1. The document discusses Teradata Vantage, a data analytics platform. It introduces Patrick Deglon, VP of Advanced Analytics at Teradata, and covers various topics around machine learning, analytics, and data management challenges. 2. An example is provided of an experiment run by eBay to test the effectiveness of marketing campaigns. Results showed increased purchases when marketing was used. 3. Teradata Vantage is positioned as a solution that can help organizations overcome challenges around data management, analytics, and machine learning by providing unified analytics capabilities across different workloads and data types.

1. 1
TERADATA VANTAGE
The Platform for Pervasive Data Intelligence
Rise above the complexity, cost, and inadequacy
of today’s analytics landscape
© 2020 Teradata
Intelligence Data Day
Paris, February 5th 2020

2. 2
Patrick Deglon Bio
After a PhD in Particle Physics and ten years at the University of
Geneva studying the creation of the Universe, Patrick spent the next
decades driving business insights at eBay, Motorola Mobility, and Teradata.
At eBay, he led significant improvements in marketing effectiveness by
developing methods to measure incremental sales, and by running large scale
experiments on Internet marketing channels.
At Google’s Motorola Mobility, he raised the bar in Analytics and on-boarded
open Google tools and technologies.
In Dec 2016, he joined Teradata as the Vice President of Advanced Analytics
driving the strategy, direction, investment and realization of Teradata’s advanced
analytics portfolio, including the Teradata Database, Aster Analytics, and Open
Source Software.
He is married with two kids and moved to San Diego, California in Dec 2016.

3. The Role of Advanced Analytics
in the Modern Enterprise

4. 4
When will computers beat humans at Angry Birds?
© 2020 Teradata

5. 5
When will computers beat humans at Angry Birds?
© 2020 Teradata

6. 6
N = 352 respondents / 1634 contacted
2059
2060
2136
2051
2019
2024
2025
2031
© 2020 Teradata

7. 7
Software 2.0
There used to be 500,000 lines of code in Google
Translate. How many line of code are there now?
500,000 lines
Software 1.0
© 2020 Teradata

8. 8
Software 2.0
Source: https://twimlai.com/twiml-talk-124-systems-software-machine-learning-scale-jeff-dean/
There used to be 500,000 lines of code in Google
Translate. How many line of code are there now?
500,000 lines
Software 1.0 Software 2.0
500 lines
© 2020 Teradata

9. 9
• Financial Forecast?
• Marketing campaign targeting?
• Asset Management?
• HR recruitment?
• Software Development?
• M&A Decision?
• Corporate Strategy?
When will Machine Learning Systems surpass humans with:
© 2020 Teradata

10. 10 © 2020 Teradata
UNIFIED DATA
WAREHOUSE
IS A FOUNDATION
FOR ARTIFICIAL
INTELLIGENCE
“
“
Andrew Ng
Leading AI Researcher
© 2020 Teradata

11. 11
Teradata Vantage is Uniquely Positioned for
Machine Learning Systems
Configuration
Serving
Infrastructure
MonitoringML
Code
Process
Management
Tools
Analysis Tools
Data
Collection
Data
Verification
Feature
Extraction
Machine
Resource
Management
Source: https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
© 2020 Teradata

12. 12
The Role of Machine Learning Systems in the Enterprise
DATA INSIGHTS
ANALYTICS
EXECUTION
DECISION
MAKING
Legal
Sales &
Marketing
Finance &
Strategy
Information
Technology
Customer
Supports
Human
Resources
Product
Development
Manufacturing
Operations
© 2020 Teradata

13. 13
Journey in Analytics
Descriptive Analytics
Understand past events
© 2020 Teradata

14. 14
Journey in Analytics
Descriptive Analytics
Understand past events
Predictive Analytics
Identify best option
© 2020 Teradata

15. 15
Journey in Analytics
Prescriptive Analytics
Automate business decision
Descriptive Analytics
Understand past events
Predictive Analytics
Identify best option
© 2020 Teradata

16. 16 © 2020 Teradata
Sales
The “Accidental Ecosystem”
SQL
BI
Data Warehouse
Customers
Inventory
Products

