How To Understand The Benefits Of Big Data

Transcription

1 Big Data & Sustainability

2 Agenda Welcome & Introduction to CCES Richard Green Introduction of Guests Cian Duggan Jane Baptist, The Crown Estate Opportunities of Big Data in Sustainability Coffee Break Tom Brown, The Information Labs Challenges of Big Data in Sustainability Sam Carson, Carbon Credentials Close, Feedback Forms, Networking RG

3 RG

4 Guest Introductions - Name and Organisation - A particular opportunity? - A key challenge? CD

5 Why Sustainability: The Greatest Global Predicament? Everything we need for our survival and well-being depends either directly or indirectly on our natural environment - anon

6 Why Big Data & Sustainability? Big Data is a Game Changer for Environmental Managers, Advocates and Regulators The Environmental Law Institute, 2013 Big data promotes transparency and clarity when developing sustainability and business strategies. The Guardian, 2013 Big data is as powerful as a tsunami, but it s a deluge that can be controlled... in a positive way, to provide business insights and value Forbes, 2014

7 What is big data?

8 Big Data Characteristics the 3 Vs

9 Why is Big Data Important for Sustainability Source: PwC

10 Big data fine, but we need insight from analytics 1. ANALYTICS: association, classification, clustering, design, optimisation, prediction, recommendation, search/ranking 2. THE UNREASONABLE EFFECTIVENESS OF DATA: 1. algorithms + data => insight => decisions => value 2. Simple models and a lot of data trump more elaborate models based on less data

11 Corporate EH&S Priorities in the Next Year? Data Source: Verdantix global survey, 2014

12 EH&S Spending Increases Vary by Programme Source: Verdantix global survey, 2014

13 How are Top-performing Organisations Using Big Data? Analytics trumps Intuition Sustainability just ask the Crown Estate! Top performing organisations apply analytics to their activities 5 times more than their competitors Chart shows likelihood that the organisations will use either analytics or intuition; 1 = equal likelihood Big Data, Analytics and the Path From Insights to Value MIT Sloan Management Review. Lavalle et al, 2011

14 Jane Baptist, Assistant Head of Sustainability at The Crown Estate

15 The Crown Estate Big Data Jane Baptist - Assistant Head of Sustainability Jane.baptist@thecrownestate.co.uk

16 Independent, commercial, property company set up under Act. 8.6bn portfolio 253m contribution to the Treasury (2013/14)

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18 Total Contribution

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21 Data Review process

22 Confidence/ Assumption

23 Carbon net positive 4 million tco 2 more emissions sequestered through forestry and avoided by low carbon energy generation than emissions generated.

24 Opportunities to Identify where action required Be more ambitious Use big data to convince in decision-making Set requirements of new data systems Tell a story over time

25 Challenges Accurate and comprehensive data in right format What about? Compatible databases Social impact data

26 Next steps Improve accuracy and scope of data Manual Methodology to measure trend Ensure materiality

27 Thank you for listening.

28 Opportunities Presented by Big Data

29 Thriving in a Big Data World: Using Datafication 1. Use all the data, not just a sample. 2. Accept messiness; more trumps better. 3. Embrace correlation. Every single dataset is likely to have some intrinsic, hidden, not-yet-unearthed value, and the race is on to discover and capture all of it. Mayer-Schönberger & Cukier, 2013

30 Opportunity Case Study By getting to grips with that huge amount of data BT was able to effectively highlight carbon hotspots - areas where it could focus its efforts to create opportunities for carbon and cost reduction. It has also allowed the business to set its Net Good 3:1 goal, with the company aiming to help its customers to reduce their carbon emissions by at least three times the end-to-end carbon impact of BT s own business.

31 Opportunity Case Study UPS s ORION (On-Road Integrated Optimization and Navigation) automatically maps delivery routes to include as many right turns as possible. Waiting to make a left turn takes longer than turning right and eliminates engine idling - a major source of wasted gas. ORION uses more than 250 million address data points. By 2017, ORION will have cut GHG emissions and saved UPS $50 million a year in fuel costs.

