Data Driven Disruption - Why Marketing and Advertising in WA lags - ADMA WA 2015 Conference
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WA is in a state of rapid transformation with the changes in Energy, Resources and support industries. At ADMA WA's 2015 annual conference, we explored why disruptive data activity in Marketing and Advertising is lagging the East Coast and Global stage
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Data Driven Disruption - Why Marketing and Advertising in WA lags - ADMA WA 2015 Conference
- 1. Data Driven Disruption. Digital Transformation What is holding WA back from rapid digital transformation? Shopping for insights with Coert du Plessis, DataAlchemists, +61406313111 @coertdup October 2015
- 2. Why is WA behind? 2) Not invented here syndrome 3) Missing crucial skills 4) Lack courageous leadership 1) Local industry mix – easy money.
- 3. Industry GM is inversely correlated to digital innovation Low Data and Digital Innovation High Data and Digital Innovation Oil/Gas (Production and Exploration) Retail (Online) Precious Metals Retail (General) Drugs (Pharmaceutical) Financial Services. (Non-bank & Insurance) Cable TV Retail (Grocery and Food) Metals & Mining Brokerage & Investment Banking
- 4. Let the data talk… Data & Source Industry # of firms EBITDA/ Sales % High Margin (From top 10) 832 33.59% Low Margin (From bottom 10) 461 5.39% Total Market 7887 15.18% Advertising/Marketing Data source: Aswath Damodaran, adamodar@stern.nyu.edu
- 5. …and marketing is the one thing it can’t stand, average Data & Source Industry # of firms EBITDA/ Sales % High Margin (From top 10) 832 33.59% Low Margin (From bottom 10) 461 5.39% Total Market 7887 15.18% Advertising/Marketing 52 15.08% Data source: Aswath Damodaran, adamodar@stern.nyu.edu
- 6. And because you data junkies want to see the detail….
- 7. 2) Not invented here syndrome
- 8. e Japanese Zen Master Nan-en’s erflowing tea-cup, you cannot add new owledge when your mind is already full.
- 9. The average Big Data talk in 30 seconds ** BONUS: Now with an effort curve! Alignment 1: Why big data talks often lack impact
- 10. Source a lot of data from different places % of Effort
- 11. Structure, clean, link and make the data smarter % of Effort
- 12. Analyse and apply an impressive algorithm % of Effort
- 13. And wozzaa – Amazing Insight! % of Effort
- 14. Interesting!
- 15. Really….
- 17. … the real effort curve % of Effort
- 18. There is a gchasm between insight and value….
- 19. What is impact? Impact means a decision is made and action is taken (or purposefully withheld)
- 20. Alignment 2: Big Data Vocabulary [ the sucking eggs section ]
- 21. 5 “V”s of Big Data Its not just the size
- 22. 5 “V”s of Big Data Its not just the size Volume
- 23. 5 “V”s of Big Data Its not just the size Volume Variety
- 24. 5 “V”s of Big Data Its not just the size Volume Variety Velocity
- 25. 5 “V”s of Big Data Its not just the size Volume Variety Velocity Veracity
- 26. 5 “V”s of Big Data Volume Variety Velocity Veracity Value Its not just the size
- 27. Only “Born Digital” companies naturally get this… Alignment 3: A Data Thinking Framework
- 29. Real Time [Stream] Real time data and insight collected from equipment and machinery sensors during Historical [Pool] Historical data and insight gained from analysing trends, patterns and opportunities for improvement learned from experience Future [Make] Future insight derived from historical analysis to improve planning, simulate and predict future outcomes Success is where all three horizons access the same structured, well- governed data to inform decisions Shared Data Decisions KnowledgePredict and Plan The three time horizons of data insight
- 30. Real Time [Stream] Real time data and insight collected from equipment and machinery sensors during Historical [Pool] Historical data and insight gained from analysing trends, patterns and opportunities for improvement learned from experience Future [Make] Future insight derived from historical analysis to improve planning, simulate and predict future outcomes Success is where all three horizons access the same structured, well- governed data to inform decisions Shared Data Decisions KnowledgePredict and Plan The three time horizons of data insight
- 31. Real Time [Stream] Real time data and insight collected from equipment and machinery sensors during Historical [Pool] Historical data and insight gained from analysing trends, patterns and opportunities for improvement learned from experience Future [Make] Future insight derived from historical analysis to improve planning, simulate and predict future outcomes Success is where all three horizons access the same structured, well- governed data to inform decisions Shared Data Decisions KnowledgePredict and Plan The three time horizons of data insight