17. 17 © 2020 Teradata
Sales
The “Accidental Ecosystem”
SQL
BISelf-Serve BI
Data Warehouse
Customers
Inventory
Products
Customers
Value
Acxiom
Segmentation
(JSON)
MySQL
Custom
Analysis
Tableau

18. 18 © 2020 Teradata
Sales
The “Accidental Ecosystem”
SQL
BISelf-Serve BI
Data Warehouse
Advanced Analytics
Customers
Inventory
Products
Customers
Value
Acxiom
Segmentation
(JSON)
MySQL
Custom
Analysis
Clicks
(HDFS)
R Server
Attribution
Model
Sales
Tableau R

19. 19 © 2020 Teradata
Sales
The “Accidental Ecosystem”
SQL
BISelf-Serve BI
Data Warehouse
Advanced Analytics
Customers
Inventory
Products
Customers
Value
Acxiom
Segmentation
(JSON)
MySQL
Custom
Analysis
Clicks
(HDFS)
R Server
Attribution
Model
Sales
Products
Spark
Sentiment
Analysis
Tableau R Python
Emails
(S3/
Azure Blob)

20. 20 © 2020 Teradata
Sales
The “Accidental Ecosystem”
SQL
BISelf-Serve BI
Data Warehouse
Advanced Analytics
Customers
Inventory
Products
Customers
Value
Acxiom
Segmentation
(JSON)
MySQL
Custom
Analysis
Clicks
(HDFS)
R Server
Attribution
Model
Sales
Products
Spark
Sentiment
Analysis
SAS
Fraud
Prevention
Emails
Tableau R Python SAS
Emails
(S3/
Azure Blob)

21. 21
60%
Number of failed
Big Data projects
90%
Number of useless
data lakes by 2018
6 months
Static data projects
value duration
50%
of data in any project
is an exact repeat of
>5 other projects
80%
Of all projects is spent
preparing data rather
than creating value
5 months
Average to develop, test,
validate, deploy and scale
new analytical models
“Lakes are overwhelmed
with information assets
captured for uncertain
use cases”
“Not establishing
data governance and
management is
underlining value”
“We have institutionalized repetition
and redundancy
because of the way we
manage data”
“We lack discipline in
data management to generate
long term
value”
“Acting like a Fintech
is a lot easier said
than done”
“We keep buying promises,
and are not cynical enough about
the time it take to
realize them”
Endemic Challenges That Must Be Solved

22. 22 © 2020 Teradata
Preferred
Compute &
Functions
Preferred
Storage &
Data Types
Preferred Tools
and Languages
Sales
Vantage: Our Solution to the Analytics Journey Challenges
SQL
BISelf-Serve BI
Data Warehouse
Advanced Analytics
Customers
Inventory
Products
Customers
Value
Acxiom
Segmentation
(JSON)
MySQL
Custom
Analysis
Clicks
(HDFS)
R Server
Attribution
Model
Sales
Products
Spark
Sentiment
Analysis
SAS
Fraud
Prevention
Emails
Tableau R Python SAS
Emails
(S3/
Azure Blob)

23. 23 © 2020 Teradata
Preferred
Compute &
Functions
Preferred
Storage &
Data Types
Preferred Tools
and Languages
Vantage: Our Solution to the Analytics Journey Challenges
API SQL PYTHON R JAVA
ADVANCED SQL ENGINE MACHINE LEARNING ENGINE GRAPH ENGINE
HIGH-SPEED FABRIC
DATA STORE
RELATIONAL AVRO CSV JSON
OBJECT STORE
CSV JSON PARQUET

24. 24 © 2020 Teradata© 2020 Teradata
Vantage within the Connected Ecosystem

25. 25
Teradata Vantage—Built On An Agile Core
TERADATA VANTAGE
Advanced
Indexing
Workload
Management
Adaptive
Optimizer
Query
Performance
Mission-Critical
Availability
Linear
Scalability
© 2020 Teradata

26. 26
Vantage Is Cloud Enabled
Teradata
Infrastructure
Teradata
Cloud
AWS,
Azure &
GCP (2020)
Commodity
Infrastructure
(VMware)
© 2020 Teradata

