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Big Data - Taming the Data Monster


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We are seeing an exponential growth in the scale, scope and immediacy of data, but how can companies commercialise this data? In this presentation we'll look at four ways companies can make the most of the data they have.
1. Turn data 'exhaust' into new revenues
2. Diversify your data to unlock value
3. Join the intention economy
4. Be an artist and a scientist.

Published in: Business, Technology

Big Data - Taming the Data Monster

  1. 1. Big Data | Taming the Data Monster Insight Briefing Market Gravity
  2. 2. BigData31 We are seeing exponential growth in the scale, scope and immediacy of data What is ‘Big Data’? As intended, the term „big data‟ refers to datasets which are so large and complex that they overwhelm standard software and hardware, requiring new technologies and approaches. Regardless of the tools that are used to manipulate data, three driving forces are bringing major change and opportunity. More data The rise of mobile and increase in internet usage globally is driving huge growth in data volumes generated From a wider range of sources Smart devices and the popularity of social media is creating new (often unstructured) information sources Available faster than ever before Advances in technology and processing power and enabling data-driven insights and decision making, in real-time Global Data Volumes Generated 2005: 130 Exabytes 2010: 1,227 Exabytes 2015 Forecast: 7,910 Exabytes Source: IDC, EMC
  3. 3. Implications31 So, for those of us in the growth business, is there more to big data than hype? 4 things have captured our imagination The Data Explosion Everything to win, everything to lose Drowned by legacy?A shift in power
  4. 4. Quantified self is exploding the supply- side of available data Life-logging has become a mainstream activity, integrating multiple devices and channels • Tracking location, activity, behaviours • Capturing data, metadata & audio/video footage Demand-side shift to data-driven digital marketing Digital channels will continue to grow and seamless cross-channel experiences will become the norm • Increase in sophistication of digital marketing, e.g. measurability of social media campaigns • Data spend focused on driving relevance – in time, location and personalisation New players and channels Companies are providing „free‟ services in order to capture data they can monetise elsewhere. • “If you‟re not paying for it, then you‟re the product” Implications32 New technologies are enabling new data-driven competition for purchases Insight 1: The Perfect Data Storm
  5. 5. Big companies with the most to gain from data are also often the least trusted Consumer trust is at an all-time low Consumer backlash across multiple industries has impacted many major reputations and brands • Banking: financial crisis / product mis-selling • Energy: price rises / high profits • Telco: excessive charges Many brands don’t have license to innovate Brands may not stretch to new services, particularly in businesses that are perceived as „utilities‟ • Consumers more interested in „fixing the basics‟ • Hard to divorce new services from frustrations with existing New players have an advantage Disruptive start-ups are more nimble, able to take more risks and act on data insights quickly • Less at stake – financially and operationally • Less long-term brand equity Implication 2: Questionable rights to play Implications32
  6. 6. Increasing awareness of the value in data Consumers recognise what their personal data is worth and expert value in return for providing it • 2/5 of consumers see their personal data as an asset, tradable for better prices and offers1 New tools that empower consumers New „personal data store‟ services are emerging that allow consumers to manage and sell their data • UK market projected to be worth £1BN b 20162 More protective regulation Data protection regulation continues to evolve, placing further obligations on businesses • Updated EU regulation expected 2014-16, providing better coverage of social networks and increasing corporate fines for breaches Implications32 Consumers are empowered to have high expectations of how their data is used Implication 3: Need to show real consumer value Source: 1) Direct Marketing Foundation / Future Foundation, 2012; 2) Ctrl-Shift / Information Age, 2012
  7. 7. There‟s a mounting tidal wave of data – it‟s hard enough just to stay afloat Winners will derive actionable data insights Technological capability and analytical skills alone aren‟t enough to use data for commercial gain • Organisational structures and processes need to enable rapid, data-driven decision making But talent shortages add to the challenge… The need for specialist skills will increase, with high competition for the best people • By 2018, US will be short 190k people with the required analytical skills and 1.5m managers and analysts to analyse big data and make decisions …and current IT investment levels aren’t enough to stand still Despite continued growth in corporate IT spend, data volume growth will likely outstrip capacity • 40% projected growth in global data generated Implication 4: Potential for analysis paralysis Implications32
  8. 8. Why should we care? Because effective use of data is now a critical competitive advantage The challenge Implications32
  9. 9. So, how should the big companies respond?
