Volkswagen Data Lab at the CeBIT 2014
Transcript of Volkswagen Data Lab at the CeBIT 2014
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Volkswagen Data Lab at the CeBIT 2014
Christian Seidel, Cornelia Schaurecker
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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.
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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
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Volkswagen Group
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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“
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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
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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
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CeBIT Movie
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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
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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
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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)
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Fleet dashboard
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e-UP! page in detail– real-time car information
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Heatmap - speed
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Heatmap – power consumption
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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
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Top location visits per car
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Internet of Things – bringing data together
14:30
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Trips over time
Weather was getting better during the day
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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
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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
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Challenges – Heatmap query
search sourcetype=e-up index=* NOT (latitude=0 longitude=0) |
geostats latfield=latitude longfield=longitude
avg(DisplayedVehicleSpeed) as value maxzoomlevel=18
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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
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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
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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
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Data quality! Will more people use the shuttle service when it‘s raining?
???
Source: www.freedigitalphotos.net/antpkr
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Data quality! There was no rain at the CeBIT …garbage in - garbage out!
Source: www.freedigitalphotos.net/panuruangjan
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GPS anomalies
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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
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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 ;-)
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Thank you, splunk!