27. 27
Open Ecosystem Connectivity with QueryGrid™
• Minimize data
movement
and duplication
• Process data
where it resides
• Scalable data
transfer with
push-down
processing
QueryGridHighSpeedFabric
Object
Store
Object
Store
Relational
Deep
Learning
Stats
NewSQL
Machine
Learning
Graph
Custom
Document
Store
Emerging
File Store Deep
Learning
ANALYTIC ENGINESDATA STORES
© 2020 Teradata

28. 28 © 2020 Teradata
Day in the Life of a
Business Analyst

29. 29
1st Example: How much is worth a human life?
1982: New chemical labeling in workplace (cost of labeling vs cost of life)
Occupational Safety and
Health Administration
Yes
Office of Management
and Budget
No
George H.W. Bush
Vice-President
?

30. 30
1st Example: How much is worth a human life?
https://www.nprillinois.org/post/how-value-life-statistically-speaking
Kip Viscusi
1982: New chemical labeling in workplace (cost of labeling vs cost of life)
Occupational Safety and
Health Administration
Yes
Office of Management
and Budget
No
George H.W. Bush
Vice-President
?
• US Worker risk of death: 1 in 25,000
• Dangerous jobs (arctic fishermen, oil rig workers, loggers) have higher risk
• By normalizing 200,000+ job profiles for education and skills, we can estimate that for $1,000 per
year more, worker are willing to take a extra 1 in 10,000 chance of dying on the job
• 10,000 workers = 1 estimated death
• so 10,000 * $1,000 = $10 millions (value of statistical life)
• Yet each life is priceless, especially for the love ones

31. 31
2nd Example: How much should you pay for the
keyword “red dress” on Google?
Google Shopping
(SKU-based, pay per impression, per click,
per sale, or for Return on Ad Spending)
Google Ads
(Keyword-based, pay per click)
Google Search
(Content-based, free)

32. 32
Experimental Design
Test Group
• Switch off Google AdWords
• 30% of USA
Control Group
• Keep Google AdWords
• 30% of USA
• Similar buying pattern/seasonality
than Test Group
US DMA – Designated Market Area
Google AdWords Locations Targetting
eBay Marketing
Experiment

33. 33
eBay Marketing
Experiment

34. 34
eBay Marketing
Experiment

35. 35
eBay Marketing
Experiment

36. 36 Don’t Do Marketing Do Marketing
No Purchase
Purchase

37. 37 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L

38. 38 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D

39. 39 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C

40. 40 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C
?
?

41. 41 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C
?
?
Cost
Direct Return
Incr Return

42. 42 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C
?
?
Cost
Direct Return
Incr Return
Rule #1: Never, ever, spend money
unless you really-really have to

43. 43
Vantage Today (16.20)
DATA STORE
HIGHSPEEDFABRIC
STORAGE ENGINES
Machine
Learning
Graph
SQL
Engine
Machine
Learning
Graph
Advanced
SQL
Engine
Data Transformation
Date/Time/Period/String/Regexp/JS
ON/XML manipulation, ngram, string
similarity, pivot/unpivot, pack/unpack
Ordered Analytical Functions​
LAG, LEAD, Cumulative Sum,
Moving Average, Month over Month,
Year over Year
Custom Analytics​
CASE, User Defined Functions,
Stored Procedures, SCRIPT table
operators, temporary tables, users
database, simulation
Statistics​
Count, Average, Min, Max, Median,
Standard Deviation, Variance,
Covariance, Correlation, Kurtosis,
Skewness, Rank, Percentile,
Quantile, Slope, Intercept,
Advanced Moving Average,
Sampling, Sub-Totals (ROLLUP)
4D Analytics
Geospatial , Temporal , Time series data types
and aggregations
Pattern & Attribution
Sessionize, NPATH​, Attribution
Scoring functions
Single Decision Tree, DecisionForest, Naïve
Bayes Classifier, Naïve Bayes Text Classifier,
Support vector machine (SVM), Generalized
Linear Model (GLM)
=
© 2020 Teradata