  10. 10. InitialInsights5 Based on our work with clients across multiple industries, we believe there are four possible responses to the challenge Turn data „exhaust‟ into new revenues New insight services derived from existing data • Hidden value in data produced as a by- product of the core business • Moving beyond existing customers • Added value through insight and advice Be an artist - and a scientist Complimentary skills to realise data potential • Statistical analysis needs to be robust… • …but only a „human‟ perspective can understand the real drivers for conclusions • Creative communication wins over audiences Diversify your data to unlock value Combining datasets with new partners • New connections and patterns • Deeper relationships with existing customers • Increased relevance to new customers 10 Thoughts on the future: 4 ways to respond to the challenge Join the intention economy Embrace the tools of the empowered customer • Customers express intentions directly to sellers • Shift from CRM to Vendor RM • Focus on small data, not big data InitialInsights33
  11. 11. Turn data exhaust into new revenues “Google decided to keep all the information from users' spelling mistakes in their search engine, data that most people would throw away. They looked at what people typed, even if they spelt Ferrari with eight 'r's. Now you can make typos in Google and still get to where you want to go. They've effectively developed the world's most powerful spellchecker.” Mark Whitehorn, Chair of Analytics, University of Dundee Be careful what you throw away There may be hidden value in data produced as a by-product of your daily core business activities Moving beyond your existing customers You may not yet have close relationships with those willing to pay for your data Added value through insight and advice Raw data alone will rarely carry much value – but the combination of data with experience and insight will SellDataExhaust3
  12. 12. Turn data exhaust into new revenues Case Study: Smart Steps from Telefonica & GFK | Deriving consumer insights from mobile phone location data SellDataExhaust3 Smart Steps uses mobile phone location data to offer retailers, councils and public safety bodies information about foot-fall in different locations. Socio-demographic insights Segment footfall in specific locations using socio-demographic criteria, measuring actual behaviour rather than perceived or claimed behaviour Existing customer base insights Information on when your customers are there and how often. Tells you if the crowd stayed at home, or went elsewhere. Also tells you if your competitors‟ areas experienced the same thing. Campaign management Enables retailers to measure marketing campaign success against naturally growing or declining footfall around their stores
  13. 13. Turn data exhaust into new revenues How can big companies harness the opportunity? 1. Co-create with new customers Work with a range of potential customers when exploring the opportunity to help uncover unmet needs and new ideas for harnessing data exhaust to gain insight and solve problems 2. Involve technical teams early Don‟t be afraid of involving the realists early on - bringing technology and analytical experts into the ideation process will not only result in more feasible ideas, but also bring new perspectives 3. Consider a range of options to deliver required capabilities Offering client value from new services will usually require a combination of data and consultative service to deliver tailored insights and recommendations – consider buy, build and partner routes to deliver this capability SellDataExhaust3
  14. 14. Diversify your data to unlock value “Making official data public could spur lots of innovation. In Europe the information held by governments could be used to generate an estimated €140 billion ($180 billion) a year.” The Economist, 2013 New connections and patterns Gain a broader view of trends and drivers by combining your internal datasets with those from outside – businesses, governments and consumers Deeper relationships with existing customers Move from a silo view of what your customers buy and how they behave within your business to a fuller picture of their lives and needs Increased relevance to new customers Combine data with those of partners to create insight services for new customers that need to understand relationships between divergent industries, customers and environmental factors DiversifyData3
  15. 15. Diversify your data to unlock value Case Study: Zest Finance | Big Data Underwriting ZestFinance uses machine learning techniques and large-scale data analysis across a range of sources to make more accurate credit decisions. Vast amount of data sources ZestFinance uses thousands of indicators to assess customer credit scores, not like most American banks relying on FICO credit scores, thought to be based on 15-20 variables. Zest uses unorthodox measures, e.g. the amount of time spent on the website before applying for a loan Machine learning techniques Its decisioning infrastructure runs dozens of individual underwriting models in parallel and delivers a 40% improvement over the current, best-in-class industry score Unlocking a new market Through improved credit risk assessment, ZestFinance is able to offer loans to people that are not being served by the rest of the industry DiversifyData3
  16. 16. Diversify your data to unlock value How can big companies harness the opportunity? 1. Be open to sharing Unless already out in the open, most organisations with valuable data will expect something in return 2. Ask questions, then look for answers To avoid being overwhelmed by the sheer volume and complexity of data, generate early hypotheses to validate before entering into analysis 3. Confirm value potential before negotiating If possible, exchange low-risk samples of data to be combined to explore the potential for identifying new insights and generating value – before entering into a potentially unnecessary contractual discussion DiversifyData3
