Data Storage - Should We Use It?

Transcription

1 BIG DATA MASTERCLASS DUBAI 25 FEBRUARY 2013 DR BRENDAN O DONOVAN HEAD OF STRATEGY (EMEA)

2 BIG DATA SEEMS TO BE EVERYWHERE

3 HOW TO TELL HYPE FROM REALITY?

4 UN GLOBAL PULSE BIG DATA FOR DEVELOPMENT

5 CANCER RESEARCH

6 BUT IS IT USEFUL TO BUSINESS?

7 BIG DATA MYTH NUMBER 1: GETTING SMART WITH DATA GUARANTEES YOU LL WIN 7

8 SEASON WIN% FOR OAKLAND A S 70% Moneyball Season 60% 50% 40% 30% 20% 10% 0%

9 LESSONS WE SHOULD LEARN FROM MONEYBALL Data improves performance, but other factors remain important You need to continuously innovate to sustain advantage Finding and retaining analytical talent is critical Without proprietary data, others will copy

10 ZETABYTES ZETABYTES BIG DATA MYTH NUMBER 2: SIZE MATTERS

11 MODERN DATASETS VARY WIDELY IN SIZE 10TB 5TB 5TB 0TB 5MB Complete works of Shakespeare ~0.5GB addresses for 15m customers Aimia's shopper transactional data (280m customers, 68bn items) Volume of data captured by Facebook in 1 day

12 REALLY WIDELY, BUT VALUE DENSITY FALLS 600TB All human life is here 500TB 300TB Foundation for responsive DM Detailed segmentation and targeting New insights, but costly? 0TB 5MB Complete works of Shakespeare ~0.5GB addresses for 15m customers 5TB Aimia's shopper transactional data (280m customers, 68bn items) Volume of data captured by Facebook in 1 day

13 RETURN ON DATA Better Decisions New Ideas Marketing ROI ROD = Incremental Business Value True Cost of Using the Data Storage Processing Analytical Resource

14 FRONTIERS IN DATA PROCESSING: TEXT MINING Flying on Delta has never been this good Flying on Delta has never been that good Delta Passenger (getting off a flight on a new aircraft) Flying on Delta has never been this good Delta Passenger (on a social media site) Delta Passenger (getting off an Emirates flight)

15 FRONTIERS IN DATA PROCESSING: VIDEO Facial Recognition Speech Recognition & Attribution Brand Interactions Social Interactions Embedded or inferred location

16 BIG DATA MYTH NUMBER 3: AUTOMATED ANALYSIS WILL REPLACE HUMAN INPUT

17 THERE ARE SOME GOOD EXAMPLES OF LARGE SCALE AUTOMATED PATTERN FINDING

18 BUT EVEN THESE FALL OVER SOMETIMES

19 THE REALITY IS THAT HUMAN AND COMPUTER INTELLIGENCE COMPLEMENT EACH OTHER HUMAN Acting on the Insight AUTOMATED HUMAN/ BESPOKE Creating the Insight (or Model) AUTOMATED/ OFF THE SHELF

20 DEEP ANALYTICAL TALENT IS LIKELY TO BE IN INCREASINGLY SHORT SUPPLY

21 FROM MYTH TO REALITY Data is the secret to success Data is another competitive edge Size matters Value of data beats volume Computers replace analysts Analytical resource becomes more critical

22 AVOIDING THE BIG DATA PITFALLS 1. Focus on the Application 2. Choose Data appropriately and sustainably 3. Get the right People 4. Worry about tools last

23 1. FOCUS ON THE APPLICATION BIG OR SMALL DATA INTENDED USE? Actions / Decisions which affect groups of customers the same Actions or decisions which need to be customised to each individual Developing segments Profiling segments New product development Store service performance Public pricing ATL media Assignment to a segment Credit risk assessment Targeted marketing Web personalisation Private pricing Targeted offers / discounts Small Data Big Data

24 2. CHOOSE DATA APPROPRIATELY KEY QUESTIONS TO ASK OF A NEW DATA SOURCE Volume Coverage Degree of Structure Ownership Excludability

25 MIX BROAD DATA AND DEEP DATA SELECTIVELY DEPTH As valuable, reusable data elements are discovered through exploring the deep data set, they can be added to our broad data Deep Data Coverage of c.50,000 individuals per country across a range of data types social media, full spending patterns, demographics, attitudes, property, etc. Drives strategic consulting and exploration / R&D Broad Data Coverage of majority of population across a narrower set of critical / readily available data types. Drives marketing campaigns and other direct applications of data BREADTH 25

26 BIG OR SMALL DATA? DEPENDS ON HOW YOU GET IT Data Type Deep Data Sourcing (Active consumer opt-ins) Broad Data Sourcing (Use of existing data sets / passive permissions) Social Media Profiles & Connections Incentivise a limited group of customers to open their Facebook profiles to you Licence direct feeds of public tweets from Twitter Locational Data Attitudes & Preferences, Property Owned Incentivise group of customers to share foursquare, or install GPS tracking app on phone Market research panelists responding to profiling and omnibus surveys over time Licence cell-tower based positioning data from a mobile phone operator Infer consumer preferences from spending patterns held by banks

27 MAKE YOUR DATA SUSTAINABLE We provide our customers with information TRANSPARENCY ADDED VALUE Our customers are aware CUSTOMERS CONTROL TRUST Our customers have control Our customers have confidence 27

28 3. GET THE RIGHT PEOPLE Gary Loveman CEO, Caesars Entertainment Corp Ex-HBS professor

29 3. GET THE RIGHT PEOPLE Choosing advisors in the world of Big Data: Use big data directly in their own business Ability to enhance your data by connecting the dots Strong position on obtaining and sustaining access to consumer data Experience in insight and live usage of data ( , web, etc) comes right at the top of an alphabetical list of suppliers

30 4. WORRY ABOUT THE TOOLS LAST If you can imagine or describe something you want to do with data, the tool probably already exists The Real Problem: Not every smart, analytical idea will work in practice

31 EVEN IF DATA ISN T A SILVER BULLET, THE PRIZE FOR GETTING IT RIGHT CAN BE SUBSTANTIAL

32 A FINAL THOUGHT

33 THANK YOU Evert de Boer Visit aimia.com/airlineloyalty for more information