Tiger graph 2021 corporate overview [read only]

Advanced Analytics and
Machine Learning on
Connected Data
Updated Jan 2021
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© 2021. ALL RIGHTS RESERVED. | TIGERGRAPH.COM |
INTRODUCTION
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© 2021. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | C
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“Graph analysis is possibly the single
most effective competitive differentiator
for organizations pursuing data-driven
operations and decisions after the
design of data capture.”

“New and valuable insights come from finding links
between well understood, integrated data.”
● Analyzing the relationships between entities to
identify patterns in the links between them, and
in space and time, extracts maximum value.
● 44% of star-performing analytics adopters
analyze graphs and linked data, use time series
and geospatial data.
● 31% use graph database technology to derive
business insights from links between items.
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© 2021. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | 5
Perfect scores were awarded for:
● Scalability
● Performance
● Workloads
● Transactions
● Queries/search
● Data loading/ingestion
● API/extensibility
TigerGraph also received the highest possible score in the community criterion in the
“strategy” category and in the global presence criterion in the “market presence” category.
TigerGraph Scores Perfectly On 9 Key Criteria for Enterprise Deployments
Download the full report here.

Why Graph Analytics
Source: Gartner - Top 10 data and analytics Trends for 2019
Graph deployments are going deeper, wider and operational:
Need to make it accessible to non-technical users
6
● Definition - Graph analytics is a set of analytic techniques that
allows for the exploration of relationships between entities of
interest such as organizations, people and transactions.
● Forecasted growth - 100% annually through 2022
● What’s driving the growth
○ Need to ask complex questions across complex data, which is
not always practical or even possible at scale using SQL
queries. (RDBMS requires time-consuming & expensive table
joins!)
● What’s needed for broad adoption of graph data stores
○ Graph data stores can efficiently model, explore and query data
with complex interrelationships across data silos, but the need for
specialized skills has limited their adoption to date.
Customer
Supplier
Location 2
Product
Payment
PURCHASED
Location 1
Order

Why Graph, Why not RDBMS
Graph handles relationship analytics better

“Finding relationships in combinations of diverse data,
using graph techniques at scale, will form the
foundation of modern data and analytics.”
● Graph analytics is a set of analytic techniques that allows
for the exploration of relationships between entities of
interest such as organizations, people and transactions.
● Graph analytics will grow in the next few years due to the
need to ask complex questions across complex data,
which is not always practical or even possible at scale
using SQL queries.
● Through 2022, the application of graph processing and
graph databases will grow at 100% annually to
accelerate data preparation and integration, and enable
more adaptive data science.
Gartner Research: Top 10 Trends in Data and Analytics, 2020. Published 11 May 2020.
Gartner Research: Cool Vendors in Data Management. Published 7 May 2020. Download the Report
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Graph+AI Delivers More Value, Better Results
Richer, Smarter Data
● Connections-as-data
● Connects different datasets, breaks down silos
Deeper, Smarter Questions
● Look for semantic patterns of relationship
● Search far & wide more easily & faster than other DBs
More Computational Options
● Graph algorithms
● Graph-enhanced machine learning
Explainable Results
● Semantic data model, queries, and answers
● Visual exploration and results
Customer
Supplier
Location 2
Product
Payment
PURCHASED
Location 1
Order

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Who is TigerGraph?
We provide advanced analytics and machine learning on connected data
○ The only scalable graph database for the enterprise: 40-300x faster than
competition
○ Foundational for AI and ML solutions
○ Designed for efficient concurrent OLTP and OLAP workloads
○ SQL-like query language (GSQL) accelerates time to solution
○ Available on-premise & on: Google GCP, Microsoft Azure,
Our customers include:
○ The largest companies in financial, healthcare, telecom, media, utilities and
innovative startups in cybersecurity, ecommerce and retail
Founded in 2012, HQ in Redwood City, California
Corporate Overview Video

TigerGraph Awards
11
2018
DBTA
22 Startups to
Watch
2018
Strata
Most Disruptive
Startup
2019
DBTA
Trend-Setting
Product
2019
SD Times
‘Best in Show’
2019
DBTA
100 Companies
That Matter
Most in Data
2020
Solutions
Review
7 Best Graph
Databases
2019
TechTarget
Up-and-coming
BI Vendors
2020
DBTA
100 Companies
That Matter Most
in Data
2020
Data
Breakthrough
Best Graph DB of
the Year
2018-2021
insideBIGDATA
Impact 50 List
2020
Gartner
“Cool Vendor”
in Data
Management
2020
Forrester
Wave Leader

Advanced Analytics and Machine Learning on Connected Data
Continuous graph-based feature generation and training
LEARN FROM CONNECTED DATA
AI-based Customer 360 for entity resolution,
recommendation engine, fraud detection
Friction-free scale up from GB to TB to
Petabyte with lowest cost of
ownership
.
CONNECT ALL DATASETS
AND PIPELINES
Customer 360 connecting 200+
datasets and pipelines
Identity graph connecting multiple
data pipelines
Item 360 for eCommerce across 100+
datasets
Advanced
Analytics
In-Database
Machine Learning
Distributed
Graph DB
Fortune 50 Retailer
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7 out of top 10 global banks
Real-time fraud detection and credit risk
assessment
10-100X faster than current solutions
ANALYZE CONNECTED DATA
Jaguar Land Rover
Supply chain planning accelerated
from 3 weeks to 45 minutes
Fraud Detection - batch to real-
time for 750 million calls/day

