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Data & Analytics at Scale -
Building an Immersive Data Function in Large Organizations
Walid Mehanna, Daimler AG
New York, 2017-11-30

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2Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30

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Data & Analytics: Ultimately it‘s about creating value
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Relevance and drivers of Data & Analytics
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
• Vastly more insightful
• Discover new patterns at
extreme granularity
• Predictive, auto-adaptive;
machine learning
• Interoperable & scalable
• Storage and processing
cost and speed advantages
• Designed for analysis
• Explosive growth
• Multi-structure
• Multi-source
Advanced
algorithms
New
Technologies
Value
Creation
Massive (new) data
Source: BCG

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Data & Analytics

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Why a Data & Analytics Workbench
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
Effort between Data & Analytics is unevenly distributed – for now
Data
Analytics
98%
2%
Us
today
Data
Analytics
50%
Our ambition:
Us tomorrow
50%
Data
Analytics 20%
80%
In general
today

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We deliver the foundation that all relevant company processes are
enabled to harness the potential of data and analytics
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Overview, Vision, Mission
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
Delivering methods,
systems and processes
to ensure the availability
of high quality data for
analytics.
DATA
ENABLER
Developing, deploying
and operating the
necessary business
platforms for
best-in-class data and
analytics solutions.
PLATFORM
PROVIDERS
Identifying, building and
operating best-in-class
analytical applications.
ANALYTICS
CONSULTANTS
Coaching and training FM
to generate and use
quality data and
analytical applications.
Creating and fostering
a data culture within
FM and MBC overall.
DATA
EVANGELISTS
COACHES AND
TRAINERS
WHO WE ARE

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Data & Analytics MBC implements analytical applications end-to-end from
idea to changed business processes or even to new business models
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Services: Provide Analytic solutions
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
Ideation Evaluation Proof of Concept Professionalization Roll out Operation
Departure
As-Is
Business
Arrival
New business
processes/
models
Decision
Go/No-go
Decision
Go/No-go
Decision
Go/No-go
Solution
in place
Minimum
viable product
General
availability
Continuous
improvement

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Our business model is integrated and build along three foundations:
Framework, Core Value Chain and Platforms
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Business Model
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
Identify
Use Case
Identify
Data
Integrate
Data
Prepare
Data
Provide
Data
Design and
test models
Use
models
Visualize
Results
Use
Results
Design
solution
Date Governance & Standards;
Strategy & Community Coordination
FRAMEWORK
Identify, Build, Deploy & Operate Analytical Applications
CORE VALUE CHAIN
Technical Architecture & Infrastructure
PLATFORMS
Project Management, User Interaction / Management

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The line organization is structured along our business model,
whereas sprint teams are staffed cross-functionally
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Organization
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
Data & Analytics
(D&A)
Data
Management
Data
Science
Business
Analytics
Visual
Information Design
Analytics
Platforms
Data
Governance
& Standards
Team &
Community
Coordination
Core Value Chain Infrastructure Framework

10.
We focus our activities in three dimensions –
services, platforms and community
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Short Term Focus
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
Providing
analytic solutions
Developing data &
analytic skills
Fostering a data
culture
Data & Analytics Workbench
Data & Analytics Studio
Data & Analytics Marketplace
Internal stakeholder with a
legitimate interest in financial
data and their analysis
Financial business functions
across all divisions
Relevant partners for data
management or analytics
solutions within and outside of
Daimler
Services
Platforms
Community

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Why?

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Summary D&A Workbench evaluation based on four use cases
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POC Findings
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
4
USE CASES
20+
INVOLVED USERS
1000+
TESTING HOURS
Data Loading
Easily integrate common data
sources such as SQL, HDF and S3
via GUI or code.
Create models
Leverage state of the art open
source machine learning
algorithms with a convenient
interface.
Data Analysis
Limited but sufficient data analysis
functionalities via data
visualizations.
Data Preparation
Basic and advanced ETL
functionalities via point and click
and various engines.
Development
Easy transition from design to
development with various
features for automation and
monitoring.
Collaboration
Easy collaboration and
coordination between data
scientist and non-technical users.
“Elaborated online documentation of functions and
capabilities.”
“R and Python were tested and worked out well.”
”The R environment is overall well implemented in
the tool. We have not experienced any technical
boundaries.”
“Easy and convenient advanced ETL functionalities
are available.”
“Limited dashboard capabilities. DS can show basic
charts to business users.”
4/5
OVERALL RATING

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Becoming a Data Scientist in two days? Not really, but our hands-on
training gives valuable impulses and lowers the entry threshold
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30 13
Services: Develop Data & Analytic skills
Data & Analytics hands-on...
• Sound theoretical foundation on
data science and machine learning
• Real world examples
• Hands on work with data on a
modern data science workbench
named „Dataiku“
• Supervision and support by
experienced data scientists
• One full day competition in terms
of two with live leaderboard
Very positive feedback,
100% recommendation rate*
Trainings are ongoing
Location: Stuttgart, Germany
Contact us, if you are interested
*based on a Net Promoter Score of 11 feedback surveys
answered (out of 23 participants = 48% response rate)

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Imagine… working with data on a 240” screen
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Imagine… knowing your
data inside out…

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Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
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… and also your
models!

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We take culture very serious –
„Connected Finance: Building a Data Culture“
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Services: Establish a data culture
Data & Analytics at Scale | EGG 2017, New York City| 2017-11-30
Collect - Ensure collection
of data
DATA
CULTURE
Share – Make data available
and the availability transparent
Access – Fill the data lake and
build APIs, the services behind
them and platforms
Connect – Exchange across
all of Finance
Co-create – Re-use and enhance
data, models and APIs together!
Model – Create (analytical)
models

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Lessons learned (so far)
Be bold, be quick
“De facto” beats “de jure” any day
Build and leverage a diverse internal and external network

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Thank you!