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Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
Data Science for Agile Strategy
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Data Science for Agile Strategy

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  • Good afternoon & thank you for invitation, It’ an honour to contribute which such a group – where else can you learn about Boris bikes, football analytics and hear the story of our industry from George Dyson. Story about speed, culture & new ways of making decisions
  • Pinnacle of motorsport, 12 teams, 20 countries every year Development budgets of $300m pa. Cars that can accelerate 0-100mph & back zero in under 5-seconds When braking & in some corners the drivers can pull 5g It’s hyper-competitive, where differences are measured in hundredths of seconds You may think this is just about a fast car & a driver keeping their foot down. It isn’t quite that simple. Race strategy, & data that drives it, is core to winning - and losing – races
  • Underpinning RS is clear intent of what the team is trying to achieve balancing risk & reward Second, an array of data signals. Telematics gives 1,700 data channels tracking everything from your throttle, braking & all manner of engine settings. Typical car is producing 2Gb data per lap & 7Tb over a race weekend. Live timing & GPS data tracks circuit position of all competitors. Hard data plus ‘soft’ - inferred intelligence from unusual sources. eg Broadcast TV infer current engine, steering & braking profiles Vital intelligence for understanding the race, identifying pit stop opportunities & making better decisions on tire choice. Counter- intelligence - radio broadcast swearing
  • Faced with so many signals & sources, as important as analytics are, effective communication of the data & any algorithmic output is key. When making decisions in seconds; data must be consumable e.g. Race trace shows evolution of position on every lap in real time. Vital data on tire-choice, pit stops & lap-time trend provides fast feedback to recalibrate scenarios. Infer ‘velocity trends’: who’s catching who, who’s a threat, spot upcoming crunch points in the race or shifts in competitor strategies.  
  • Use this to forecast competitor pit stops, race strategies & impact of these decisions on finishing position.
  • The sheer speed means that you’re competing in turmoil – the fantastic plan you crafted before the race unfortunately only lasts 3 seconds as by the time you get to the first corner things start changing! So data, and the associated forecasts of pace, position and gaps, is driving constant what-ifs to recalibrate options in real time as race events unfold. <pause> It’s the teams that respond faster and better to changing conditions that win.
  • Race strategy is an OODA loop. With data driving the tempo & reduction in decision cycle. Tempo is military strategy concept; strategic value is the number of events in a given period that require a competitive response Adapt loop to be intent driven, use analytics to add power in explore options & viz to improve narrative. But at heart it’s a loop. In data driven world capability for fast feedback loops is key Not strategy then execution, it’s strategy & execution, at same time.
  • Two races in F1. The first is the race day race on the track. But winning the championship is not just how fast your car is at start of season, but critically how quickly and effectively you can improve your pace throughout the season. The second race is back at the factory. Teams are now using data to understand and improve the performance of their organization. Over summer, teams start thinking about next years’ car. This year, one team has taken a ‘data first’ approach. Before turning on a CAD tool they’re blueprinting how they will use data across everything they do to gain advantage: - How the car is designed; enabling better experimentation and faster feedback cycles - How the organization is structured, building in flexibility, performance clarity and a new cultural norm of agility. - How it will be raced; next gen RS is not just predictive software used trackside on day but plugged into whole organization & stretched from a race to a season Every business is now a data business. Even race teams.
  • Example of hidden data 3000 projects 97% failure rate 16 weeks
  • Race teams are using data to fight an arms race in innovation. And we’re finding this thinking has moved to the boardroom. MIT Sloan published research last year that suggested data-driven organizations outperform their peers by 5%. Data has evolved from being a scarce resource to being super-abundant; the cost of storage, processing and distribution is now enabling new capabilities in discovering insights, exploring many more scenarios, reacting to change much faster, optimizing solutions and communicating to a wider audience. Many of the questions are the same, the goal of better insight, strategic response & capability to gain competitive advantage. Henry Miller “One’s destination is not a place, but a new way of looking at things”. To cope with the data genie having escaped the successful organizations of the future will be underpinned by agile structures in which data is turned into an asset.
  • Data genie presents challenges: - Information overload hampers insight & decision-making, - The challenges we’re solving tend not to be static snapshots so how to can manage the data over time, - How do we cope gracefully with the “garbage” when at a human level both “hard and soft” data are useful in informing decisions. - Perhaps more important than the technology, gaining sustainable advantage with data requires a shift in organizational skills, capabilities and culture. 1, it must be intent driven and looking to answer important, valuable questions, rather than chasing technical solutions i.e. what is the strategic purpose? 2. it needs to be adaptive, experimental, good at creating hypotheses and validating them, and not force-fitting old solutions to new problems i.e. what are the most appropriate analytics? 3. it must be actionable, so the insights must be clear and the data must be consumable. So alongside your data wizards you need good human behavior and visualization skills to tell the crucial story.
  • So let’s apply some common sense
  • Keep it real and our feet on the ground Head & tail around data analytics This mix of SAD enables agility
  • Race teams can attest to “no plan survives engagement with the enemy”. Traditionally firms invested 80% energy in the perfect plan. But plan wrong. So flip 80/20 Van Molke, Prussian General Strategy as intent = the evolution of a central idea through continually changing circumstances” Data drives this. In tighter loops than competition It’s true for race teams, and I believe it’s true for corporate strategy, product design and service delivery. Ultimately your core advantage is the ability to learn and adapt.
  • Intelligence FS Info Provider Problem outline: understanding customer behavior, improve product design
  • PE
  • At a glance fingerprint Hedge funds
  • PE
  • Shared across roles Before/After capability
  • 3 mistakes Confusing understanding with information Confusing clarity with detail Confusing outcomes with measures Use help do the right things to shift the odds in your favour
  • It’s about Intent, answering important, valuable questions that people care about And making it consumable
  • Start with what you have. Most data is crap. Figuring our graceful ways of dealing with crap data is a source of advantage Most decisions, esp strategy, are forward facing, so inherently uncertain. Hard facts is history.
  • Create fast feedback loops; Recognise that strategy & execution not sequential, but run in parallel. So they to get smart in picking up signals, interpreting them and reacting - feedback at strategy, product & organisation level drive this capability to learn & adapt. OODA loops rule
  • Be temporal, treat all data and numbers as timeseries so that they can be flexed, stressed, updated as events unfold around you. The plan will be wrong, so the ability to continuously search for new scenarios and adapt your strategy gives a real edge (and a very different culture).
  • Make is easy to consume Goal is not to have super sexy Data Science team making magic. Goal is for Product Owners, Brand Managers, Country Managers knowing what it is, why it’s important and what to do about it
  • My view is that this next decade is going to see a step-change in how organizations, governments and individuals perceive data. They may see data purely as truth i.e. the facts, or as entertainment, think about your fantasy football team or graphics used in the Olympics to show the world record pace, or even as a metaphor as in maps over the years. But whatever the perception when added to the heady mix of increasing speed, highly complex problems and evolving cultures, many industries, from healthcare to aerospace to sport to media are recognizing that it’s a Moneyball moment. Strategy = clear intent Analytics = robust, greater coverage, Design = at a glance understanding & decisions   I hope I’ve given you something to think about and look forward to continuing the conversation. Thank you
  • Transcript