Why is TigerGraph Cool?
DEMOCRATIZATION OF
GRAPH FOR ENTERPRISES
“TigerGraph is cool for its ability to
democratize graph analytics for
enterprise adoption”
MASSIVE GROWTH IN
GRAPH DB & ANALYTICS
“Through 2022, the application of graph
processing and graph databases will grow
at 100% annually to accelerate data
preparation and integration, and enable
more adaptive data science.”
RELATIONSHIP ANALYSIS
AS THE FOUNDATION
“Finding relationships in combinations of diverse
data, using graph techniques at scale, will form
the foundation of modern data and analytics.”
TIGERGRAPH FOR
ENTERPRISE-LEVEL ADOPTION
“TigerGraph is a good fit for organizations that
have clear graph problems to solve, but cannot
find a solution initially for enterprise-level
adoption. It also fits organizations that have the
requirement for real-time and multihop
analytics.”
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Download the Cool Vendor Report

Perfect scores were awarded for:
● Scalability
● Performance
● Workloads
● Transactions
● Queries/search
● Data loading/ingestion
● API/extensibility
TigerGraph also received the highest possible score in the community criterion in the
“strategy” category and in the global presence criterion in the “market presence” category.
TigerGraph Scores Perfectly On 9 Key Criteria for Enterprise Deployments
Download the full report here.

CUSTOMER AND
PEER INSIGHTS
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What do Customers say about TigerGraph on
Gartner Peer Insights?
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See more: https://www.gartner.com/reviews/market/data-warehouse-solutions/vendor/tigergraph

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See more: https://www.gartner.com/reviews/market/data-warehouse-solutions/vendor/tigergraph/product/tigergraph/review/view/1393006

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See more: https://www.gartner.com/reviews/market/data-warehouse-solutions/vendor/tigergraph/product/graphstudio/review/view/1384756

How Our Customers Use TigerGraph
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Find similar users/customers
Recommend next best action
Find most influential
users/customers
Detect connected users
(communities)
Uncover hidden connections
Who are the patients that are going through a particular type of
journey that results in an adverse health outcome?
Is the
Is the new credit card applicant or transaction connected to
known fraudsters?
Can I run a real-time credit score algorithm and recommend an offer
based on the customer’s credit profile & need?
Which users are driving higher usage or adoption of my product or
service?
What is average spend over time across a community of connected
users (fin. services, airlines, healthcare, retail..)?

Advanced Analytics on Connected Data
Deep Wide Operational
Analyze
relationships deeper into the
data to find hidden patterns
Connect
all datasets to uncover
undisclosed relationships
Process
transactions in real-time to
provide the next best action
At Scale
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Example: Driving Business Value in 3 Steps
Step 1:
Entity Resolution
Step 2:
Relationship Analysis
Step 3:
Insights and Actions
Link IDs to create a unified
identity
Connect & analyze the
internal and external
datasets for user behavior
● Marketing Campaigns
● Viewership History
● Promotional Responses
● Engagement Activity
● Purchase History
● And More
Find similar users/customers
Recommend next best action
Find most influential
users/customers
Detect connected users
(communities)
Uncover hidden connections
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TigerGraph: Why, How & What
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Our Mission How What
● Improve fraud detection for 4 out
of top 5 global banks
● Deliver care path
recommendations for 50 million
patients
● Reduce power outages for over 1
Billion people
Deep, Wide and
Operational
Analytics at
Massive Scale
TigerGraph Cloud
&
TigerGraph
Enterprise
Help Our Customers
Improve the World with
Deeper Insights
TigerGraph Corporate Introduction Video

HEALTHCARE
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Distinguished Engineer
TigerGraph is the only system today that can help
us make real-time care-path recommendations
using knowledge of 50 million patients. Your
products will have worldwide impact on making
everyone’s lives better in more ways than you can
imagine.
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FORTUNE 10 HEALTHCARE COMPANY

Mapping Out the Patient Journey in Healthcare with a Relational DB
Complex Database Joins Across 50+ Silos Leads to Delayed Business Insights
Patient
Facility
Medical History
Prescriber
Healthcare Services Claim
Rx Claim
Mental Health Claim
Procedure Claim
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Healthcare: Connect All Datasets and Pipelines for
Customer 360 Journey for Healthcare Insurance Members
● Integrate 200+ datasets
and pipelines to provide
unified view for each
member driving higher
productivity for call center
operations
● Find similar members
with a click of a button
in real-time (50 ms)
● Deliver care path
recommendations for
similar members
UnitedHealth Group Has Built the Largest Healthcare Graph in the world with 10 billion entities
(claims, patients, doctors..), 50 billion relationships & 23,000+users! (Graph+AI Keynote - https://info.tigergraph.com/keynote-edward-sverdlin)
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Advanced Analytics for Healthcare in Graph
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● Foundational for ML, AI features and applications
● Built on the only scalable enterprise graph
database
○ Designed for OLAP and OLTP workloads in same
database
● SQL-like querying for faster user adoption and
application development
Built to combine multiple types of data in real-time to
address four strategic imperatives:
● Deliver high quality of care while controlling costs
● Detect and prevent waste, abuse and fraud
● Link public and internal data to improve outcomes
● Improve member satisfaction
“TigerGraph is the only system today that can
help us make real-time care-path
recommendations using knowledge of 50 million
patients. Your products will have worldwide
impact on making everyone’s lives better in
more ways than you can imagine.”
- Distinguished Engineer at
Fortune 10 Healthcare Company

USE CASES
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© 2021. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | CONFIDENTIAL INFORMATION |
Entity Resolution Customer 360
Cybersecurity
Machine Learning
Recommendation
Systems
Data Lineage Fraud Prevention
Supply Chain
Management
Law Enforcement
Network & IT
Resource
Utilization
Influencer &
Community
Identification
Knowledge
Graphs
Explainable AI
Social Network
Analysis
Drug Reaction
Analysis
Graph Use Cases
31
Investment
Opportunity
Analysis

