Data & Analytics Unit (DNA) · VWFS Digital Solutions • 20 years data expertise DSAI TEAM Data Science & AI HEAD OF DATA & ANALYTICS UNIT Dr. Alexander Borek • Head of Smart Data

Data & Analytics Unit (DNA)Use Cases Data Analytics & Success Story Poland

Alexander Borek, Michael Engel and Simon SchreyerGlobal CDO Meeting, Tel Aviv, 23-24.October 2018

DNAData & Analytics Unit

2Data & Analytics Unit (DNA)

Data & AI Platform ManagementEnsure data access & provisioning and provide data analytics

standard toolsScope:

Implement Standard Tools and Licenses similar to DU infrastructure or from the VW Group IT Cloud

Establish a vison for a VWFS Data pool, especially the integration of vehicle data

Data & AI Product DeliveryShow success of data analytics use-cases and projects in different

marketsScope:

Identify Data Analytics Topics concerning their potential, data availability and market’s willingness to implement

Prototype, measure and execute Data Analytics Use-Cases

AWS Data Storage

AWS Data Storage

On-Premise Data Storage

On-Premise Data Storage

Data Ingestion from Data Sources and Legal Basis for Data Usage

Data Access Control and Provisioning to Users and Systems

Analytics & AI Tools

Data Ops & APIs

Data Quality Assurance

Data Quality Assurance

Frontends & Dashboards

Data Asset Catalog

Data Asset Catalog

IdentifyIdentifyUse CaseUse Case IngestIngest LabelLabel CheckCheck ControlControl IdeateSafari Validate Develop Pilot Operate

Data Analytics Training

Data Analytics Training

Data Engineering

Data Engineering

Data Product Management

Data Product Management

Use Case

Ideation

Use Case

Ideation

Machine Learning

Machine Learning

UX Design

UX Design

Software Integration and Testing

Software Integration and Testing

Data OpsData Ops

The DNA focuses on delivery of Data and AI products and creating the AI platform

DEOP TEAMData Engineering & Ops

3

PRODX TEAMProduct Management & UX

DEOP TEAM LEAD Markus Lemke• BI Program-Manager,

VWFS Digital Solutions• 20 years data expertise

DSAI TEAMData Science & AI

HEAD OF DATA & ANALYTICS UNITDr. Alexander Borek• Head of Smart Data & Analytics, VW Group Digitalization• Consultant at Gartner and IBM, Keynote Speaker and Author

Ulf Lange• Platform & architecture• Telematics Data Unit• FSAG IT developer

Dr. Uwe Bretschneider• Promotion Social Media

Analytics at Universität Halle-Wittenberg

Dr. David Wiese• Data Scientist at

Lufthansa• Promotion database

Dr. Chiara Bianchin• Trainee• Researcher at Wayne

State, Utrecht & CERN

DSAI TEAM LEAD Marius Försch• AI Specialist• Teamlead in Berlin at

Alexander Thamm

CHIEF DATA SCIENTISTDr. Alexei Volk• Senior Data Scientist

at Deloitte• Developer at Talanx

Lukas Hestermeyer• Data Scientist at TUI• MSc Uni Osnabrück in

Cogntive Sciences

Gunnar Behrens• Trainee• MSc TU BS in

mathematics

PRODX TEAM LEAD Michael Engel• Data Strategy at Deloitte

Innovation expert

Dr. Fabian Lang• Strategy consultant at

Talanx and zeb• Quantitative Promotion

Eric Wichmann• Trainee• MSc Imperial

College in statistics

Jan Stening• Master thesis DNA

Used Car Analytics• Uni Osnabrück

Christoph Graczyk• Working student• Mathematics, TU

BS

Jan Rautmann• CRM Digital, VWFS• BICC, VWFS• Riskmanagement

Dr. André Hintsches• Gebrauchtwagencenter• Promotion production

and logistics, TU BS

Data & Analytics Unit (DNA)

DATA BOARD & STRATEGYTheresa Klein• Data strategy with MHP• Digital retail strategy

USER EXPERIENCE LEADTitta Jylkäs• UX for AI assistants &

Mobility at VW AG

TRAINEE PROGRAM

PhD

WORKING STUDENTS

GW

CC

RM

VDD-IP (IT)

IH-IB (IT)

IH-IAFV (IT)