- 32. Real Time [Stream] Real time data and insight collected from equipment and machinery sensors during Historical [Pool] Historical data and insight gained from analysing trends, patterns and opportunities for improvement learned from experience Future [Make] Future insight derived from historical analysis to improve planning, simulate and predict future outcomes Success is where all three horizons access the same structured, well- governed data to inform decisions Shared Data Decisions KnowledgePredict and Plan The three time horizons of data insight
- 33. Real Time [Stream] Real time data and insight collected from equipment and machinery sensors during Historical [Pool] Historical data and insight gained from analysing trends, patterns and opportunities for improvement learned from experience Future [Make] Future insight derived from historical analysis to improve planning, simulate and predict future outcomes Success is where all three horizons access the same structured, well- governed data to inform decisions Shared Data Decisions KnowledgePredict and Plan The three time horizons of data insight
- 34. Relationships
- 35. Real Time [Stream] Real time data and insight collected from equipment and machinery sensors during Historical [Pool] Historical data and insight gained from analysing trends, patterns and opportunities for improvement learned from experience Future [Make] Future insight derived from historical analysis to improve planning, simulate and predict future outcomes Success is where all three horizons access the same structured, well- governed data to inform decisions Shared Data Decisions KnowledgePredict and Plan The three time horizons of data insight
- 40. 3) Missing crucial skills
- 41. You need Purple People Technical Genius Commercially Astute
- 42. Four classes of Data Scientist • The Scientist • The Creative • The Business Leader • The Systems Builder
- 43. 4) Lack courageous leadership
- 44. Leadership
- 46. What should you do? (copied from my speaking notes) • Build out your own eco-system of knowledge in Data Science - Beware the Nigerian 419 styled “Big Data” Specialists - Go beyond your existing comfortable eco-system. Find purple people. - Find the leaders that think differently…. The ones that take personal risk. Work with them (some of these leaders may be junior to your level) - Research the disruption in your industry – it is happening everywhere now – or change your industry - Read… start with an empty cup and keep reading – Think and reflect. Your skills in visualisation, design thinking, Story Telling, customer experience are crucial to enable data science to succeed. You are part of the eco system… the symbiosis here is strong… start with an empty cup and keep reading. The rate of learning is ridiculous. - Try things. Change things. Little experiments - Ask Why (The average young child asks 300 questions a day) -- by middle school we have squashed that defect very well – time to unleash your curiosity.
- 47. Last words [Moneyball – Adapt or Die video]
- 48. DataAclhemists Shopping for insights with Coert du Plessis, © DataAlchemists, +61406313111 @coertdup October 2015. Curious by nature
Editor's Notes
- A topic that is crucial to the future and competitive success of our beautiful state….
- I don’t have great news. My message today is hopefully a wakeup call – You KNOW what I am talking about. And if you don’t, then it is likely that you are part of the problem. The reason for this situation is that during the boom of Data Science and Digital, our WA industry mix did not create the urgency around “real innovation” We have “linear thinking” industries; the reality is the disruption industries are the “exponential thinking” industries. 2) Not invented here – you don’t know what you don’t know. 3) Missing skills – never came here – we were not developing them, and right now the East Coast pull is very strong, and very hard to compete internationally. 4) Leaders need to back change. A period of learning. If you don’t know, you can’t lead. If the Data Science skills is an acute shortage, then the leadership in data science is like finding the goldilocks planet I am going to spend some time on 1 & 2 today, and give you some tools and frameworks that will hopefully provide you with a foundation to make this exponentially growing space of data and digital less confusing, and provide a foundation to build your knowledge and capabilities.
