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![Strictly Private and Confidential © 2015 ThoughtWorks, Ltd. All rights reserved.
FRAMINGTHEHYPOTHESES
We know that:
[insight and data that lead to hypothesis]
We believe that:
[building this capability/feature][for these people]
Will result in:
[this outcome]
We know we will have succeeded when:[we see this measurable signal]
Process
This structure helps
ensure a quantitative
and objective validation
of ideas](https://image.slidesharecdn.com/twdatastrategypresentationv5-160913192630/85/Data-Strategy-Enabling-the-Data-Guided-Enterprise-21-320.jpg)
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Data Strategy - Enabling the Data-Guided Enterprise
This document discusses how companies can develop an effective data strategy to become more data-driven. It emphasizes that a data strategy should target value proposition, culture, processes, people, and technology. It also stresses that successful companies set clear goals for how data will be used, define what success looks like, and ask the right questions of their data. The document provides examples of how companies like AutoTrader and Etsy have transformed their culture to be more data-guided in order to gain competitive advantages.
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Data Strategy - Enabling the Data-Guided Enterprise
- 1. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. DATASTRATEGY Enabling the Data-Guided Enterprise
- 2. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. 2 The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results. Big Data: The Management Revolution - HBR Oct 2012
- 3. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. DATASTRATEGY Effective Data Strategy targets these important dimensions Value Proposition - Why is this important for your organization? Culture - How does becoming data-led affect your organizational culture? Process - What are the key processes for an effective data strategy? People - Do you have the right people and skills to achieve your data goals? Technology - How does the new data strategy affect your technologies? Lean Road Map - How do you get started and what does success look like?
- 4. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. WHYISBECOMINGDATA-GUIDEDIMPORTANT? Increase revenue and widen margins through: Value Efficiency: tailor product offerings more efficiently through analytics. Innovation: use measurable hypotheses to ensure most effective proposition development. Insight: tailor data offerings to support the decision-making process for customers. Intelligence: unlock opportunities within and beyond core business.
- 5. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. AUTOTRADERUK Buyers Sellers From paper… Buyers Sellers …to digital… Dealers Manufacturers Owners Shoppers …to data guided Value
- 6. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. Making Money from Data Audience Growth Operational Effectiveness Achieving our Purpose Value
- 7. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. 7 Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions. Big Data: The Management Revolution - HBR Oct 2012
- 8. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. FROMWHEREYOUARETOWHEREYOUWANTTOBE Data Averse Data Unaware Data Aware Data Guided “We don’t need data to tell us what we already know.” “We need to make better use of our data, but we don’t know how.” “Data has never been a priority for us. What is the benefit?” “Data confirms every decision we make and action we take.”
- 9. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. DATACAPABILITYMATURITY What happened? Why did it happen? What will happen? How can we optimize? Analytical sophistication Valuecreation COMPLEXITY BENEFIT DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE ADAPTIVE How can we Learn? Value
- 10. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. REARVIEW=BUSINESSATPARITY What happened? Why did it happen? What will happen? How can we optimize? Analytical sophistication Valuecreation COMPLEXITY BENEFIT DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE ADAPTIVE How can we Learn? Reporting and Business Intelligence Value
- 11. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. FUTUREREADINESS=COMPETITIVEEDGE What happened? Why did it happen? What will happen? How can we optimize? Analytical sophistication Valuecreation COMPLEXITY BENEFIT DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE ADAPTIVE How can we Learn? Reporting and Business Intelligence Data Science & Engineering Value
- 12. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. VALIDATEDLEARNING=DATA-GUIDEDENTERPRISE What happened? Why did it happen? What will happen? How can we optimize? Analytical sophistication Valuecreation COMPLEXITY BENEFIT DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE ADAPTIVE How can we Learn? Reporting and Business Intelligence Data Science & Engineering Data Guided Enterprise Value
- 13. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved.
- 14. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. Over 30 million unique items Search accounts for 30% of all traffic Search relevance is a huge problem
- 15. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. DATA-GUIDEDETSY Culture shift to learn, discover, iterate Started with 5 KPIs Now adding 300K new metrics/month 15 Culture
- 16. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. 16 Data and metrics now support the entire company's operations. At Etsy, there's a saying: "If it moves, graph it." Kellan Elliott-McCrea , Etsy CTO "And if it doesn't move, graph it anyway because it may make a run for it."