Data & Analytics Unit (DNA)Team, Roles and Org Structure

Our Product Management and UX Team is end to end responsible for Data and AI Products from Ideate to Operate Phase


PRODX TEAMProduct Management & UX

PRODX TEAM LEAD Michael Engel• Data Strategy at Deloitte

Innovation expert

Dr. Fabian Lang• Strategy consultant at

Talanx and zeb• Quantitative Promotion

Jan Rautmann• CRM Digital, VWFS• BICC, VWFS• Riskmanagement

Dr. André Hintsches• Gebrauchtwagencenter• Promotion production

and logistics, TU BS

DATA STRATEGY & OFFICETheresa Klein• Data strategy with MHP• Digital Retailstrategy

USER EXPERIENCE LEADTitta Jylkäs• UX for AI assistants &

Mobility at VW AG

Ph

DG

WC

CR

M

FROM IDEA TO PROTOTYPE

• Inspire with talks and presentation

• Ideate in collaborative workshops

• Define data product requirements

• Assess potential business impact and feasibility

FROM PROTOTYPE TO OPERATED DATA PRODUCT

• Managing stakeholders and relationships

• Align with legal and data security departments

• Coordinate the product team and monitor their progress

• Communicate the status to product owners

Our Data Science & AI Team develop high quality Data & AI products using machine learning and data science techniques









VDD-IP (IT)

IH-IB (IT)

IH-IAFV (IT)

DSAI TEAMData Science & AI

Dr. Chiara Bianchin• Trainee• Researcher at Wayne

State, Utrecht & CERN

DSAI TEAM LEAD Marius Försch• AI Specialist• Teamlead in Berlin at

Alexander Thamm

CHIEF DATA SCIENTISTDr. Alexei Volk• Senior Data Scientist

at Deloitte• Developer at Talanx

Lukas Hestermeyer• Data Scientist at TUI• MSc Uni Osnabrück in

Cogntive Sciences

Gunnar Behrens• Trainee• MSc TU BS in

mathematics

Eric Wichmann• Trainee• MSc Imperial

College in statistics

Jan Stening• Master thesis DNA

Used Car Analytics• Uni Osnabrück

Christoph Graczyk• Working student• Mathematics, TU

BS

TRAINEE PROGRAM

WORKING STUDENTS

BUILDING HIGH QUALITY DATA AND AI PRODUCTS

• Validate use case ideas in terms of feasibility

• Prototype use cases ideas for application in semi-productive environments

• Design and implement models that solve business problems

• Utilizing state-of-the-art machine learning and AI techniques to convert data into data products

• Ensure that models are applicable in productive environments

Our Data Engineering & Operations Team deploy, integrate and operate our Data and AI products and develop and provide our Data & AI platform


DEOP TEAMData Engineering & Ops








VDD-IP (IT)

IH-IB (IT)

IH-IAFV (IT)

BRINGING SOFTWARE ENGINEERING AND IT ARCHITECTURE TO DATA SCIENCE & AI

• Deploy and operationalize data products as secure, compliant, maintainable and scalable applications

• Build CI/CD pipelines that allow for high degrees of automation

• Integrate data & AI products into existing IT systems

• Connect data products to all kinds of internal and external data sources

• Create and maintain data pipeline architectures

• Operate analytics platform and cloud infrastructure that assist data scientists and data engineers in building high-quality data products

• Ensuring security and compliance

DNA implements and operates use cases in six phases with an initial focus on rapid prototyping and the development of scalable Data and AI products


IdeateIdeate ValidateValidate

Ideation to define and prioritize use cases

Validation of use cases with productive data

Development of Minimal Viable Product (MVP)

Piloting of MVP and further development to final data product

Maintenance and operation of data product and roll out to other markets

3 months 3 months 6 months 12 months x years

• Data science safari• Use case definition• Business case• Data provisioning• Data understanding• Data suitability

• Model prototype• Model evaluation• User testing • Legal, ethics and

compliance report• Plan for MVP

• Developed MVP ready for semi-productive pilot

• Integration of MVP into backends and frontends prepared

• Go Live MVP• Operation of MVP• Further feature

development• Final product testing

• Operated and maintained final data product

• Scale to other markets

Turning an idea into a data prototype From prototype to operated data product

Only validated use cases with high business value

PilotPilot OperateOperateDevelopDevelop

Fast validation of use case potential

The first two phases of development will usually executed in the Public Cloud (AWS) – afterwards two decision points are defined