44. 44
Frequency Calculation in Excel vs SQL

45. 45
Analytics Dataset Preparation for Sales Forecast
-- Shelves Reorder Using Previous Row Value
inventory_items
- LAG(inventory_items) OVER (
PARTITION BY item_sto_no
ORDER BY sale_date)
+ items_sold AS reorder_cnt
-- Weekly Running Average of Number of Items Sold
AVG(items_sold) OVER (
PARTITION BY item_sto_no
ORDER BY sale_date
ROWS BETWEEN 7 PRECEDING AND 1
PRECEDING) AS items_sold_trailing_7_days
-- Enable Division By Zero As NULL
SUM(item_qty_sale*item_val_sale) /
NULLIFZERO(SUM(item_qty_sale)) AS avg_selling_price
© 2020 Teradata

46. 46
Vantage—the Foundation for Enterprise Scale and Performance
Through In-Database Advanced Analytics
© 2020 Teradata
Traditional Analytics
2
3 4
SQL
LAPTOP1
In-Database Analytics
API
3
4
SQL
1
2
~ GB
~ MB

47. 47
Power of In-Database Analytics
Local R Script In-Database R Script
240 10minutes minutes
© 2020 Teradata
6 HOURS
Download users data
Retail Company
Shipping Company
In-Database R Script
Download
360minutes
~4minutes
Upload
360minutes
Local Churn
~10minutes
Local Python Script In-Database Python Script
2880 18minutes minutes
Manufacturer

48. 48
Power of In-Database Analytics
Local R Script In-Database R Script
240 10minutes minutes
© 2020 Teradata
6 HOURS
Download users data
• Faster results
• Iterate more
often
• Fresher
business
insights
• Fail faster
• Better
governance
(monitor, audit,
backup, …)
Retail Company
Shipping Company
In-Database R Script
Download
360minutes
~4minutes
Upload
360minutes
Local Churn
~10minutes
Local Python Script In-Database Python Script
2880 18minutes minutes
Manufacturer

49. 49
Operational Simplicity
• Only SQL used
• One command to train the model
• One command to score
Verizon Results
GOAL RESULT
Avoid Data Movement / Duplication Met
Initial Accuracy of 64% or better Goal Exceeded: 69.8%
Model Training with >1M records to be <20 min Goal Exceeded: <13min
Model Scoring >200M records to be <30 min
(scoring the entire US customer base)
Goal Exceeded: 22.5min
“I’ve done this for a
long time. I really
haven’t seen this
result ever.”
- Ksenija Draskovic
Operational Results
In less than 40 minutes, they can refresh their
model and score their entire customer base,
with results live in their Teradata system

50. 50 © 2020 Teradata
Day in the Life of a
Citizen Data Scientist

51. 51 © 2020 Teradata
Vantage Analyst: Path for Equipment Failure

52. 52 © 2020 Teradata
Vantage Analyst: Most common Equipment Failure

53. 53
Example of a self-service App
https://insights.transcend-vantage.td.teradata.com/

54. 54 © 2020 Teradata
Day in the Life of a
Data Scientist

55. 55
Machine Learning and Graph Engine Functions
DATA
STORE
HIGHSPEEDFABRIC
STORAGE ENGINES
Machine
Learning
Graph
SQL
ENGINE
SQL
ENGINE
Graph
Machine
Learning
=
Statistics (17)
Path & Pattern (16)
Data Transformation (21)
Association (9)
Time Series (29)
Predictive Modeling (33)
Clustering (11)
Text (31)
Graph (12)
© 2020 Teradata

56. 56
Discover the Possibilities with the Teradata Vantage
Prediction
• How much revenues will we
have next month?
Segmentation
• Which prospects are the more
likely to purchase our product?
Understanding Causality
• Which customer events are
the most important to drive a
sale?
$
Text Mining
• Which offers include non-
compliant terms?
Networking Hypothesis testing
• Which customers are likely to
be fraudsters?
• Does our new website
generate significantly more
leads?
?
Re: Investment question
I can guarantee you a return on investment
of 10%, if you open a new saving account
with ACME Bank Inc. before the end of the
month.