32 Opportunity Case Study Ford is using big data and analytics to increase fuel economy, reduce vehicle emissions and drive other sustainability advances. Ford utilises the following Big Data tools: Mathematical models - optimize vehicle combinations to help construct an ecoconscious and cost-effective global technology roadmap. Model that projects CO2 emissions generated by the fleet of vehicles on roads worldwide for the next 50 years, helping Ford set aggressive fuel economy targets. Specific tools to provide fleet customers with customized purchase recommendations that help them save money and improve corporate sustainability. Green routing Life-cycle analysis tools

33 Opportunities Roundtable here s a few to discuss 1. Prioritise action on sustainability 2. Understand carbon hotspots 3. Identify sustainability outliers 4. Social data to drive financial performance 5. Benchmarking against industry 6. Performance metrics for decision making 7. Big data smooths out inaccuracies 8. Materiality Assessments 9. Energy-use forecasting 10. New product market research 11. Create lower carbon product options 12. Optimise delivery routes 13. Benefits of transparency 14. Whole life costing models Timings: 35 minutes to discuss 10 minutes to report back

34 Coffee Break

35 Results of Opportunities session

36 Feedback on Opportunities Opportunity Average Score 1. Information to prioritise action on sustainability 7 2. Understand carbon hotspots 7 4. Social data helps drive financial performance 6 7. Big Data volume smooths out inaccuracies (more trumps better) New product market research Create whole life costing models 5

37 Tom Brown, Director of The Information Lab

38 Can Data Visualisation save the world? Tom Brown

39 Why visualise?

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48 Is it easy?

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54 What can go wrong?

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57 Can you see the problem? Something very bad just happened

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60 Carbon Credentials are using better technology than NASA of the 1970 s. Much better!

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63 In summary By visualising data it becomes possible to find opportunities to reduce emissions which would have gone unnoticed. It s also easier to communicate these opportunities to stakeholders. This can only be a good thing! Which might just save the world?

64 Sam Carson, Carbon Credentials, Director of Data Services

65 Agenda Context Audiences Robust & Flexible Data Systems

66 1. Context is Key!

67 Sustainability is a Whole System View of Business!

68 Communicating Sustainability

69 Example - Understanding One Tonne of CO2 Temperature and Degree Days Asset Lists within Building Building Fabric Benchmarks Footfall Country of Emissions, for Electricity Building Type Occupancy Breakdown of Emissions Sources

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71 Of Course, Sustainability Isn t Just Environmental Maybe you have more datasets than you think Many economic datasets are robust as they are used for business Social datasets might be used for marketing/hr Using these datasets might improve legitimacy in an organisation Social Economic Environmental

72 Wait! You may already have the data!

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74 2. Understand your Audiences

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76 Leadership Needs Clearer Signals

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78 Big Data Isn t More Data

79 3. Data Systems Need to be Robust & Flexible.. This is Possible.

80 Simplified data flow for success

81 Data flows pipes not buckets

82 3 Keys Elements of Successful Sustainability Programme

83 Challenges Presented by Big Data

84 Back to the 3 Vs

85 Big Data Sources are increasing in volume, velocity AND variety

86 Big Data Challenges survey results IT Hound Big Data Survey, March 2013

87 Challenges Roundtable here s a few to discuss 1. Where to focus Big Data investment 2. Getting approval for investment in Big Data 3. Sharing information across organisational silos 4. Gathering external data 5. What data to use 6. Getting quality data in the right format 7. The sheer volume of data! 8. Technology 9. Data structure and version control! 10. Creating questions which leads to insight 11. Visualisation 12. Determining what to do with Big Data insights 13. Finding data scientists 14. Building trust between data scientists and key stakeholders Timings: 35 minutes to discuss 10 minutes to report back *IT Hound Big Data Survey, March 2013

88 In conclusion 1 minute video 5 tips Feedback form

89 Cisco Big Data video

90 Tips for Implementing Analytics-Driven Management 1. FIRST, THINK BIGGEST -- focus on the biggest and highest-value opportunities 2. START IN THE MIDDLE start with questions, not data 3. MAKE ANALYTICS COME ALIVE embed insights and visualisations to drive actions and deliver value 4. ADD, DON T DETRACT build on existing capabilities and datasets 5. BUILD THE PARTS, PLAN THE WHOLE use an information agenda for future planning Big Data, Analytics and the Path From Insights to Value, MIT Sloan Management Review

91 Results of Challenges session

92 Feedback: Challenges Challenge Average Score 1. Understanding where in the organisation to focus Big Data investments 7 2. Getting top management in the company to approve investment in Big Data 6 3. Getting business units to share information across organisational silos 7 4. Gathering external data from suppliers and other sources 4 7. The sheer volume of data interpreting thousands/millions of date points 4 8. Technology that can control the large volume, velocity and variety of Big Data 7

93 Thank you please fill in the Feedback Form