- 17. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. ALIGNDATASTRATEGYWITHBUSINESSSTRATEGY From this… …to this! “We built a data lake for self-service, now we’re trying to get the business to use it.” Culture
- 18. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. WHATDOESBEINGDATA-GUIDEDMEAN? Looking at Data with a Product Mindset Seeing Data Innovation As a Constant Practice Lean Experimentation and Decision-Making Data at the Core of the Business Culture
- 19. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. DATA-GUIDEDINDIVIDUALBEHAVIOUR Data Skills • Data Exploration • Analytical Tools • Simple Visualizations • Simple Statistics How am I going to be successful as part of a data-guided organisation? Data Thinking • Data Curious • Questioning • Hypothesis Driven Lean Approach •Small Rapid Cycles •Validated Learning •Ruthless Rejection of Ideas Culture
- 20. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. Use Hypothesis-Driven Development across two agile tracks for better outcomes Experiments are used to validate new ideas for investment Hypothesis-Driven Development to validate that new functionality leads to expected outcomes ProcessIMPLEMENTINGHYPOTHESIS-DRIVENDEVELOPMENT Discovery Track Development Track
- 21. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. FRAMINGTHEHYPOTHESES We know that: [insight and data that lead to hypothesis] We believe that: [building this capability/feature][for these people] Will result in: [this outcome] We know we will have succeeded when:[we see this measurable signal] Process This structure helps ensure a quantitative and objective validation of ideas
- 22. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. ProcessHYPOTHESIS-DRIVENDEVELOPMENT Discovery Track development track Development Track
- 23. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. ProcessLEANPROPOSITIONDEVELOPMENT OPPORTUNITY: “Assist automobile dealers in reducing the mean time to vehicle sale.” VISION STRATEGY EXPERIMENTS Goals KPIs Signals Multi-year time frame 3 months 2 wks 2 wks 2 wks 2 wks 2 wks 2 wks 2 wks 2 wks 2 wks 2 wks 2 wks 2 wks
- 24. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. 5BUCKETS… Analytics & TechBusiness Value DATA SCIENCE DATA ENGINEERING DATA WAREHOUSING DATA SOLUTIONS DATA STRATEGY People Uncovering data opportunities and guiding the vision for transformation organizations to become data-led Developing specific business solutions that are powered by data science & engineering Complementary data skills for organizing, pipelining, managing, and analyzing data
- 25. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. UNIVERSEOFDATASKILLS Management & Planning Data Science Data Engineering Data Analysis Communication Shaping & Planning Project Management Maths Machine Learning Statistics Prototyping Data Warehousing Data Architecture Data Munging Data Discovery Advanced Visualisation Authoring / Public Speaking Facilitation Concept Generation Visual Story Telling / Data Journalism Business Domain Research People “Big Data”
- 26. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. REMOVINGTHEDATABOTTLENECK Data Warehouse Incoming data is cleaned and organised into a single schema up front. Data Lake Incoming data goes into the lake in its raw form. Long development times to create new value from data. Analysis activities are distributed across technologists and business users. Technology Analysis is done directly on the curated warehouse data. Data is selected, structured, and organize as needed, when needed.
- 27. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. OPERATINGANEFFECTIVEDATALAKE Operational systems stream data into the lake using topical queues Data scientists investigate the lake for new insights Lakeshore marts curate and organise the data for self- service analysis Technology Multi-tiered data lake for processing, distribution, and serving Operational systems share data via services
- 28. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. DATAARCHITECTURE-AUTOTRADER SPARK PUBLISHER HDFS Spark SQL JDBC R TABLEAU JAVA PYTHON DW Publisher Publish Data in different representations. Internal & External Analytics Platform Process, cleanse and extract information for analytics. Real time KPI streaming Learn and discover on ad hoc basis Data Lake Retain and open up access to valuable data. Track usage and value of data sets Technology
- 29. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. GETTINGSTARTED DISCOVERY Data Assets Technology People Processes Mindset/Culture ASSESSMENT VISIONING ROADMAP Strategy Week 1 Business Week 2 Week 3 Week 4 Leadership Sponsors
- 30. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. 30 Data-driven transformation isn't just about technology adoption, it requires changing the very culture of your organization — the way your people think, interact and work. Why a Data-Driven Transformation Requires a Culture Shift CIO Magazine Apr 2015
- 31. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. LEARN FAST: Make minimum viable experiments everyday Data is a new currency Sean McClure, A Data Scientist's Advice to Business Schools THINK BIG: Break ambitions into small steps for action START NOW: What's the cost of delay?