    • 1. Data science for agile strategyFrom Formula One to the BoardroomSimon Williamssgw@quantumblack.com@sg_williams@quantumblack
    • 2. QuantumBlack ©2009-2012
    • 3. Array of signals
    • 4. An arms race in learning
    • 5. The data genie is out of thebottle
    • 6. What is your intent?Or, it’s not the size of yourdata that counts.
    • 7. “The goal of forecasting is notto predict the future…”
    • 8. “…but to tell you what to needto know to take meaningfulaction in the present”Paul Saffo, HBR
    • 9. Response enabled by dataNo such thing as a It’s how you Critical advantage isperfect plan respond that wins to reduce your decision cycle
    • 10. Our manifesto for gettingstarted
    • 11. It’s not about the analyticsAvoid grand schemesLoops not linesBe temporalStrive for the human touch
    • 12. It’s not about the analyticsAvoid grand schemesLoops not linesBe temporalStrive for the human touch
    • 13. It’s not about the analyticsAvoid grand schemesLoops not linesBe temporalStrive for the human touch
    • 14. It’s not about the analyticsAvoid grand schemesLoops not linesBe temporalStrive for the human touch
    • 15. It’s not about the analyticsAvoid grand schemesLoops not linesBe temporalStrive for the human touch
    • 16. Q&ASimon Williamssgw@quantumblack.com@sg_williams@quantumblack

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