  17. 17. Join the Intention Economy “I see it as the economy that arises when customers can express their intentions directly to sellers. And it begins to have a real effect not only on prices and offerings, but on the entire economy at large.” Doc Searls, author, The Intention Economy Customers express intentions directly to sellers The effect of the internet in moving us from a mass- market economy to a personalised one is still to reach its fullest impact Shift from Customer Relationship Management to Vendor Relationship Management When engaging with consumers, businesses will agree to the consumer‟s terms and conditions, rather than the other way round Focus on ‘small data’, not big data New tools will allow consumers to have full control over their personal data and how it is used – managing all their interactions in one place IntentionEconomy3
  18. 18. Join the Intention Economy Case Study: Songkick | Empowering music fans to influence where their favourite acts perform, more effectively connecting supply and demand IntentionEconomy3 Songkick, one of the original high tech start-ups in London's Silicon Roundabout area, provides personalised news about live music events. Their „Detour‟ functionality lets users collectively persuade performers to come to their town. Users pledge to buy tickets People interested in a certain performer coming to town can declare how much they are willing to pay for a ticket. Users are only charged when the concert is confirmed. Taking action Songkick partners with a promoter and contacts the artist. The user is contacted with the confirmed date and ticket price. If the user can‟t make the date of the concert, they are not charged. Catching on Eventful‟s „Demand‟ functionality allows users to declare an event or performance they would like to see locally, and then campaign for others to join their "demand“
  19. 19. Join the Intention Economy How can big companies harness the opportunity? 1. Forget big data There is significant opportunity in doing „small data‟ well – building profitable relationships with customers through managing their personal information more effectively 2. Expect customers to gain more control than they have already The pace of change isn‟t slowing down – social media has given customers a voice and taken control away from companies, but new tools and the continued democratisation of data will bring further empowerment 3. Build capabilities to listen, respond and adapt Accept the new reality and build the skills and competencies to listen and respond to customers in real time, and the internal processes that enable quick changes of direction based on this feedback IntentionEconomy3
  20. 20. Be an artist – and a scientist “The hottest job right now is 'data scientist', but that label is wrong. It's not just about science, it's also about art. The hottest job should be data artist -- those who understand the quirks of data and think through and enjoy the nature of what you're talking about.” Douglas Merrill, CEO ZestFinance (Ex-Google VP Engineering) Statistical analysis needs to be robust… Without question, technical skills are required to gain the most from data analytics and confidently identify relationships and patterns …but only a ‘human’ perspective can understand the real drivers for analytical conclusions But quantitative science alone will miss the cultural and social drivers behind customer behaviours Creative communication wins over audiences Clever data visualisation makes the huge scale of big data manageable and engages audiences in the story ArtistsandScientists3
  21. 21. Be an artist - and a scientist Case Study: Proctor & Gamble | Visualising and communicating operational data to support managers in making better business decisions Procter & Gamble has institutionalized data visualization as a primary tool of management with its „Decision Cockpit‟ Common Visual Language A consistent way of displaying information is especially important in a global organisation such as P&G, where managers move regularly between brands and geographical markets. Data Meeting Spaces P&G has built meeting spaces that it calls "Business Spheres" in over 50 locations where management information is displayed for review and decision-making by groups. Getting ‘beyond the what to the why and how’ Decision-makers shouldn‟t spend time with figuring out what has happened in an important area of operations. They should have time to focus on why it happened and how to address the issue. Good visual displays direct management attention to where it is most needed. ArtistsandScientists3
  22. 22. Be an artist - and a scientist How can big companies harness the opportunity? 1. Hire a broader mix of people Bringing art and science to analysis requires a mix of skills – quantitative and qualitative; logical and intuitive 2. Watch, then analyse Watching how customers behave will help to form hypotheses for why they do what they do – which can then be validated through focused analysis 3. Tell stories Tap into your audience‟s rational and emotional needs to engage them in analysis and insights – distil to key messages and use vivid imagery to communicate robust evidence ArtistsandScientists3
  23. 23. In summary...
  24. 24. If companies don‟t realise the big data opportunity, they will become obsolete Takeaways4 If you remember nothing else.. Generate new revenues from exhaust data Combine data sources to derive value Be an artist and a scientist Give people the tools to tell you what the want
  25. 25. 29 Wootton Street, London SE1 8TG. | Big Data | Taming the Data Monster Robin Scarborough +44 (0)7538 406662