Cybersecurity
Machine Learning
Recommendation
Systems
Supply Chain
Management
Law Enforcement
Network & IT
Resource
Utilization
Influencer &
Community
Identification
Knowledge
Graphs
Explainable AI
Social Network
Analysis
Drug Reaction
Analysis
32
Investment
Opportunity
Analysis
Graph Use Cases

Entity Resolution &
Customer 360
Problem:
Mapping users in multiple environments (entity
resolution).
Solution:
By creating a universal ID that aligns the logic of
the business with downstream systems our
customer is able to generate better marketing and
sales campaigns.
Plus, if the business logic changes, it will be easy
to update the analytics (but very difficult using
legacy systems such as RDBMS.)
33

HBO/WarnerMedia
Identity Graph
AT&T created Xandr in 2018 as a way to consolidate all
of AT&T's advertising infrastructure, including assets
from its Time Warner and AppNexus acquisitions.
Xander CEO says that in 2020, Xandr will integrate all
of WarnerMedia's content data onto its own data
platform to build an "identity graph," or a database
that links all of the particular preferences and attributes
of a single consumer.
"We did a great job of pulling together [last year] the
telecom data [from AT&T], and in 2020 there's lots of
data assets within WarnerMedia that we're going to
gain access to," CEO Lesser said, referencing consumer
data spanning properties from Bleacher Report to HBO
Max.
34
https://www.axios.com/att-xandr-2020-time-warner-media-
advertising-01e48f29-32fc-4c3f-bca7-ee43b9477e8b.html

Graph Based Entity Resolution for MDM
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Visit the solution page - https://www.tigergraph.com/solutions/real-time-customer-360mdm/

From Graph + AI Conference Presentation by Xandr Data Science team - www.tigergraph.com/xandr
Media/Telecom: Entity Resolution of people & households

● Distributed graph with 5+ billion vertices and 7+ billion edges
● Up to 1 billion daily graph external updates
● 300 million vertices and 1+ billion edges created by the algorithms
● 10-node TigerGraph cluster. Each node has 48 cores, 400GB RAM, 3BGps NVMe storage
● BFS-style algorithms [in GSQL], like Label Persistence, over a large distributed graph
● We can add more RAM [scale vertical]. We need to scale horizontally [add server nodes]

Opportunity
Xandr wanted a way to help advertisers target audiences with the
right promotional messages by deeply analyzing data on consumers,
devices, content, advertisers’ needs and other attributes, collected
across 15 WarnerMedia properties and credit reports from Experian.
Xandr Improves Advertising Targeting Effectiveness
with Identity Graph powered by TigerGraph
Solution
Xandr has built an identity graph using AT&T, WarnerMedia, Third-party and its own data, and leverage
TigerGraph to perform entity resolution.
Results
● Implement frequency-capping at the household or user level to ensure efficient advertiser spend
● Help advertisers find more consumers with audience extension and increase their campaigns lift with
conversion attribution across different devices
● Manage consent elections across first party assets and third party data to respect customer preferences
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More details including customer success story, Graph + AI conference session by Xandr team at tigergraph.com/xandr

Challenge
Tru Optik wanted to provide advertisers with the ability to
target consumers in over-the-top media and gaming
environments with unmatched accuracy and scale while
complying with relevant privacy regulations
Better Advertising Engagement for OTT
Media and Gaming with TigerGraph
Solution
• Entity resolution that consolidates all household information into a single ID
• Support for IPv6 that enables advertisers to be more granular in their targeting and
deliver personalized ad content to viewers
Results
Tru Optik is extending its lead as a platform for highly-targeted advertising - its Household
Graph is bringing the world’s leading brands and 80+ million homes closer together
Learn about TigerGraph’s customers: https://www.tigergraph.com/customers/

Challenge
Ensure that consumers keep coming back to HBO by
promoting the right content and ads to the right people -
this requires creating a single unified ID for subscribers
viewing content on different platforms and devices
Increased Customer Loyalty and Subscriber
Revenues with TigerGraph
Solution
• Entity resolution creates a single unified ID for each subscriber
• Multi-hop analysis of data improved user segmentation and recommendation engine
prompts viewers to watch additional content
Results
HBO is able to increase subscription revenues and improve customer loyalty by promoting
content that more closely aligns to the viewing interests of individual households
Learn about TigerGraph’s customers: https://www.tigergraph.com/customers/

Optimizing Digital Marketing Campaigns with TigerGraph
Business Challenge
Running multiple campaigns across different digital channels can
give marketers a headache when it comes to analyzing the integrated
results in real time. Each campaign can be looked at in isolation but
the challenge is to track this horizontally.
Solution
• Extracting audience analytics by adding network intelligence
on top of a data lake.
• Serve interactive fine-grained analysis of campaign
performance versus audience across multiple ad-tech and
mar-tech platforms.
Business Benefits
Myntelligence enables customers to optimize their go-to-market
strategy via access to campaign results in real-time to monitor
audience reach and path-to-conversion for awareness and
performance. Brands can capitalize their own campaign knowledge
and design infinite customer journeys through AI-powered campaign
mapping, improving targeting efficiency and increasing ROI.
43
“We were looking for a high performing graph
analytics platform that would be easy to build
our solution on top of. We evaluated all the
players, including open-source solutions, and
TigerGraph emerged as the best fit.”
Selection Process
Why TigerGraph
“TigerGraph offers the performance, scalability,
and ease-of-use we needed and allows us to
connect and transform the data in our Hadoop-
based data lake so that we can deliver
contextualized insights to our customers.”
Press Release - Link

