Volkswagen Data Lab at the CeBIT 2014

Christian Seidel, Cornelia Schaurecker

Disclaimer

During the course of this presentation, we may make forward looking statements regarding future events or the expected performance of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward-looking statements made in this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, this presentation may not contain current or accurate information. We do not assume any obligation to update any forward looking statements we may make. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only and shall not, be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionality described or to include any such feature or functionality in a future release.

Data Lab and short Bio The Data Lab is: •  a future oriented, data-driven innovation hotspot for the 12 Volkswagen Group Brands, as well as markets and cross-

functions •  building use cases in the areas of Big Data, Analytics, Connectivity and Information Security •  leveraging the value of Big Data for Volkswagen Group through quick innovative prototyping Cornelia Schaurecker - Director Volkswagen Data Lab Cornelia is an experienced Automotive IT Manager with a long term experience in IT as well as international business areas. Having previously worked for eBusiness & Marketing, she joined the IT for Audi, SEAT and Lamborghini, where she was in charge of the IT-Strategy for these brands and all international websites, CRM, internationalization and setup of new markets. Thereafter, she joined the Volkswagen Group, where she was CIO in UK for the Volkswagen brands. Since June 2013, she has been setting up and is heading the Volkswagen Data Lab. She holds degrees in International Management as well as Industrial Economics, specializing in Digitalisation, IT, eCommerce and Customer Relationship Management. Christian Seidel – Senior Project Manager in the Volkswagen Data Lab responsible for projects in the areas: •  connected customer / connected car •  Internet of Things experience in natural language processing, information retrieval and machine learning Christian holds a PhD in Computational Linguistics from the Ludwig-Maximilians-Universität München

Volkswagen Group

Why a Data Lab inside Volkswagen Group IT? Fast implementation of innovative Use Cases in the areas of Big Data & Analytics

  „Data is the new oil“   Value-driven data analysis   Digital IT solutions   Reducing dependencies from

external Service Providers and Agencies   Internal, scalable infrastructure

 Pre-development with business departments, brands and markets  Quick prototyping development &

environment of Use Cases and growth-orientated business models  Technology-orientated working environment

Customer & Business focus Innovation

Data Speed

  Dynamic partnering for generation of innovative solutions   Flexible IT standards   Fast, content-driven implementation   Improvement of transparency, speed and savings

  Building up own competencies in the areas •  Big Data Analytics •  Connectivity •  Internet of Things •  Information Security

  Building up a culture of innovation to attract „new digital talents“

Examples for Data Lab Use Cases

Data Lab Fleet: Connected Customer / Connected Car

Internet of Things

Analysis & Prediction of Customer Needs

Big Data Analysis & Visualisation in After Sales

Customer Loyalty & Churn Prevention

Information Security

CeBIT 2014 – DataBility

•  Europe‘s largest IT fair •  Topics: Big Data, Mobility, Security •  March 10th – March 14th 2014 •  210.000 Visitors •  ca. 130.000 m² •  Volkswagen Booth: 848m² in Hall 2

CeBIT Movie

The Show Case

Speed

kmh

max 76

35 RPM

rpm

2541 max 5583

Battery

96%

Range

147km

Weather

10.0°C Wipers off

Doors

0 Top destinations

CeBIT - Big Picture

•  Concept started: February 2014 •  Development: 10 days in March 2014 •  15 e-UP! exclusively as shuttle service during all 5 days •  What are the top destinations? •  Fleet overview •  Traffic prediction and optimization

Results: 5.600 km driven 1.600 potential customers (2-3 persons per trip) Internet of things: car data combined with smart watch

Technical setup

•  2 Mac Books Pro •  512 SSD •  16 GB Ram •  i7 CPU •  Splunk Enterprise 6.1 Beta •  Webframework based on JavaScript and HTML5

for customized dashboards •  10 data sources (cars) •  up to 20 parallel searches (transactions)

Fleet dashboard

e-UP! page in detail– real-time car information

Heatmap - speed

Heatmap – power consumption

Heatmap – top locations

Entry / Exit, & Train station

Charging station & Entry / Exit

Pizza place

Volkswagen

Entry / Exit

Sources: www.freedigitalphotos.net/digitalart www.freedigitalphotos.net/graur razvan ionut www.freedigitalphotos.net/piyato

Top location visits per car

Internet of Things – bringing data together

14:30

Trips over time

Weather was getting better during the day

Challenges

Speed

kmh

max 76

35 RPM

rpm

2541 max 5583

Battery

96%

Range

147km

Weather

10.0°C Wipers off

Doors

0 Top destinations

Challenges – What‘s a stop / end of a trip?

