The document discusses how data analytics and artificial intelligence are transforming businesses in the era of digital transformation. It covers the history and evolution of AI from early neural networks to today's deep learning approaches enabled by massive increases in data and computing power. Examples are given of how AI is now exceeding or matching human-level performance in areas like image recognition, medical diagnosis, and speech recognition. The document advocates that businesses leverage AI, data science, and a 360-degree view of customer data to drive personalization, predict customer needs, optimize operations, and gain competitive advantages in their industries.

1. Data Analytics and Artificial Intelligence
in the era of Digital Transformation

2. WE ARE LIVING IN A DIFFERENT ERA
Web First à Mobile First à AI First

4. Google TPU

5. AI history à Perceptron
1958 F. Rosenblatt,
“Perceptron” model,
neuronal networks
1943 W. McCulloch,
W. Pitts, “Neuron” as
logical element
OR function XOR function
1969 M. Minsky,
S. Papert, triggers
first AI winter
feed forward

6. AI history à AI winter
1958 F. Rosenblatt,
Perzeptron model,
neuronal networks
1987-1993 the second
AI winter, desktop
computer, LISP
machines expensive
1943 W. McCulloch,
W. Pitts, neuron as
logical element
1980 Boom expert
systems, Q&A using
logical rules, Prolog
1969 M. Minsky,
S. Papert, trigger
first AI winter
1993-2001
Moore’s law, Deep
blue chess-
playing, Standford
DARPA challenge

7. AI history
Accuracy
Scale (data size, model size)
other approaches
neural networks
1990s
https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI

8. More Data + Bigger Models + More Computation
Accuracy
Scale (data size, model size)
other approaches
neural networks
Now
https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI
more compute

9. Human
Intelligence
Artificial
Intelligence
Average
Intelligent
Sub human
Par human
High human
Super humanPerformance
AI comparison with human performance
Borderline
performs better than all humans

10. 10
AI beats human in games - 2016
Komodo beasts H. Nakamura in 2016AlphaGo beats L. Sedols in 2016
Go 4:1 Chess 2:1

11. Image Classification- 2016
Human Performance AI Performance
https://arxiv.org/pdf/1602.07261.pdf
95% 97%
The ability to understand the content of an image by using machine learning

12. Breast Cancer Diagnoses - 2017
Pathologist Performance AI Performance
https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
73% 92%
Doctors often use additional tests to find or diagnose breast cancer
The pathologist ended up
spending 30 hours on this
task on 130 slides
A closeup of a lymph node biopsy.

13. Face Recognition - 2016
Human Performance AI Performance
https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
https://arxiv.org/pdf/1603.01249v2.pdf
97,5% 97,7%
The ability of a computer to scan, store, and recognize human faces for use in identifying people

14. Speech recognition - 2016
Methodologies and technologies that enables the recognition and translation of spoken language
into text by computers
https://arxiv.org/pdf/1610.05256v1.pdf
Human Performance AI Performance
41,3% 57,9%

15. Traditional Programming Machine Learning

16. 16
Machine Learning Problem Types

17. More Data + Bigger Models + More Computation
= Better Results in Machine Learning

18. Millions of “trip”
events each day globally
400+ billion viewing-
related events per day
Five billion data points
for Price Tip feature
Movie
recommendation
Price
optimization
Routing and price
optimization

19. Rethink the Status quo and embrace
Data, AI and Technology

20. Who are you?
Who do you know?
What can you afford?
Where are you?
What have you purchased?
What do you like?
What content do you prefer?
Why have you contacted us?

21. Marketing
Tools
Touchpoints Aftersales
Capture Data across Digital Channels
Each of these customer interactions produces data

22. What are the key challenges?
q Data silos – Data spread across a number of silos
q Data volumes / growth – High rate of data growth
q New / unstructured data sources
q Cost of data storage & processing

23. Breaking Down Data Silos
Connect all your data tools,
other sources, and gain a 360
degree view on your data
Get actionable insights and
serve them personal, relevant
content along their journey
Real-time processing and
decision making
One
Data Platform
Marketing
Tools
Touchpoints
Historical
Aftersales
Data Analytics
Machine Learning
Data Apps

24. Fishing in the sea versus fishing in the lake
Data Warehouse Data Lake
Business Intellingence helps find
answers to questions you know.
Data Science helps you find the
question itself.
Any kind of data & schema-on-readStructured data & schema-on-write
Parallel processing on big dataSQL-ish queries on database tables
Extract, Transform, Load Extract, Load, Transform-on-the-fly
Low cost on commodity hardwareExpensive for large data