Cybersecurity
Machine Learning
Recommendation
Systems
Supply Chain
Management
Law Enforcement
Network & IT
Resource
Utilization
Influencer &
Community
Identification
Knowledge
Graphs
Explainable AI
Social Network
Analysis
Drug Reaction
Analysis
44
Investment
Opportunity
Analysis
Graph Use Cases

Automated Fraud Prevention
Problem:
Losing $M to fraudsters because of manual
and slow investigation processes.
Solution:
By adding graph to its payment fraud systems
our customer has created a rapid and precise
process automation system that significantly
reducing the number of false-positives.
Investigators can now focus on investigating
high-value fraud cases.
45

Automated Fraud Prevention with Advanced Analytics
Deep Wide Operational
Analyze
relationships deeper into the
data to find hidden patterns
Connect
all datasets to uncover
undisclosed relationships
Process
transactions in real-time to
provide the next best action
At Scale
46

Fraud Detection: Need for Better AI
47
● $118 billion of blocked sales in the U.S. with
15% of cardholders experiencing blocked
sales
● High-income consumers (> $75,00/yr) at
higher risk of false positives (22%)
● 40% of denied users are attempting to pay a
greater than $250 transaction
$30B
Lost to
Fraud
80% false
positives
(blocking non-
fraud
transactions)

Fraud Detection in Financial Services (Payments)
48
Email
Account
Phone_number
Send_payment
Receive_payment
Payment
User
Device
Used_with
Bank
Sets_Up

49
Email 1
Account 1
Phone_number 1
User 1
Device 1 (phone)
Used_with
Credit Card
Sends_payment
Payment 1
Account 2
User 2
Sets_Up
Bank
Device 101
Account 101
Stolen Credit Card
Phone_number 101
Sets_Up
Has
Phone_number 2
Hop 1
Hop 2
Hop 3
Hop 4
Hop 5
Payment 101
Hop 6
Fraud Detection in Financial Services
User 101
New accounts 1 & 2 - linked back to device
101 used for prior fraudulent payment 101
& account 101 - Payment 1 rejected!
User 1 & User 2 flagged for investigation.
Advanced Analytics(Deep)
with TigerGraph
Regular Analytics
(Shallow) vs
Sign up FREE for TigerGraph Cloud to use the starter kit for fraud detection (payments)

RDBMS Requires Complex Table Joins: Can’t Support Real-Time
Traversal of Connected Data
50

Preventing Fraudulent Loans with
TigerGraph
51
Business Challenge
A leading U.S. bank needed to search across 20TB of data for
possible connections between credit card applications known to
be fraudulent and applications of unknown status - relational
databases and other graph providers were not up to the task, as
they were unable to deliver the speed and scale required.
Solution
• Pairing graph technology with machine learning to identify
fraudulent activity at scale and intervene in real-time.
• Leveraging deep analytics to find hidden connections
across 20TB+ of data.
Business Benefits
• Able to score and prevent fraudulent loan applications on a
massive scale – minimum 30% uplift and $15M annual
incremental fraud avoidance. $1.5M through cost savings
on false positives.
Tier 1 U.S. Bank
20TB
Card applications
data
6 weeks
PoC elapsed time
3 months
Time to build and fully deploy
platform to production
$16.5M
1st year ROI with 30%
uplift in fraud detection
CLV Impact > $100M

NewDay Intercepts Fraudulent Credit Card
Applications with TigerGraph Cloud
52
Business Challenge
NewDay is one of the largest issuers of credit cards in the UK. They needed to
uncover and prevent fraudsters joining their credit card network at the time of
application. Traditional relational databases could not scale to analyze the
volume of interconnected data or any potential connection to organized crime.
Solution
● GraphStudio integrates all phases of graph data analytics into one
graphical user interface.
● Fraud Investigation team can act autonomously to tune queries in near
real-time with ‘train-of-thought’ analysis and speed, without needing
developer resources.
Business Benefits
NewDay specialists are now empowered to identify and prevent fraudsters from
joining their network by checking data against known and new fraud syndicates,
resulting in millions of dollars saved and a double digit reduction in fraud.
Read the Press Release NewDay Press Release
“NewDay has always had a ‘customer-first’
mindset, and it is this dedication to empowering
and protecting customers that fueled our
signing on with TigerGraph,
We had looked into other graph analytics
companies after we upgraded our data
platforms, yet none provided the forward-
looking technology, ease of use, training or
support that TigerGraph did..”
Danny Clark, head of fraud prevention, NewDay
Building The Next-Gen Fraud Detection

Customer Testimonials - NewDay
“NewDay works with millions of customers,
each with billions of rows of valuable account
data that we can use to disrupt criminals.
Traditional relational databases could not
scale to analyze the volume of interconnected
data or any potential connection to organized
crime that we wanted to find,”
Jamie Burns
senior fraud strategy and analytics manager,
NewDay
Why TigerGraph
"In our ever-changing world with increasingly
interconnected data, we needed to uplevel our
technology offering. At the same time, we
wanted to enable our Fraud Investigation team
to act autonomously – without relying on
developers – to tune queries in near real-time
with ‘train-of-thought’ analysis and speed."
Danny Clark
head of fraud prevention, NewDay
Ease of Use
https://www.linkedin.com/in/danny-clark-0b80011/ https://www.linkedin.com/in/jamie-b-126a8557/

Feature Extraction for ML/AI
54
Double the performance of Fraud Detection System with 50% reduction in false positives & 50% reduction in
undetected fraud transactions with Graph DB features when compared to legacy solution

Visit tigergraph.com → Solutions → Financial Services for the solution brief & machine learning
workshop for building the fraud detection system with TigerGraph

