2014 10 22 broadband world forum mike sherman
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My take on the issues facing big data and two solutions to addressing these issues: one required and the other disruptive
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2014 10 22 broadband world forum mike sherman
- 1. DataMediary (Consumer Data Mall) – A Consumer Driven Big Data Concept Mike Sherman Amsterdam, October 22, 2014 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 2. KISS always applies 1 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 3. 2 Big Data Defined Big data is like High School 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 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 4. More Variety 5 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 5. More Volume Billing Data (old) CDR (call data records: old but can store more now App usage (if on network, new) Location (new) Sensors, e.g. accelerometer, gyroscope (new) 6 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 6. IT is helping 7 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 7. Big Data: Overhyped! 10 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 8. Source: DMA, Big Data and one to one marketing, 12 myths busted, August, 2014 11 Myth: Big Data is a Strategy Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 9. Source: DMA, Big Data and one to one marketing, 12 myths busted, August, 2014 12 Myth: Big Data Provides A Competitive Advantage Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 10. Source: DMA, Big Data and one to one marketing, 12 myths busted, August, 2014 13 Myth: More Data = More Insight Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 11. 14 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 12. Sometimes Less Data Is Better 15 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 13. Big Data isn’t Pacman, you can’t gobble data and spit out the answer 16 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 14. Lots of Overpromise 17 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 15. It made for great “the future of business” fodder. A technology company crowdsources its R&D by ponying up $1,000,000 to anyone who can improve their recommendation engine. Netflix, stickler for user-experience as it is, was willing to pay all this money even if it meant making their algorithm just 10% better. Amazing! If you were looking for an anecdote to cheerlead Web 2.0 to, this was almost too good to be true. And in fact, it was. Despite all the plaudits and case studies, Netflix announced this week that despite paying $1 million dollars to a winning team of multinational researchers in 2009, they never bothered to implement their solution. Why? Because, according to Netflix the “additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.” In other words, all the experts telling businesses to follow in Netflix’s crowdsourcing footsteps were shouting bogus advice all over the internet. Source: What the Failed $1M Netflix Prize Says About Business Advice – Forbes April 16, 2012 18 More failures than successes Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 16. Failed Personalization Selling gas to a bus rider? Copyright 2014 Mike Sherman, no use allowed without explicit 19 written permission
- 17. Failed Personalization: Selling flights 6 months too late? 20 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 18. Issue: Communication Gap End Users / Senior Management 정보 요구 정보 가 필요합니다 αιτήματος Ανάλυση Έξοδος Ανάλυση Data, IT, Data Scientists 22 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 19. Translator/Integrator Role Required End Users / Senior Management 정보 요구 정보 가 필요합니다 (Information needs) (Information required) αιτήματος Ανάλυση Έξοδος Ανάλυση (Analysis Request) (Analysis Output) 23 Data, IT, Data Scientists Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 20. The Consumer Data Mall 24 Data Mall Data Scores Relevant Offers Feedback Dialogue, not Monologue Permission based, not passive Win-Win Marketers Efficiency and Effective-ness) Consumers Service, not sales Engagement, not annoyance Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 21. Consumer Data Mall: Anchor Tenants Retailer(s) Telco 25 Transaction processor Category exclusivity Bring extensive data and customers Benefit by expanding their share of market Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 22. Consumer Data Mall: Other Tenants 26 Non-exclusive Bring more data Use data output (marketing scores) Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 23. Consumer Data Mall: Kiosks Kiosks 27 Non-exclusive Mainly local companies Use data, contribute little Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 24. Consumer Data Mall: Consumers 28 Opt-in Share data Receive PERSONALIZED messages and offers Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 25. Key Points Yes, Big Data exists: there is more data, more types of data, faster access to data and perhaps, more accurate data (the 3-4 V’s) Yes, there are new hardware and software solutions that allow us to collect, access, manipulate and analyze that data No, that alone won’t solve our problems What else do we need? Translators/Integrators? Consumer Data Mall? 29 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 26. Thank You! Questions? www.MikeSherman.net Mike@MikeSherman.net 30 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
- 27. Consumer Data Mall Kiosk 31 Copyright 2014 Mike Sherman, no use allowed without explicit written permission
Editor's Notes
- Kiss: keep it simple, stupid! Let me see if I understand Big Data enough to explain it simply
- The suite of IT products that enable Big Data is extensive and growing
- Big Data is likely very overhyped, as reported by Garnter
- As the DMA reminds us, Big Data is NOT a strategy
- Nor does it provide competitive advantage. Big data is a tool, nothing more, nothing less
- Again from the DMA, more data doesn’t necessarily mean more insight, sometimes more is less
- And the old rules still apply: such as garbage in, garbage out, e.g. just having more data doesn’t help unless it is the right data
- As we learn from Harvard Business Review where they talk to the focusing on getting the right data, not lots of data
- And then there is the pacman myth, where people believe that all you need is “pacman IT’ to gobble up data and spit out the answers. It just doesn’t work that way.
- There is a lot of overpromise – most companies would report mainly investments to date, little if any profit improvement and certainly not 10%
- And when big data is employed, it doesn’t necessarily make a difference. In this case, despite winning a $1 million prize, the big data solution was never implemented.
- The promise of big consumer data, personalized marketing, is still largely more a promise than a reality. As seen here, four square thinks that bus riders need to buy gas
- And Smarter Travel is still trying to sell me a ticket to Barcelona, 6 months after they know I went there.
- Solution One: we need to close the gap between end users and senior management, who have business problems where big data could help and the data scientists, who with their tools, could help. But today, the don’t communicate because they speak different languages
- What I believe is needed is a new role, the Big Data Translator or Integrator, who understands both languages and can insure that information needs are translated into appropriate analysis and the analysis output is retranslated into information that the end users and senior managers can use
- Marketers want both efficiency (reach more of the right people) and effectiveness (more responses from those they do reach) Consumers respond when messages are perceived as service (not sales) and when they are engaged (not annoyed) The consumer data mall seeks to deliver both via Dialogue (versus monologue, with permission, not passivity, seeking a win win value exchange How does this work Marketers send data to the “mall”, which combines data from various sources. They also send messages and offers they would like to use The Mall sends back a “score” that predicts how likely a person is to respond to an offer The scores are used to decide who gets an offer. Importantly, marketers agree NOT to send to those with low scores, as this is unlikely to lead to a response and it annoys those customers (or leads them to ignore messages in general) Consumers feedback both via their responses and by providing responses to information requests
- Up to 4 Anchor tenants who bring a strong core of customers and customer data: Telco, Transaction processor and a food and hard goods retailers Category exclusivity, so they can benefit by building share
- Additional tenants who bring more data, e.g. airlines, hotels, restaurants, insurance, automotive, medical Non-exclusive, they benefit by using data (via marketing scores)
- Kiosks, e.g. local merchants, dry cleaner, restaurants, they mostly use data, but contribute little additional data Non-exclusive
- Consumers, they opt in to participating and share additional data In return they get relevant messages. So engagement increases by a step function, as they welcome, rather than ignore messages and offers. The challenge for marketers is to increase the relevance of the messaging – sending the same “mass market message” to either everyone or selected segments ignores the need to customize the message, not just the target. Initially, they may get point, miles, etc as an incentive to join, especially if one of the anchors has an existing program or an airline joins (in which case they might get category exclusivity). But the longer term value proposition is “less spam, more communications you like”. A key tenet is that the reinforcement between sharing data and getting something relevant back should be very quick – the mall should seek to expliciity reinforce the value of sharing data.