Using big data in product design. Executive Director Jesper N. Pedersen

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1 Using big data in product design Executive Director Jesper N. Pedersen

2 Agenda The challenge is Big data a Big thing? Danish Innovation approach 1. Product innovation 2. New online services 3. Smart city Lessons learned

3 Wo we are Some of our clients Jesper Nørding Pedersen. Executive director & Partner Advice

4 The hype Big data is like teenage sex Everybody is talking about it Everyone says that they are doing it Nobody has really tried it

5 Transistor count The reality we have the power Year

6 And we can now understand the world by algorithm

7 But to use data we have to understand our perspective. What are we looking at, and what are we trying to solve? shaunaxton.com

8 Inefficient use of ressources (96% of the time, cars stand still)

9 Provide better health care

10 Be more effective

11 Create new experiences?

12 The data points are increasing. Internet of things is changing our understanding of the real world

13 Benefits Creating an overview of own data sources Customer segmentation Optimization by combining own and external data Better pricing opportunities Typical effects Productivity Additional profits sales Inventory optimization Data driven innovation New services Faster product development Expanded business areas New businesses Selling data Intelligent concepts Figure 2: Benefits related to big data and data driven business development. Source: IRIS Group 13

14 How is Denmark facing this challenge? 14

15 Denmark 6 million people EU member since 1972 The happiest people in the world Because of our size we are dependent on exports and innovation

16 World Economic Forum Information Technology Index 2015

17 World Economic Forum Information Technology Index 2015

18 Danish innovation approach

19 20th century product innovation + Hardware Engineering

20 20th century product innovation + design

21 20th century product innovation + User Experience

22 So, if innovation is based on big data. How do we compeed in 2020?

23 Front-runners Two kinds of business in Denmark Start-ups (2 types) Analysis and Consulting Companies Innovative start-ups who has had success with the use of (public) data. Larger Corporations With existing large scale Business Intelligence departments, such as Vestas, Lego, Grundfoss, Mærsk, etc.. Source: Danish Business Authority report (2013); Potentials and Barriers of Big Data in Business Development. 23

24 4 Main drivers Data & Technology Digitization Data driven business development New business models Accelerating competition Figure 1: Big Data and datadriven business development the most important drivers. Source: IRIS Group 24

25 Government as enabler: Free open core data

26 Public data is great business Net provenue Free open data Private sector Government sector 26

27 Core data v.1 People Business Property Adresses, roads, areas Maps & geolocation 27

28 Three main areas of big data innovation Product design New online services Smart city

29 Danish product innovation

30 case Product innovation based on structured and non-structured data on online usage LEGO Employees 13 % Revenue growth 2014 more than: 130 Countries Source: LEGO Group Annual Report 2014

31 case LEGO INTENTION à ATTENTION Social networks Lego.com online community ABILITY TO CHANGE AWARENESS Special LEGO user groups ( members) Interaction with lead users Online Sentiment Analysis LEGO Universe (online gaming)

32 Courtesy of Vestas Wind Systems A/S case VESTAS Using big data to choose location of major windmill sites & bringing data and insights to the windmill owners wind turbines 70 countries 90 mio MWh pr year

33 Case: Vestas Data sources Vestas invested in the super computer FIRESTORM, which gather data from: public weather stations world wide More than 150 different parameters every 6 th hour Individual wind turbine s performance (SCADA) Production charts Event and alarm notifications Performance and power curve reports Point of common coupling measurements 33

34 From big data to big business

35 case Vaavud design to collect data An app that enables users to measure wind speed at their location. The app uses a plug-in wind meter, with sensors that makes measurements per second.

36 case Vaavud An app that enables users to measure wind speed at their location. The app uses a plug-in wind meter, with sensors that makes measurements per second.

37 Data sources The data is produced by the users and every measurement is shared via the app Data from build in barometers in most newer smartphones Creates a high-resolution local weather forecast 37

38 case Vaavud An app that enables users to measure wind speed at their location. The app uses a plug-in wind meter, with sensors that makes measurements per second.

39 JOM Mama case Novo Nordisk - Indonesia Preventing diabetes Nurses are able to monitor use of app between visits and can make necessary motivational calls Public-private partnership between Ministry of Health, Malaysia, pharma-company Novo Nordisk, University of Witwatersrand in Johannesburg, University of Southampton, Steno Diabetes Centre in Denmark and Advice Preventing diabetes in young couples by helping them to a healthier llifestyle. After a visit to the nurse the couple downloads an app with daily challenges Scientists get access to data from app and nurses journals in order to analyse behavioral patterns

40 New online services

41 case Boliga.dk commercial web portal using public open data to create new ways of buy/sell real estate. Residences demurrage Filtering options: Price changes Districts Characteristics Prices The geographical outline A number of general trends in the housing market What is for sale or has been offered / sold in a given area and at what prices Prices similar homes have been sold for Statistics on: Average prices per m2 Biggest price changes Most and least expensive real estates within a certain area

42 Case: Boliga.dk How? Collects data from real estates on the market Collaboration with regular estate agencies Automation of data collection Public Innovation Service (OIS) 42

43 Case: Boliga.dk Data sources The open data source Building and Housing Register (BBR) which is offered by the Public Innovation Service (OIS). Covers: Area, Location, Use, Installations, Water and drainage conditions, kitchen facilities, wall- and roof materials, annual rent, etc. Estate agencies own data about price and price changes, how long the item has been on the market, etc. 43

44 case Boliga.dk commercial web portal using public open data to create new ways of buy/sell real estate.