Detecting Fraud Rings with TigerGraph
56
Business Challenge
A leading U.S. bank wanted a better way to detect and remove
fraudsters from their credit-card network. Prototypes had shown
that a combination of advanced graph algorithms gave significant
gains – big-data tools and other graph technologies either couldn’t
scale to the full customer base or gave inconsistent results.
Solution
• Implementing PageRank and Louvain [fraud] community
detection in an MPP native-parallel database.
• Leveraging deep analytics to find hidden connections across
20TB+ of data.
Business Benefits
• Able to expose fraud rings, shut down connected cards, and
combat fraudulent activity on a massive scale –35% uplift
and $50M incremental fraud avoidance. >$1.5 million through
cost savings on false positives, infrastructure and TCO
Tier 1 U.S. Bank
10TB
Card applications
data
6 weeks
PoC elapsed time
3 months
Time to build and fully deploy
platform to production
+$50M
1st year ROI with 35%
uplift in fraud detection
CLV Impact > $200M

7 of the Top
10 Global
Banks Use
TigerGraph Merchant Analytics:
Transaction
sequencing to
detect geolocation
proximity.
Credit Card Fraud:
Is applicant
connected to
potential
fraudsters?
Trade
Surveillance: Are
employees
following the
rules?
Impact Analysis:
Communities or
Clusters
impacted by
the fraud rings
Credit Scoring:
Real-time credit
scoring to help
recommend
offers best suited
to customer
profiles?
Wealth
Management:
What Accounts,
HNI to target for
stocks or life
change events.
57

Customer Due Diligence - Combine Internal Data With Third-party Sources
58
Visit the solution page at https://www.tigergraph.com/solutions/risk-assessment-and-monitoring/
Hop 5
Hop 4
Hop 4
Hop 1
Hop 2
Hop 3
Regular Analytics
(Narrow)
Graph Analytics
(Wide)

OpenCorporates Upgrades Performance and Functionality
press release -https://tinyurl.com/y36skysr
Challenge
OpenCorporates is the world’s largest open database of
corporate information. They had challenges with scalability, lack
of support for simple queries, and speed using a first-generation
graph technology
Solution
● Support queries of up to five degrees of separation to help
uncover relationships between entities and see which
relationships are active vs. dead
● Insight into how relationships and networks have changed
and evolved over time (temporal graph search)
Benefits
Scaled their database with 170 million corporate entities to
provide users with deeper analysis of the information and help
uncover instances of criminal or anti-social activity - such as
corruption, money laundering, and organized crime
59
“TigerGraph’s excellent scalability and
performance enables us to achieve
things we previously could not do, and
to better support ongoing investigative
work in the process.”

IceKredit Builds a Customer 360 Graph for Credit Rating
and Risk Assessment
60
Business Challenge
Rapid growth in size and complexity of the interconnected global financial
markets makes it difficult for banks to process loan applications for home,
automobile, etc.
Solution
● Leverage Machine Learning and AI for custom advanced models and
analytics to build comprehensive credit views for applicant
● Quantify applicant’s fraud probability and compares it with actual business
activity
● Find undisclosed relationships and connections within data; assign and
update risk ratings in real time
Business Benefits
IceKredit is empowering lenders by reducing their fraud risks with more
accurate, detailed credit ratings for applicants that are not tracked by
traditional credit bureaus.
Read the Press Release https://info.tigergraph.com/tigergraph-fintech
“We selected TigerGraph for its
superior data warehousing speed
and computational processing
capacity, which improved
performance by an order of
magnitude.”

Credit Risk Assessment - Combine Traditional & Non-traditional Data Sources
61
Visit the solution page at https://www.tigergraph.com/solutions/risk-assessment-and-monitoring/
Regular Analytics (Narrow) Advanced Analytics with TigerGraph (Wide)

Pagantis Delivers Faster Consumer Finance Services
with TigerGraph on AWS
Business Challenge
Pagantis must assess credit worthiness and fraud risk in real-time
for customers to allow them to pay for their purchase in monthly
installments. Risk assessment with relational databases was taking
too long, delaying the time for loan approvals.
Solution
• Real-time calculation of customer’s credit rating using their
current activities as well as all available historical data
• A scalable, high-performance system to deliver insights into
complex relationship-based workflows for credit scoring,
fraud detection, recommendation engines and risk analysis
Business Benefits
Pagantis can now offer a faster and seamless consumer finance
solution for the eCommerce merchants throughout Italy, France and
Spain.
Press Release - https://info.tigergraph.com/pagantis-tigergraph 62
“We selected TigerGraph for its
superior data warehousing speed
and computational processing
capacity, which improved
performance by an order of
magnitude.”

Cybersecurity
Machine Learning
Recommendation
Systems
Supply Chain
Management
Law Enforcement
Network & IT
Resource
Utilization
Influencer &
Community
Identification
Knowledge
Graphs
Explainable AI
Social Network
Analysis
Drug Reaction
Analysis
63
Investment
Opportunity
Analysis
Graph Use Cases

Machine Learning
Problem:
Detect fraud at scale in real-time.
Solution:
By adding graph analytics and artificial
intelligence to link its historical data, our
customer is creating new machine learning
features with the data.
Enables preventative measures that continue
to outsmart fraudsters and save
$Millions/month.
64

China Mobile Detects Phone Based Fraud with Real-Time
Customer 360 & Machine Learning
Business Challenge
Find and report fraudsters among 50 million subscribers & billions of
calls per week.
Solution
● Maintain a real-time operational graph with 50 million subscribers &
15 billion call detail records
● Analyze caller patterns with immediate call recipients as well as
extended network to compute features such as stable group, in-group
connections & 3-step friend connection to find fraudsters
● Feed machine learning with new training data for fraud detection with
118 features per phone every 2 hours
Business Benefits
Scale up for over 2,000 calls per second to detect fraudsters
committing phone based fraud in real-time with 5 level hop analysis
and improve customer satisfaction with proactive identification and
blocking of fraudsters.
65
China Mobile is using TigerGraph to
check each of its hundreds of
millions of daily calls in real time to
see if it looks to be from a spammer
or phone-based fraudster.

