Big data
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Big data
- 1. INSERT IMAGEFuture of Big Data and Analysis
- 2. 2 Data Analysis: long-lived 0 20 40 60 80 100 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 100 = Peak searches Searches for "data mining" Searches for "big data"
- 3. 3 VARIETY VELOCITY VOLUME VALUE The 3 V’s What makes BIG Data different? 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’s.
- 4. 4 The options here are nearly endless. Your geolocation through mapping services exactly what store displays you are looking at. DATAFICATION
- 5. INSERT IMAGE THE BIG DATA AFFECT ON PREVAILING SOCIO- ECONOMIC TRENDS
- 7. 7Image: Shoppertrak Data filed storesThe promise of
- 8. 8Source: Apotek responsive spacesThe promise of 8
- 9. 99 super serviceThe promise of Photo © Virgin Atlantic
- 10. 10 Retail Reloaded: the Big Data impact Retail space, messaging, service Connected devices: personal data passports and unique identifiers Info security, limits to personalised messaging
- 12. 12 EDIT1 in 6 respondents born after 1981 use personal health tracking apps Source: nVision Research, 2014 GB Photo © Future Foundation 12
- 13. 13 Society of Sobriety: the Big Data impact Datafication of the entire human experience Targeted messaging, incentives and rewards
- 15. 15 Memory Skill accumulation (language, directions) Free choice Adventure and discovery Privacy Big Data: accelerating the end of…?
- 16. 16 Big Data accelerating: Big Data impact Accuracy of Marketing A growing need for Big Data CSR Can Big Data re-create old-fashioned service?
- 17. INSERT IMAGE THE BIG DATA How can use it !
- 18. 18 Big Data: business partner of insight? “Overwhelmingly, respondents 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
- 19. INSERT IMAGE 19 In a short while from now… 19 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. Insight will re-skill itself and de-IT Big Data.
- 20. 20 1 | Big Data is a genuine revolution. 2 | Personalised marketing has its limits . 3 | Big Data cannot guarantee success for creative ideas – but can guide insight to maximised tactical advantage. Big Claims: our summary view
- 21. INSERT IMAGEThank you… Abdullah Masoud 1109073
Notes de l'éditeur
- 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 number of 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’s Variety. 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 coffee Everything 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 drive Our compatible traits for love... So mainly location when it comes to Tinder They 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.
- Corporate social responsibility
- Photo © Marc Smith 2013