- 32. Strictly Private andConfidential © 2015 ThoughtWorks, Ltd. All rights reserved. BUILDING BUSINESSES DATA-GUIDED
Editor's Notes
- #2 Culmination of 3 weeks onsite + 2 weeks offsite, larger scale engagement than planned due to the breadth of scope We have really enjoyed working with AT on this Now, we are going to present a summary of our recommendations This is in effect an overview of our full report, with our more detailed findings and recommendations We have put breaks in the presentation at what we hope are the most useful points, so we can discuss the material while it is still fresh in everyone’s minds
- #3 In particular, companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors
- #6 Optimise current New product development Insight as a product - becoming a pure insight provider to others Smarter ways of serving the market: putting smarter analytics in the operating systems to improve customer journeys, and honing insight to share relevant data with new insight customers - e.g. manufacturers
- #10 Not every organization is ready to be like Google or Facebook. But leading companies seek to become more data guided to validate intuition and judgement.
- #11 Lean Approach assess, validate, fail
- #15 Etsy is an e-commerce platform for small artisans to sell handcrafted items.
- #19 Data strategies that are driven by IT are focused on infrastructure, platform, and technologies. When data strategies are driven by the CEO or CDO, they are guided business priorities and value propositions. “How does becoming a data-led company help us become more competitive?”
- #20 MB 3-11
- #22 Lean Approach assess, validate, fail
- #23 Discovery Track - used to design and test something new, encourage fail fast and change tack approach Development Track - during development to validate that what is being built or maintained is meeting the outcomes that we expect
- #24 As seen in the away day Discipline of developing a hypothesis BEFORE taking an action, and setting measurable goals for success or failure.
- #25 Discovery Track - can go round several ways for example - historical data, counter-factual estimation, small scale manual experiments, small-scale Experiments through parallel running Development track - putting this into production and measuring the outcome before scaling; reviewing the performance of the product on an ongoing basis, to feed back into further discovery for improvement
- #26 Within each squad, these Experiments test the proposition frequently on a micro-level, reducing risk and the cost of failure. The business domain strategy, as hypotheses - for example for consumer - is iterated quarterly, and the overall business vision is amended to reflect these learnings. These squad experiments are prioritised in terms of criticality and risk of failure, on the basis of answering the most important elements of the domain hypotheses. So, the signals from experiments directly inform domain hypotheses, which in turn deliver or amend overall business goals. So, the business becomes a leaner organisation, by efficiently directing effort at the most important questions to realise the most valuable opportunities.
- #27 Lean Approach assess, validate, fail
- #28 The universe of data science skills is very broad, with the value in the mix of skills that cross the whole business - it’s really about how they are combined
- #29 current - small group of people who don’t work in the squads to understand the context of their needs and gain frequent feedback TRYING to build a one-size fits all system to serve everyone Move to.. Raw Data accessible to everyone, through a variety of technologies, so that squads can organise and publish the data they need
- #30 Building data assets, and enabling AT prod and services SUPPORTED by experiments Move to a situation where operational systems co-operate where they need without integrating with DW Exploration happens at the lake, and we serve up just what is needed for each system Start with Product track exploration because it’s easier to think about
- #31 Lean Approach assess, validate, fail
- #34 What defines a data-enabled business is one that is able to exploit this currency to deliver exceptional value… To be successful as a data-driven company, we believe that Auto Trader needs to: link current experiments more clearly to support an evolving future business vision be ruthless in prioritising the highest potential value opportunities, and in developing the new capabilities needed to identify and deliver them challenge yourselves to put hypotheses and measurement at the centre of everything you do, to simply achieve more
- #35 Culmination of 3 weeks onsite + 2 weeks offsite, larger scale engagement than planned due to the breadth of scope We have really enjoyed working with AT on this Now, we are going to present a summary of our recommendations This is in effect an overview of our full report, with our more detailed findings and recommendations We have put breaks in the presentation at what we hope are the most useful points, so we can discuss the material while it is still fresh in everyone’s minds