- Here are the full graphs
- Don’t know what you don’t know The absolute “feeding frenzy” of big data players and pretenders makes any shopper runaway and cry. It is incestuous; the pretend players are so plentiful, a Nigerian 419 scammer would not feel out of place, with a gullible audience that totally wants to “no change”
- STORY: The Japanese Zen Master “Nan-en”, keeps pouring tea into the cup. Meiji Era (late 19th century) College professor comes to visit to learn Zen.
- Source a lot of different Data - Big Data, Small Data, Thin Data, Thick Data…
- Make the data talk to each other / smarter
- Apply an amazing algorithm / analysis technique
- Be blown away by the unexpected deep results
- Interesting is what you call a painting by an acclaimed artist…. The South African response is “the frame is interesting” no to be too impolite. Interesting achieves little sustained change…. It may change the conversations, but it doesn’t sustainably change behaviours and decisions and actions.
- You see we stop at insight… which is interesting. But when we stop at Impact, now that is valuable. And it is a simple mind frame on how we setup these leaps of faith when we start on these journeys to insight… that we often don’t know the road to impact…. But even so, it is irresponsible to ignore the effort curve, and the “true” balance of effort to create impact. Where we stop is "interesting" When we do something, it is "valueable" But we often don't get past interesting, as that is what we set ourselves up for… Interesting! There is no time/budget/resources left for impact and value.
- This is what I call the chasm of insight to value…. Even in our advanced brains trust, we underestimated how hard it is to turn insight into value and transformational action.
- Avoid the religious debate Not overly into market definitions – prefer design principles…
- Mine’s bigger than yours…. Really? I am watching my 6 year old boy argue.! It is all 5!
- Mine’s bigger than yours…. Really? I am watching my 6 year old boy argue.! It is all 5!
- Thick data, thin data, fat data, big data, master, transaction, IT / OT…..
- Speed data is being created
- That it is right in context… i.e. 100%
- Value – there must be value at the end of the tunnel… a decision is made or an action is taken that is valuable.
- A capability that is collaboration (not just cooporation) and knowledge sharing. Atlassian – embedded.
- Think about it like the back to the future clock
- Inventory Strategy - from TV – sell bulk Audience Strategy from RTB and now programmatic
- Eye blink is 300 ms. You can do 3 of these in that time.
- Eco System Partners Who do you know? DSP – space / customer segmentation / history SSP – adds, predefined rules and value RTB – Market – like the ASX Measure success of campaigns.
- The second big reason WA was falling behind was an “extremely acute” lack of a digital skills base. There was no local talent pool to draw from. That also made it hard to attract and retain talented staff who could transform a business’ marketing. “So we are often asking people that have traditionally done another job — customer relationships management, campaign management, communications — to transform themselves into digital and analytics experts,” “We’re essentially asking a person with a marketing or communications degree to compete on a global scale with someone with a PhD in physics or an engineer.”
- These are the skill you need Not often in one person What is your eco-system look like? – Did you see the symbiotic relationship in the programatic world? That is the essence of digital disruption and design thinking…. It needs an eco system of skills and relationships to work… My tip to you is to read, and build your eco system…. Not your usual buddies.
- Mr du Plessis said where companies hired young people with digital capabilities they often still failed because they were led by managers who did not understand digital skills or language.
- 1980s experiment Social dynamics – status quo Ladder, banana - 1 up – stream of cold water. Little splash in other monkeys -2nd, the same - By the 4th monkey, the others were beating the monkey - They change out a monkey, new monkey gets beaten - Scary thing – eventually they change out all the monkeys, and the new monkeys, that have never had water on their faces, beat the new monkey trying to climb up!!!