25. thank youData
Analytics
Speed
Data
Complexity
Analytics
Complexity
Data Size
Accuracy /
Precision

26. Where Analytics can help…
+ Predicting lifetime value
+ Churn estimation
+ Customer segmentation
+ Cross/Upselling
+ Recommendations
+ Demand forecasting
+ Market Basket Analysis
+ Sentiment analysis
+ Loyalty programs
+ Reactivation likelihood
+ Discount targeting
+ Call to action
+ Risk analysis
+ In store traffic patterns

27. Actionable data insights that
businesses can use to...
ü Better understand and better
engage your customers
ü Respond to the convergence
of customer expectations
ü Driver of brand perception
360 degree view of your customers
Social
Apps
CRM
Billing
Channels
Service Call
Location
Devices
Network
Ordering
Customer 360

28. GDPR
General Data Protection Regulation

29. GDPR
General Data Protection Regulation
+ Regulation on the protection of natural persons with regard to
the processing of personal data and on the free movement of
such data, and repealing Directive 95/46/EC (General Data
Protection Regulation)
+ 25 MAY 2018: ALL THE CONCERNED COMPANIES MUST BE COMPLIANT
29
GDPR (General Data Protection Regulation) also knows about "sensitive personal data"
which is defined as information revealing racial or ethnic origin, political opinions, religious
or philosophical beliefs, trade-union membership, and data concerning health or sex life.
This information is supplied without liability and without any claim to comprehensiveness.

30. Processing
any operation or set of operations which is performed on personal data or on sets of personal data, whether or
not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or
alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making
available, alignment or combination, restriction, erasure or destruction
Personal Data
Any information relating to an identified or identifiable natural person (name, identification number, location
data, online identifier, one or more factors specific to physical, physiological, genetic, mental, economic,
cultural or social identity of that natural person (Data Subject)
It includes IP address; PD that has been pseudonymised – eg key-coded- can fall within the scope of the
GDPR depending on how difficult it is to attribute the pseudonym to a particular individual
Sensitive personal data
Racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic
data, biometric data processed to uniquely identify an individual, data concerning health, or data concerning a
natural person’s sex life or sexual orientation cannot be processed except if expressly authorized.
Material Scope
30This information is supplied without liability and without any claim to comprehensiveness.

31. Examples of Personal Data
+ Name
+ Home Address
+ Photo
+ Date/Place of Birth
+ Age, Gender
+ Race/Ethnic origin
+ EMail Addresses
+ Phone numbers
+ Any form of ID-numbers assigned to
individuals
+ Passport Number & National Identification
Card Number
31
+ Credit Card Number
+ Health Status
+ Criminal Record
+ (Vehicle) locations and history thereof
+ CCTV and video recordings
+ IP Addresses, Mobile Device IDs, MAC
Addresses
+ Genetic data, Health status (including
pregnancy), Biometric data
+ Religion / Philosophical beliefs
+ Sexual preferences
This information is supplied without liability and without any claim to comprehensiveness.

32. How to start?

33. “Culture eats strategy for breakfast,
technology for lunch, and products for dinner,
and soon thereafter everything else too.”
Peter Drucker

34. + Classification, Regression, Clustering,
Collaborative Filtering, Anomaly Detection
+ Supervised/Unsupervised Reinforcement
Learning, Deep Learning, CNN
+ Model Training, Evaluation, Testing,
Simulation, Inference
+ Big Data Strategy, Consulting, Data
Lab, Data Science as a Service
+ Data Collection, Cleaning, Analyzing,
Modeling, Validation, Visualization
+ Business Case Validation,
Prototyping, MVPs, Dashboards
Data Science Machine Learning

35. + Architecture, DevOps, Cloud Building
+ App. Management Hadoop Ecosystem
+ Managed Infrastructure Services
+ Compute, Network, Storage, Firewall,
Loadbalancer, DDoS, Protection
+ Continuous Integration and Deployment
+ Data Pipelines (Acquisition,
Ingestion, Analytics, Visualization)
+ Distributed Data Architectures
+ Data Processing Backend
+ Hadoop Ecosystem
+ Test Automation and Testing
Data Engineering Data Operations

36. Think Big Business Strategy
Data Strategy
Technology Strategy
Agile Delivery Model
Business Case Validation
Prototypes, MVPs
Data Exploration
Data AcquisitionStart Small
Value
Proposition

37. thank you