Phases 1 to 3Decision Point 1 for MVP Development (before Phase 4)

Decision Point 2 for Productive Operation(before Phase 5)

Validation in most cases in AWS Cloud with productive data

Advantage of flexibility, agility, scalability and the possibility to experiment

FSAG Amazon AWS CloudFSAG Amazon AWS Cloud

and / or and / or

On-Premise On-Premise

IdeateIdeate ValidateValidate PilotPilot OperateOperateDevelopDevelop

PARKING Predictive Parking (Mobility Unit)*

OPEX

Used Car Pricing Switch (GWC)*

Leasingnehmereinheiten OPEX (GER)*

Service Factory Approval Autom. (ESP)

RISK

RV Risk Process Automation (POL)*

Residual Value Forecast (GER)

MAN FS Retail Portfolio (ESP)

Voluntary Termination Score Card (UK)

USED CAR

Identification Homogen. Fleets (GWC)*

Leasing Images (GWC)*

Optimal Car Configurations (ESP)

CONNECTIVITY & FUNC. ON DEMAND

IVEDA Car Data Interpretation (GWC)

Fleet TCO+ Advisory (ESP)

Telematics Analytics (GER)

PRICING

Dynamic Pricing Perfect Car (GER)*

Engine Sales Analysis (GWC)*

Downpricing Deduction (GWC)*

Pilot Risk Based Pricing (GER)

Customized Pricing Maintenance (ESP)

LOYALTY & SALES

B2C Renewal (ESP)*

Campaign Optimization (UK)

The use cases of DNA are clustered into four domains


SALES & MARKETING

MOBILITY & NEW

BUSINESS

VEHICLES & CONNECTIVITY

SMART OPERATIONS

OVERVIEW USE CASES *: see fol. slides

Ideate

Validate

Develop

Pilot

Operate

as of October 2018

Automating the process to estimate residual value, enabling on-time delivery to Controlling – Go-live achieved in September

Risk Management department is responsible for estimating the residual value for provision forecast

Process to obtain residual value is very complex and includes a lot of manual work, making the process very vulnerable

Controlling requests cannot be met in time

Potential quick win

USE CASE: RISK PROCESS POLAND Pilot | Poland | Smart Operations

Developing a user-friendly dashboard that allows Risk Management Team to upload relevant data

App automatically joins data and delivers estimated residual value to Risk Management Team

On time delivery of regulatory required information

Higher process and output quality

Time savings of 10 hours per month

Team can focus on Data Quality Management, which further improves the overall reliability

BACKGROUND

SOLUTION BUSINESS VALUE


First IdeaJune 2018

Product DefiinitionJuly 2018

Kick-OffAugust 2018

Development Aug- Sept 2018

Go LiveSeptember 2018

TODAYAutomation Go Live

December 2018

TIMELINE

The residual value forecast process of the Polish market used to be very lengthy and complex, resulting in delays for controlling requests


11 HOURS EFFORT

CONTROLLING

RISK MANAGEMENT TEAM

FORECAST PROVIDER

EoM DATA BASE

RISK MANAGEMENT ACCESS DATA BASE

DWH

x

PROVISON ESTIMATION

RESIDUAL VALUE

> 6 HOURS

MISSED DEADLINE FROM

CONTROLLING(After 6 hours)

Previous process to estimate residual value for provision forecast

DEADLINE FROM CONTROLLING

11

By using data analytics and a simple dashboard solution, the estimation process is now simplified and significantly accelerated


< 1 HOUR EFFORT

CONTROLLING


FORECAST PROVIDER

EoM DATA BASE


DWH

✓

PROVISON ESTIMATION

RESIDUAL VALUE

RISK MANAGEMENT APP

Upload of CSV file from DWH to app

Risk Management checks data quality

of CSV file

Residual value estimation is

calculated within seconds

Cancel external provider

Time savings >10 hours


New process to estimate residual value for provision forecast

ON TIME DELIVERY TO CONTROLLING

13

Screenshot of Solution App


As a next step we will completely automate the process with regular data uploads directly from the data warehouse to increase reliability