Using Graph-Based Features for Machine Learning In Healthcare:
Good Doctor - Bad Doctor
66

67
Detecting Phone-Based Fraud by Analyzing Network or Graph
Relationship Features at China Mobile
Download the solution brief at - https://info.tigergraph.com/MachineLearning

AI-based Detection of “Bad Doctors”
1.Generate graph-based features
2.Correlate graph features to target activity
68
Machine
Learning
System
Training
Data
Claims, patients, prescribers, facilities..
(1)Stable group for routine ICD
codes
(2)Average cost of prescribed
medications, tests &
procedures
(3)No potential undeclared
prescriber-facility
relationships
Low risk prescriber
features
Good Phone
Features
(1)Empty Stable group for
routine ICD codes
(2)Higher cost of prescribed
medications, tests &
procedures
(3)Potential undeclared
prescriber-facility
relationships
High risk prescriber
features
Detection
Model

Powering Explainable AI with Graph Database
Additional details at https://www.tigergraph.com/solutions/ai-and-machine-learning/ 69

In-database Machine Learning with TigerGraph
graph algorithm library - https://docs.tigergraph.com/graph-algorithm-library
▪ PageRank
▪ Community Detection
• Louvain
• Label Propagation
• Connected Components
• Triangle Counting
▪ Similarity
• Jaccard Similarity
• Cosine Similarity
70

PageRank for Hub Detection - Finding the Influencers Driving
the Spend
71
Additional details at https://www.tigergraph.com/solutions/product-service-marketing/

Community Detection to Understand Current Spend and
Prioritize Marketing Activities for a New Drug
72
Additional details at https://www.tigergraph.com/solutions/product-service-marketing/

Amgen: Transforming Product & Service Marketing in Pharma
with Machine Learning & Graph Analytics
Solution page https://www.tigergraph.com/solutions/product-service-marketing/
We quickly ran into problems
scaling with our original graph
database – loading the data took a
lot of time and once it was loaded
computing either didn’t finish or
was extremely slow.
Business Challenge
Understanding relationships among patients & prescribers to increase the
sales of a pharmaceutical drug.
Solution
● Identify referral relationships among prescribers through
correlation of medical and pharmacy claims data over time
● Detect communities of prescribers based on claims analysis and
identify influential hubs
● Prioritize the key prescriber communities to roll out a new drug
Business Benefits
With terabytes of data, finding a graph database that could scale to load
and compute the referral networks was a challenge. With TigerGraph,
Amgen is now able to find the most influential prescribers driving
prescriptions for cardiac care & educate them on products with the best fit
and efficacy for their patient population.
73

Vishnu Maddileti
Data Science & Analytics
Noel Gomez
Watch on Youtube:
Part 1
Part 2
Customer Testimonial - Amgen
74
All testimonials - https://www.tigergraph.com/testimonials/
We are dealing with a humongous
amount of data, EMRs (electronic
medical records), CHRs (comprehensive
health records), it’s in terabytes and
combing through that in RDBMS would
be a nightmare.
With 5 billion vertices and 20 billion
edges, it’s huge data and finding
inferences in that data is not easy, but
TigerGraph has scaled for us.

Cybersecurity
Machine Learning
Recommendation
Systems
Supply Chain
Management
Law Enforcement
Network & IT
Resource
Utilization
Influencer &
Community
Identification
Knowledge
Graphs
Explainable AI
Social Network
Analysis
Drug Reaction
Analysis
75
Investment
Opportunity
Analysis
Graph Use Cases

Supply Chain
Problem:
Incurring fines ($400M+) resulting from
needing 12 weeks to understand if forecasts
are accurate.
Solution:
By complementing its supply chain analytics
with graph our customer has transitioned from
contracts based on estimated volumes to
bringing real data in real-time into its supply
chain capacity planning models.
$20M+ savings/year with 1 query!
76

Building a Better Supply Chain with Graph Analytics
77
Graph analytics makes it possible to track every individual part through
its entire lifecycle, from supplier through manufacturer to finished
product

Business Value: Forecast versus
Orders Supplier Impact
A large manufacturer identified that they would benefit (potentially by
£tens-hundreds of millions) from a timely analysis of impact to their supply
chain of changes to their forecast orders.
● Sales forecasts are typically years in advance so suppliers can tool-up
● Minimum buy volumes are committed from forecast to support
investment
● Demand can vary widely and quickly from the forecast
● Costs to the business can significantly impact margins
● Having good information allows the executive to put mitigations in
place
78

Graph Analytics Opportunities in Automotive
Sales Orders
Marketing
Feature
Engineering
Feature
Parts Suppliers
Actual or Synthetic
Car configurator maps
the complex
relationship between
features
Master feature
dictionary
Map features to
versioned parts
Map parts to their
local suppliers
Map upstream
supplier network
Answering historic blind spots
Identifying tactical opportunities
Optimisation

Sales
Orders
Marketing
Feature
Engineering
Feature
Parts Suppliers
Sales Order Book(SOB) and Build Planning
Targeted benefit – Increase average profit per unit and minimize
aged inventory
What is the impact of part shortage on customer orders?
Manufacturing Efficiency
Targeted benefit – Reduce Line & Role changes and reduce CPU &
Network cost
How much can we switch production of one model for another
within constraints?
What would be the optimal sales order mix in order to minimize
cost and disruption to supply chain and manufacturing?
What lines will be most impacted by the latest change to the SOB?
What optimum production level should be proposed to enable SOB
optimisation?
What change to the schedule would decrease the changes without
impacting customer promise-dates ?
Optimisation
80

Sales Order Book(SOB) and Build Planning
Targeted benefit – Increase average profit per unit and minimize aged inventory
What is the impact of part shortage on customer orders?
Manufacturing Efficiency
Targeted benefit – Reduce Line & Role changes and reduce CPU & Network cost
What would be the optimal sales order mix in order to minimize cost and disruption to supply chain and manufacturing?
What lines will be most impacted by the latest change to the SOB?
How much can we switch production of one model for another within constraints?
What optimum production level should be proposed to enable SOB optimisation?
What change to the schedule would decrease the changes without impacting customer promise-dates ?

