.
    • 26. 26 1 | In our Big Data future, the marketing and insight community can – without loss – pay much less attention to consumer motive. Research budgets can be shrunk accordingly. 2 | Mass marketing is progressively replaced by commercial messages which are unique, focused, personal. 3 | Big Data will stimulate ever more consumerist agitation to the point of legitimising/accelerating legal restraints on practitioners. 4 | Big Data will perfect new product development and minimise R&D losses. Big Data: the Big Four Claims
    • 27. 27 1 | Big Data is a genuine revolution. But even as its efficiencies universalise so brands have to find new creativities in order to compete. Therefore insight redux! 2 | Personalised marketing has its limits, its costs. The customer may revolt against intrusiveness. But, more worryingly, she simply may not respond to precision- bombing, however sophisticated. Something extra required. 3 | Neither political regulation nor populist agitation can suppress Big Data or its commercial applications. 4 | Big Data cannot guarantee success for creative ideas – but can guide insight to maximised tactical advantage. Big Four Claims: our summary view
    • 28. INSERT IMAGEWant more? info@futurefoundation.net +44 (0) 203 008 4889 London | New York | Stockholm