< 1 HOUR EFFORT

CONTROLLING


FORECAST PROVIDER

EoM DATA BASE


DWH

✓

PROVISON ESTIMATION

RESIDUAL VALUE

RISK MANAGEMENT APP

Upload of CSV file from DWH to app

Risk Management checks data quality

of CSV file

Residual value estimation is

calculated within seconds

Cancel external provider

Time savings >10 hours


New process to estimate residual value for provision forecast

ON TIME DELIVERY TO CONTROLLING

AUTOMATED UPLOAD

Dynamic Pricing develops an individual, data-driven pricing solution for used car warranties – pilot run will start end of November

15

Warranty extension for used cars (PerfectCar)

Very low closing rates (out of ~ 10.000 monthlyapproached customers just ~ 500 conclude a contract)

Prices are determined based on car type and age – independent of individual willingness to pay

DYNAMIC PRICING PERFECT CAR Develop | Germany & Holding | Sales & Marketing

Personalized, data-driven pricing

Development of a predictive model for the closing probability of each customer and – based on this model – optimization of pricing

Consideration of additional risk factors (e. g., engine power) and integration of further data sources that are indicative for the willingness to pay

Indicative estimation – pilot run for validation

BACKGROUND


Product DefinitionApril 2018

KickoffMay 2018

PrototypeJuly 2018

Decision for Pilot RunAugust 2018

TODAYGo-Live Pilot Run

November 18

Expected increase deals concluded

Expected gain average margin

Expected additional profit p. a.

50%

2%

300TEUR

End of Pilot RunAugust 2019

TIMELINE


Developing a data-driven downpricing strategy to optimize used car sales process – Go-live in December 2018

16

During the remarketing process, the cars are being offered in online auctions.

If the car has not been sold after a certain number of offer rounds, the target price is reduced by 10%.

This downpricing can be done twice by 10% each time.

The 10% are the same for all cars.

DOWNPRICING DEDUCTION Validate | GWC | Vehicles & Connectivity

Analyzing if the 10% value fits in all cases.

Based on the historical sales data, define the optimal downpricing deduction, if necessary differentiated by brand, model or age.

Possible extension of use case: algorithm to automatically adjust deduction values if necessary.

After first year:Assuming that we reduce successfully the downpricing deduction by 1% for 10,000 vehicles: 10,000 x 1% x 12 TEUR = 1.2 MEUR p. a.

After three years:Considering a volume increase from 100,000 to 140,000 vehicles: 1.2 MEUR x 140% = 1.68 MEUR p. a.

BACKGROUND


First IdeaAugust 2018

Product DefinitionSeptember 2018

Kick-OffOctober 2018

ResultsNovember 2018

Go-LiveDecember 2018

TODAY

TIMELINE


First IdeaJuly 2018

Quantifying the effect of engine types on the used car sales price

17

Pricing a used car is a complex task and needs to take many parameters into account

Manually isolating and quantifying the effect of a single parameter like engine type is not feasible

Key question: What is the effect of an engine type (2.0 TDI 110kw BMT) on the sales price (taking all other effects e.g. mileage, damage, age, equipment aside)?

ENGINE SALES ANALYSIS Develop | GWC | Vehicles & Connectivity

Regression analysis using the sales data of 3 years

Result is a list of residual value percentages which quantify the effects of engine types

Values will be manually updated in remarketing system

Less manual efforts by pricing managers

More precise standard prices

Assuming 5% of volume achieves an additional 0,5% of RV (150 EUR) leads to additional

After first year: 750 TEUR p. a.

After three years: 1 MEUR p. a.

BACKGROUND


Product DefinitionAugust 2018

Kick-OffSeptember 2018

ResultsOctober 2018

TODAY PresentationNovember 2018

TIMELINECorrections

December 2018


Identifying the customers‘ needs to make an adequate offer at the best time to retain the customer with a renewed contract

18

The TOP KPI Loyalty should be increased

Focus on NEXT and CPC product

Spanish customers are not used to renew their car frequently

Most of the portfolio is still amortization credit (~80%)

Idea to combine three different topics: Customer, Car and the respective Offer

B2C RENEWAL Ideate | Spain | Sales & Marketing

Development of a customer score to express thewillingness of a customer to renew the contract

Development of a vehicle score to determine the best point in time comparing residual value against outstanding credit amount (equity calculation)

Development of a recommender system for the best fitting product for the customer

Combination of all three models for individual campaigns

BACKGROUND


Data Science Safari June 2018


Kick-Off September 2018

Validation PhaseDecember 2018

TODAYTIMELINE

Increase ofrenewal rate

(from 8% to 10%)

Expected additional profit p. a.