45 case Boliga.dk commercial web portal using public open data to create new ways of buy/sell real estate.

46 Case: Boliga.dk Possibilities Potential types of public open data in the future Pollution School ratings Crime statistics Risk analysis s (eg. water damage reports) 46

47 case NemHandel (EasyTrade) NemHandel is an open e-business framework which allows businesses to send standardised electronic invoices directly from their PC via the internet in a secure and reliable fashion.

48 Effective invoice system Replaces less cost-effective solutions, such as paper invoices. Reduce administrative burdens Connectivity across Europe Based on experience with NemHandel Denmark has played an active role in the design, development and implementation of OpenPEPPOL (Pan- European Public Procurement Online) 48

49 Mandatory e-invoicing Legislation & cooperation with business organisations Reduce administrative burdens More than 28 % of Danish Businesses uses NemHandel (2013) Connectivity across Europe Based on experience with NemHandel Denmark has played an active role in the design, development and implementation of OpenPEPPOL (Pan- European Public Procurement Online) 49

50 Case: Rejseplanen.dk Data source NHR (NemHandelRegister / EasyTradeRegister) is a data base of all NemHandel registered recipients. 50

51 case MinBilDinBil (MyCarYourCar) MinBilDinBil is a car renting company, that allows private people to rent their cars to other private people. Uses open data for business development, marketing, etc.

52 Case: Rejseplanen.dk Data sources Mortorregistret (Danish Vehicle Register) Statistics Denmark Data set from the municipality of Copenhagen: The Copenhagen Map Copenhagen Data 52

53 Smart cities

54 case Open Data Aarhus Aarhus is the second largest city in Denmark. The municipality has released a lot of different open data sets to improve the city.

55 case Aarhus The map shows how well the traffic is flowing right now. It is based on data from OpenStreetMap and real-time traffic data taken from the

56 case Aarhus Traffic intensity in relation to recreational areas. Made with open data from odaa.dk

57 case Odense DataPlatform Odense is the third largest city in Denmark. The municipality has released a lot of different open data sets to improve the city.

58 case Public parking lots Web service that tells how many parking lots there are, and how many of them are vacant.

59 case Public parking lots Web service that tells how many parking lots there are, and how many of them are vacant.

60 case Public Lavatory App that uses open data to help users find the nearest public lavatory facility.

61 case Copenhagen Data Copenhagen is the capital of Denmark and the municipalities has also released a lot of open data sets.

62 case Water Quality for outdoor swimming Copenhagen has an automatic alarm system when it s not recommended to swim in the harbours.

63 case Water Quality for outdoor swimming Copenhagen has an automatic alarm system when it s not recommended to swim in the harbours.

64 Lessons learned: Data driven business development

65 Big business Research show big economical potentials in strategic data driven business development Companies that uses big data performs 5-6% better by income than companies that do not use big data. McAfee og Brynjolfsson (2012); Big Data: The Management Revolution i Harvard Business Review Source: Danish Business Authority report (2013); Potentials and Barriers of Big Data in Business Development. 65

66 Big Data - The 3 V s Volume The large and exponentially increasing amounts of data generated and stored every second of businesses, citizens and authorities. Velocity The increasing speed in which data can be stored and analysed - partly because of developments in computing power of large computers. Variety The increasing number of data sources that are available for businesses, which can be easily collected and communicated through services such as broadband and mobile technologies. Data come from computers in production, sensors, social media, customers, traffic on the company's website, text files from the Internet, digital cameras, public data sources, etc. Source: Danish Business Authority report (2013); Potentials and Barriers of Big Data in Business Development. 66

67 Benefits Creating an overview of own data sources Customer segmentation Optimization by combining own and external data Better pricing opportunities Typical effects Productivity Additional profits sales Inventory optimization Data driven innovation New services Faster product development Expanded business areas New businesses Selling data Intelligent concepts Figure 2: Benefits related to big data and data driven business development. Source: IRIS Group 67

68 Just the beginning For a few companies in Denmark the positive effects of using big data is starting to show. But the socio-economic benefits are still relatively small. Data driven business development is an important factor to increase productivity. Source: Danish Business Authority report (2013); Potentials and Barriers of Big Data in Business Development. 68

69 Potentials 0,7 % yearly increase in productivity in the life science sector 69 McKinsey Global Institute (2011) ; Big data: The next frontier for innovation, competition and productivity.

70 Potentials up to: 50 % reduction of production cost in product development 70 McKinsey Global Institute (2011) ; Big data: The next frontier for innovation, competition and productivity.

71 Barriers Competences Data availability Ø Framework for big data in existing companies Ø Terms and conditions for new data-driven start-ups Legislation and security Lack of knowledge Financial ressources Figure 3: Figure of key barriers to exploitation and dissemination of big data and data-driven development. Source: IRIS Group 71

72 5 Recommendations 1. Start a big data campaign 2. Strengthen big data throughout the education system. 3. Better access to public data 4. Safe and legal use of personal data 5. Establish big data start-up programs Source: Danish Business Authority report (2013); Potentials and Barriers of Big Data in Business Development. 72

73 Wo we are Some of our clients Jesper Nørding Pedersen. Executive director & Partner Advice

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