Sales
Orders
Marketing
Feature
Engineering
Feature
Parts Suppliers
Parts Supply
Targeted benefit – Reduce emergency logistics costs and overhead
Which parts are most at risk of shortage after the latest change to
the SOB?
Supplier Risk
Targeted benefit – Reduce supplier fines and disruption
What other sourcing options are available for parts with a predicted
shortage?
Which orders are impacted by at-risk/constrained Suppliers?
What minimum and maximum order levels should be set in the
contract based on SOB scenarios?
Which high risk suppliers are critical for production and are not
currently prioritised for support?
Optimisation
Which parts should have their ordering levers changed on the
basis of SOB scenarios?

Parts Supply
Targeted benefit – Reduce emergency logistics costs and overhead
Which parts are most at risk of shortage after the latest change to the SOB?
Supplier Risk
Targeted benefit – Reduce supplier fines and disruption
Which parts should have their ordering levers changed on the basis of SOB scenarios?
Which orders are impacted by at-risk/constrained Suppliers?
What other sourcing options are available for parts with a predicted shortage?
What minimum and maximum order levels should be set in the contract based on SOB scenarios?
Which high risk suppliers are critical for production and are not currently prioritised for support?

84 © 2020 Gartner, Inc. and/or its affiliates. All rights reserved. 205201
Answering Critical Business Questions With Graph Analytics
Overview
Data and analytics leaders struggle to advance a shared understanding of data across business verticals and functions.
Jaguar Land Rover demonstrates how graph analytics can give the business a connected view of supply and demand,
enabling efficient answers to critical business questions.
Solution Highlights
1. Identify a common language for speaking business and data.
2. Connect supply and demand data in a knowledge graph and explore your most critical business problems by browsing
up and down the graph. Examples:
a) Demand for a model is suddenly surging in the US market. Do we have all the parts
we need to meet this demand? Where do the supplier risks lie?
b) Demand for a model is suddenly dropping drastically in the US market.
What parts will we now have in surplus? How can we best use these parts?
c) What is the profitability impact of changing a feature of a car?
About the Company
Jaguar Land Rover (JLR)
Industry: Manufacturing
Headquarters: Coventry, UK
Revenue: GBP 25.8 Billion (2019)
Employees: 44,101 (2019)
Harry Powell
Director of Data
and Analytics
Alice Grout-Smith
Data Scientist
Martin Brett
Senior Data Architect
Hazel Scourfield
Data Scientist
Gartner case study for Jaguar Land Rover - Answering Critical Business Questions with Graph Analytics (Jaguar Land Rover),
October 28, 2020, Gartner ID G00733557
Automotive: Analyze Supply Chain & Demand Factors

Clear Two-Way Line of Sight Between Demand and Supply
JLR’s Demand-Supply Graph
Car C contains
the feature F1.
Features F1 and F2 are
connected because they are
both features of car model C.
Parts P1 and P2 are
connected because they are
both parts for feature F3.
Source: Adapted From Jaguar Land Rover
Demand
Supply
P2
P1
F1
F3
F2
C
Show dependencies
between Suppliers →
Parts → Features → Cars
Include costs and
constraints

Identifying and Reducing Supply Chain Risks
JLR’s Demand-Supply Graph for Exploration & Discovery
Panoramic Sunroof
Evoque SE
Fixed Sunroof

Making the Most of Surplus Inventory
JLR’s Demand-Supply Graph for Investigation & Inference
Discovery
Sport SE
Evoque SE
Range Rover
Sport SE
Hinge
Panoramic Sunroof
Fixed Sunroof
Wind Deflector

Solving an Intractable Optimization Problem
Critical Business Question: What is the profitability impact of changing a feature of a car?
Evoque With Plain Roof Evoque With Sunroof
The feature change
has downstream
ripple effects on
parts inventory and
cost.
The feature change
has upstream ripple
effects on the car’s
price and revenue.
▲Revenue
impact
Replace the sunroof
with the moonroof.
▼Cost
impact
One example of millions of what-if perturbations

Results
Decision Speed Business Value
“As we began using the same data as
our commercial and manufacturing
partners, it has become a lot easier
to work together and address our
business problems in greater depth.”
Director of Purchasing, JLR
Supplier Risk
▲ 3x
2017 2019
▲ 120x
2017 2019
▼ 35%
2017 2019

Business Challenge
Sales forecasts are typically made years in advance so suppliers can prepare
and tool-up highly specialised production lines. JLR were incurring large fines
from their suppliers due to being unable to perform timely analysis of impact
to their supply chain of changes in their forecast orders.
Solution
● Seamless joining of complex tables across multiple systems allows
data access across customers, vehicles, features, parts, and suppliers.
● Advanced production planning using predictive analytics, real-time
simulations and scenario modelling.
Business Benefits
Having up-to-date and highly qualitative information allows business
stakeholders to quickly put mitigations in place. With TigerGraph, JLR are
benefiting from a timely impact analysis of changes to their forecast orders to
their supply chain, minimising and potentially avoiding fines from their
suppliers of millions of pounds.
Jaguar Land Rover (JLR): Production Planning Optimisation
for Highly Complex Supply Chains
90
"With TigerGraph we can join sources of
data together and make connections
within the data that previously we
couldn’t. We can now answer questions
that, for the last 20 years, we didn't think
were possible to ask."
“We used the graph to re-sequence how
our vehicle orders were to be built in our
factory in response to a supplier failure. A
process which in the past might have taken
days was both modelled and evaluated in
less time than it took to write the
PowerPoint slide to present the idea.”
New Insights with TIgerGraph
Speed of Planning with TigerGraph