2% 1.7MEUR


Partial automation of the lease return process by using automatic damage classification and evaluation

19

Automate damage assessment process during return process of lease cars

Considering ~100,000 appraisals being ordered by GWC in 2018 gives a rough estimate of the business value of this use case

LEASING IMAGES (UNI HI) Validate | GWC | Vehicles & Connectivity

Use Case is divided into three parts:

1. Damage Classification: Usage of deep learning model

2. Repair Action Classification: Usage of deep learning model

3. Repair Costs: Regression model

First results are better than results of IBM Watson / TÜV SÜD

Two scenarios possible:

1. Software for partial automation of appraisal process

2. App for end customer for own damage estimation

BACKGROUND


Start of UCApril 18

Involvement DnAJuly 18

First ResultsAugust 18

Watson benchmark exceededOctober 18

Results of bounding boxesNovember 18

TODAY Decision about PrototypeJanuary 19

TIMELINE


Enabling the business unit to automatically identify clusters of homogeneous vehicles – Go-live end of October

20Abteilungskürzel | Autor/-in | TT. Monat JJJJ | Kurztitel

GWC remarkets approx. 100,000 vehicles/year.

High amounts of identical vehicles in a short period lead to sales problems and significant loss in prices.

Example: Unexpected return of fleet of young ADAC VW Touran in 2008 led to a drop in prices of 5% of list price!

IDENTIFICATION HOMOGENEOUS FLEETS Develop | GWC | Vehicles & Connectivity

Creating a cloud-based tool to identify risk clusters

Customer uploads a list of expected lease returns, defines the critical amounts and tool automatically identifies risk clusters.

Common remarketing problem. Therefore, tool could be of interest to other remarketing departments.

After first year (rollout DE):240 TEUR p. a.

After three years (rollout EU5): 840 TEUR p. a.

BACKGROUND


First IdeaJune 2018

Product DefiinitionJuly 2018

Kick-OffAugust 2018

1st PrototypeSeptember 2018

Start of TestOctober 2018

TODAYGo-Live

November 2018

TIMELINE

Implementing a self-learning switch deciding between automatic and manual price decisions – Go-Live in mid 2019

21

The GWC pricing department prices all cars manually (100,000 cars in 2018)

The car is being priced by the system with a standard price and pricing managers adjust these prices if necessary

About 20% of standard prices are adjusted manually, 80% are priced according to standard

SWITCH FOR PRICING AUTOMATION Develop | GWC | Vehicles & Connectivity

Implementing a self-learning switch to automatically

identify vehicles which prices need to be adjusted

Switch compares cars with the recent sales results of similar cars and decides if adjustment is necessary

Additional feature: Within a given range, the switch can make smaller price adjustments itself

After first year:~2-3 FTE which don‘t need to be created

After three years:~5 FTE which don‘t need to be created

BACKGROUND


First IdeaJune 2018


Data CollectionSeptember 2018

1st resultsOctober 2018

End of ValidationDecember 2018

TODAY Go-LiveSpring 19

TIMELINE


For the leasing taker units, DNA builds a data product that collects and aggregates data from different data sources that are used manually so far


Solvency unit needs to track their credit/leasing taker units (Kreditnehmereinheiten bzw. Leasing-nehmereinheiten; KNE/LNE)

Very high manual efforts to collect data from different commercial register websites and credit agencies

LEASINGNEHMEREINHEITEN Validate | Germany & Holding | Smart Operations

The data product automates manual searches andcollects data from different websites

After collection, the product aggregated the relevant information in a structured form and filters the information

In the long run, the solution could mostly automate the routine tasks, or at least, increase the employees’ productivity to a very high degree

Currently: 45 FTE working with 30-50% of their capacity on LNE

After first year:Reduction of work load by 10%: Potential of ~2 FTE

After three years:Reduction of work load by 50%: Potential of ~7-11 FTE

BACKGROUND


First IdeaJune 2018

Product DefinitionJuly 2018

Kick-OffJuly 2018


TODAYGo-Live First Support Tool

Spring 2019

TIMELINE

DNA develops a customized credit risk scorecard for the retail segment of MAN Trucks & Busses in the Spanish market – go live is planned for Q2 2019


Currently, MAN Spain uses a risk scorecard in theirwhich was developed for Volkswagen CommercialVehicles.