57. 57
Day-in-the-life of a Data Scientist:
What Gems Can We Find in Our Customer Reviews?
* Technical maximal limit on InteliFlex 2.1
1
Launch Jupyter on AppCenter
Single Node, up to 36 Cores, 1.5 TB memory*
Powerful
workbench
2
Load customer reviews on Amazon Video into DataLab
(user space)
Easy
ingest
3
Benefit from JSON parsing data manipulation to clean the data in-
database at scale
Simple
transformation
4
Run Text Mining to understand hot keywords and relationship between
reviews using Cosine Similarity
Preferred
methodology
at scale
5
Develop a micro-app for marketing to visualize recent reviews in a graph
to improve marketing campaigns
Share
my findings
© 2020 Teradata

58. 58
Deep Dive Example: Clustering of Movie
Reviews Using Text Clustering and Graph
amazon_raw (table)
Amazon Prime Video Show
Reviews (JSON) from UCSD
What insights &
hidden gems are in
the review text?
© 2020 Teradata
TEXT MINING ON AMAZON REVIEWS

59. 59
Benefit from JSON parsing data manipulation
to clean the data in-database at scale
© 2020 Teradata
TEXT MINING ON AMAZON REVIEWS

60. 60
Transform Text to Vector Space Model (TF/IDF)
nGram function
“split the words”1
TF_IDF function
“words statistics”2
© 2020 Teradata
Term Frequency (i.e.
how often occurs the
term in this document)
(e.g. 1 / 28 = 0.0357…)
Inverse Document
Frequency (i.e. how rare
is the term across all
document, inverse of
likelihood to find a
document with this term)
TF*IDF: how
peculiar is this
term in this
document
TEXT MINING ON AMAZON REVIEWS

61. 61
Run Cosine Similarity between Doc Vectors
and Create Sigma Visualization
Cosine Similarity
“compare all reviews”3
© 2020 Teradata
TEXT MINING ON AMAZON REVIEWS

62. 62
Visualize and Drill Down in App Center
Identify top clusters
of key topics!
“Covert Affairs”
© 2020 Teradata
TEXT MINING ON AMAZON REVIEWS

63. 63
Visualize and Drill Down in App Center
“Edge of your seat”
New expression for
Marketing campaigns!
© 2020 Teradata
TEXT MINING ON AMAZON REVIEWS

64. 64
Large US Bank: Short Text Disambiguation
Merchant Names in Dictionary
Merchant Names in Transactions
© 2020 Teradata
Fuzzy
JOIN

65. 65
Generating QGRAMS
WEIGHTEDVECTOR#1WEIGHTEDVECTOR#2
© 2020 Teradata
Source: https://innersource.teradata.com/shared/qgram (for Teradata consultants)

66. 66
Cosine Similarity
VECTOR#1
VECTOR#1
Dictionary names QGRAMs
Transaction names QGRAMs
VECTOR#2
VECTOR#2
VECTOR#3VECTOR#4
© 2020 Teradata

67. 67
Display Matches with
Highest Cosine Score

68. Future of Teradata Vantage

69. 69
Teradata Vantage – Future (2020+)
QueryGrid
External Data
Store Access
NewSQL
R
Java
NewSQL
DATA STORE
HIGHSPEEDFABRIC
STORAGE ENGINES LANGUAGES
Spark
QueryGrid
External Analytic
Engine Access
TOOLS
BI and
VISUALIZATION
IBM Cognos
MicroStrategy
Oracle
Power BI
Qlik
Tableau
TIBCO Spotfire
ANALYTICS
Dataiku
TensorFlow
SAS – SAS Viya
NOTEBOOKS
and IDEs
RStudio
Jupyter
Studio
APP
FRAMEWORK
AppCenterNative Object
Store
AWS S3 &
Azure Blob
Deep
Learning
SAS Viya
Python
SAS
Scala
Machine
Learning
WORKFLOW KNIME
© 2020 Teradata
Pluggable Engines
Graph

70. 70
Key Takeaways
Teradata Vantage
© 2020 Teradata
2 31 Provides an extensible
framework for future
technology advancements
Delivers powerful
operational analytics with
integrated engines,
languages, and data sets
Simplifies the customer’s
analytical ecosystem

71. 71
Thank you.
© 2020 Teradata.