search sourcetype=e-up vehicle=WOB-Q7061 |

search NOT (latitude=0 OR longitude=0 OR odometer=0) |

transaction vehicle endswith="doorOpen=1" |

eval range=max(max(latitude) - min(latitude),

max(longitude) - min(longitude))|

where range>0 AND duration>5

Challenges – Heatmap query

search sourcetype=e-up index=* NOT (latitude=0 longitude=0) |

geostats latfield=latitude longfield=longitude

avg(DisplayedVehicleSpeed) as value maxzoomlevel=18

How splunk works …

Heatmap query – as normalized search: litsearch sourcetype="e-up" index=* NOT ( latitude=0 longitude=0 ) | litsearch sourcetype=e-up index=* NOT ( latitude=0 longitude=0 ) | addinfo type=count label=prereport_events | fields keepcolorder=t "DisplayedVehicleSpeed" "geobin" "latitude" "longitude" "prestats_reserved_*" "psrsvd_*" | eval geobin = mvappend("bin_id_zl_0_y_" . floor(('latitude' + 90.000000) / 22.50000000000000 ) . "_x_" . floor(('longitude' + 180.000000) / 45.00000000000000), "bin_id_zl_1_y_" . floor(('latitude' + 90.000000) / 11.25000000000000 ) . "_x_" . floor(('longitude' + 180.000000) / 22.50000000000000), "bin_id_zl_2_y_" . floor(('latitude' + 90.000000) / 5.62500000000000 ) . "_x_" . floor(('longitude' + 180.000000) / 11.25000000000000), "bin_id_zl_3_y_" . floor(('latitude' + 90.000000) / 2.81250000000000 ) . "_x_" . floor(('longitude' + 180.000000) / 5.62500000000000), "bin_id_zl_4_y_" . floor(('latitude' + 90.000000) / 1.40625000000000 ) . "_x_" . floor(('longitude' + 180.000000) / 2.81250000000000), "bin_id_zl_5_y_" . floor(('latitude' + 90.000000) / 0.70312500000000 ) . "_x_" . floor(('longitude' + 180.000000) / 1.40625000000000), "bin_id_zl_6_y_" . floor(('latitude' + 90.000000) / 0.35156250000000 ) . "_x_" . floor(('longitude' + 180.000000) / 0.70312500000000), "bin_id_zl_7_y_" . floor(('latitude' + 90.000000) / 0.17578125000000 ) . "_x_" . floor(('longitude' + 180.000000) / 0.35156250000000), "bin_id_zl_8_y_" . floor(('latitude' + 90.000000) / 0.08789062500000 ) . "_x_" . floor(('longitude' + 180.000000) / 0.17578125000000), "bin_id_zl_9_y_" . floor(('latitude' + 90.000000) / 0.04394531250000 ) . "_x_" . floor(('longitude' + 180.000000) / 0.08789062500000), "bin_id_zl_10_y_" . floor(('latitude' + 90.000000) / 0.02197265625000 ) . "_x_" . floor(('longitude' + 180.000000) / 0.04394531250000), "bin_id_zl_11_y_" . floor(('latitude' + 90.000000) / 0.01098632812500 ) . "_x_" . floor(('longitude' + 180.000000) / 0.02197265625000), "bin_id_zl_12_y_" . floor(('latitude' + 90.000000) / 0.00549316406250 ) . "_x_" . floor(('longitude' + 180.000000) / 0.01098632812500), "bin_id_zl_13_y_" . floor(('latitude' + 90.000000) / 0.00274658203125 ) . "_x_" . floor(('longitude' + 180.000000) / 0.00549316406250), "bin_id_zl_14_y_" . floor(('latitude' + 90.000000) / 0.00137329101562 ) . "_x_" . floor(('longitude' + 180.000000) / 0.00274658203125), "bin_id_zl_15_y_" . floor(('latitude' + 90.000000) / 0.00068664550781 ) . "_x_" . floor(('longitude' + 180.000000) / 0.00137329101562), "bin_id_zl_16_y_" . floor(('latitude' + 90.000000) / 0.00034332275391 ) . "_x_" . floor(('longitude' + 180.000000) / 0.00068664550781), "bin_id_zl_17_y_" . floor(('latitude' + 90.000000) / 0.00017166137695 ) . "_x_" . floor(('longitude' + 180.000000) / 0.00034332275391), "bin_id_zl_18_y_" . floor(('latitude' + 90.000000) / 0.00008583068848 ) . "_x_" . floor(('longitude' + 180.000000) / 0.00017166137695)) | prestats count(latitude) count(longitude) mean(DisplayedVehicleSpeed) sum(latitude) sum(longitude) by geobin

Challenges – distance chart in Fleet overview

search sourcetype=e-up | where speed>0 |

transaction vehicle maxpause=1m maxevents=100000 |

eval distance = tonumber(mvindex(odo, mvcount(odo)-1))-tonumber(mvindex(odo, 0)) |

timechart sum(distance) as km

What we didn‘t expect …

Speed

kmh

max 76

35 RPM

rpm

2541 max 5583

Battery

96%

Range

147km

Weather

10.0°C Wipers off

Doors

0 Top destinations

Data quality! Will more people use the shuttle service when it‘s raining?

???

Source: www.freedigitalphotos.net/antpkr

Data quality! There was no rain at the CeBIT …garbage in - garbage out!

Source: www.freedigitalphotos.net/panuruangjan

GPS anomalies

If we just knew that before …

Speed

kmh

max 76

35 RPM

rpm

2541 max 5583

Battery

96%

Range

147km

Weather

10.0°C Wipers off

Doors

0 Top destinations

If we knew that before … lessons learned

•  Clean GPS anomalies: create a ground truth

via splunk queries, i.e. exclude certain trips

•  Transactions: fast to implement, but not very efficient Ok for showcase

•  Programming was faster than organizing the new cars ;-)

Thank you, splunk!