As the MAN busses & trucks retial business is slightly different resulting in a relatively high default rate of ~8%.

MAN could benefit from a scoring model tailored to the specific segment it is employed in.

MAN RETAIL RISK SCORECARD Ideate | Spain | Smart Operations

DNA develops a scoring model based on historicaldata of the MAN retail trucks & busses business

During model will use state of the art

In the long run, the solution could mostly automate the routine tasks, or at least, increase the employees’ productivity to a very high degree

Currently: 45 FTE working with 30-50% of their capacity on LNE

After first year:Reduction of work load by 10%: Potential of ~2 FTE

After three years:Reduction of work load by 50%: Potential of ~7-11 FTE

BACKGROUND


First IdeaJune 2018

Product DefinitionJuly 2018

Kick-OffJuly 2018


TODAYGo-Live First Support Tool

Spring 2019

TIMELINE

Prediction of free on-street parking spaces based on analysis of parking data from Travipay and PaybyPhone

24

Parking services face a lot of competition on the market and predictive models are becoming more a necessity to keep up than a differentiator from competitors

The smart parking companies Sunhill Technologies (Travipay) and PaybyPhone do not offer parking availability yet and have a high amount of so far unused historical data for parking transactions

PREDICTIVE PARKING Validate | Mobility Unit | Mobility & New Business

We are working on a predictive parking function for on-street parking spaces based on the periodical transactional data from PaybyPhone and Travipay

The development of the predictive model is done by the University of Hildesheim and the prediction focusses on the time frame of 5 min

In order to test the accuracy of the model a MVP will be developed until eoy 2018 for five german cities

Predictive parking allows the parking companies Sunhill Technologies and PaybyPhone to increase the coverage, number of users and transactions

Access to more cities and avoid the risk of losing tenders

Opportunity to reach a stronger and more copetitive position in the market by offering the predictive parking function

BACKGROUND


Data Science SafariMay 2018

Follow UpJune 2018

Prediction ModelAugust 2018

MVP Planning September 2018

TODAYGo-Live Pilot

December 2018 (TBC)

TIMELINEUX ApproachOctober 2018


AI Products focus on development of machine learning algorithms while Data Products focus on the agile integration and transformation of data

•Dynamic Pricing Perfect Car (Germany)•Customized Pricing Maintenance (Spain)•Renewal B2C (Spain)•Fleet TCO+ Advisory (Spain)•Campaign Optimization (UK)•Used Car Pricing Automation Switch (GWC)•Optimal Car Configurations (Spain)•Residual Value Forecast (Germany)•Engine Sales Analysis (GWC)•MAN FS Retail Portfolio (Spain)•Voluntary Termination Score Card (UK)•Predictive Parking (Mobility Unit, Uni HI)•Downpricing Deductions (GWC)•Leasing Images (GWC)

AI

Products

Identification Homogeneous Fleets (GWC)• IVEDA Car Data Interpretation (GWC)•Service Factory Approval Automation (Spain)•RV Risk Process Automation (Poland)•Leasingnehmereinheiten OPEX (Germany)•Risk Based Pricing (Germany)Data Products


Focus on Data Engineering

Focus on Machine Learning

Just as a reminder: The Data Science Safari is a 3 hour workshop to start the Ideation in your market


1. Safari Bootcamp: Ending the buzzword bingo

3. Taming the lions:Creating value from data

2. Watching big elephants:

Use case examples

5. Finding goldPrioritizing your use cases

4. Discovering the Zebras:Identifying use cases in

your department


Feel free to contact us, if you have any questions!

Michael EngelTeam Lead Product Management & UX

[email protected]+49 152 54502242

Dr. Alexander BorekHead of Data & Analytics Unit

[email protected]+49 178 1433854

DNAData & Analytics Unit

mailto:[email protected]

mailto:[email protected]

Documents

Data & Analytics Unit (DNA) · VWFS Digital Solutions • 20 years data expertise DSAI TEAM Data Science & AI HEAD OF DATA & ANALYTICS UNIT Dr. Alexander Borek • Head of Smart Data