Vishnu Maddileti
Noel Gomez
Martin Brett
Senior Data Architect, JLR
Customer Testimonial -
Jaguar Land Rover
91
All testimonials - https://www.tigergraph.com/testimonials/
"We were really impressed with the speed
and ease at which TigerGraph was deployed.
Also being reasonably schema-loose allows
design changes to be made fairly last minute
and provides a highly flexible option that also
offers extensibility to add additional datasets
as the needs of the graph change over time."
Harry Powell
Director of Data & Analytics, JLR
"TigerGraph was the only solution that was
able to execute our highly complex use case
at scale. Other solutions we tried could do
queries on use cases with quite limited
interconnectivity but as soon as that was
scaled up, the solution no longer worked."
Selection Process
Ease of Deployment & Flexibility
Harry’s blog outlining £100 million in incremental annual
profit for Jaguar Land Rover with Advanced Analytics -
https://www.linkedin.com/pulse/unicorn-attic-harry-powell

Jaguar Land Rover Featured on CIO.com
Harry Powell
The software, from TigerGraph, detected when
suppliers would fail to meet quota demands.
“We used the graph to re-sequence how our vehicle
orders were to be built in our factory in response to a
supplier failure,” Powell says. Queries across the
supply chain model now take 30 to 45 minutes
compared to weeks
using SQL relational database software.
Accelerate planning at JLR - weeks to minutes
CIO.com article - The pandemic pivot: IT leaders innovate
on the fly, August 13 2020 https://www.cio.com/article/3570423/the-
pandemic-pivot-it-leaders-innovate-on-the-fly.html
92

Jaguar Land Rover - Full Excerpt from CIO.com article
The pandemic pivot: IT leaders innovate on the fly
By Clint Boulton, Senior Writer, CIO, AUG 13, 2020 3:00 AM PDT
Overhauling the sales forecast
The COVID-19 outbreak threatened to disrupt Jaguar Land Rover’s (JLR) supply chain, a global network comprising hundreds of
suppliers.
The automotive company typically relies on sales forecasts cultivated years in advance to orchestrate its production lines, a delicate dance
that required it to manage thousands of combinations of parts made by myriad manufacturers, says Harry Powell, director of data and
analytics. Accuracy is paramount, as minimum buy volumes of parts are committed with penalties for not meeting the agreed upon
volume.
Recognizing that JLR’s careful choreography wouldn’t hold during the pandemic, Powell told business leaders that they could not
longer rely on sales forecasts to which they were accustomed. “I went around telling
everybody that we were looking at this [challenge] through the wrong end of the telescope,” Powell says. “You have to be more flexible in
how you make things and have the ability to react to new information.”
The analytics team needed to provide more timely analysis of the impact changes to the forecast orders would have on JLR’s supply
chain. Powell’s team revved up its use of graph database software, which analyzes the relationships of entities — in this case parts and
suppliers — to provide its business with more accurate analytics.
The software, from TigerGraph, detected when suppliers would fail to meet quota demands.
“We used the graph to re-sequence how our vehicle orders were to be built in our factory in response to a supplier failure,” Powell
says. Queries across the supply chain model now take 30 to 45 minutes compared to weeks using SQL relational database software.
Source - https://www.cio.com/article/3570423/the-pandemic-pivot-it-leaders-innovate-on-the-fly.html
93

Jaguar Land Rover 2nd Featured on CIO.com
Harry Powell
CIO.com article - Emerging tech soothes pandemic-disrupted
supply chains - August 18th, 2020
https://www.cio.com/article/3570487/emerging-tech-soothes-pandemic-disrupted-supply-
chains.html?utm_campaign=organic&utm_medium=social&utm_content=content&utm_sou
rce=twitter
“The task, in which JLR combined 12 data sources in
a graph equivalent to 23 relational database tables,
helped JLR make connections within the data - such
as exactly what it can build at the moment with parts
in hand - that it previously couldn’t.” Powell says.
The analysis took only 45 minutes compared to the
weeks it would take to join the data using
relational systems.
94

Jaguar Land Rover - Full Excerpt from 2nd CIO.com article
Emerging tech soothes pandemic-disrupted supply chains
By Clint Boulton, Senior Writer, CIO | 18 AUGUST 2020 11:00 BST
Auto company plots a path with graph analytics
Jaguar Land Rover is one such organization using analytics to help alleviate disruptions to its sales forecasts. JLR, which makes the
namesake Land Rover and Range Rover SUVs, typically relies on forecasts issued years in advance, granting hundreds of suppliers lead
time to craft parts. In addition to helping JLR estimate demand, the forecasts ensure it can commit to purchase minimum buy volumes of
parts.
But the COVID-19 outbreak forced JLR to scrap its sales forecasts, says Harry Powell, JLR's director of data and analytics, who told his
business peers the company had to be more nimble about balancing supply and demand given the uncertainty about whether suppliers
would be able to make enough of the 30,000-odd parts automotive makers require.
To perform a more timely analysis of its supply chain, JLR leaned into graph database software to correlate data and identify
relationships between entities across multiple complex data sources, including forecast and supply chain data, parts data and car
configuration data. Graph analytics helps data scientists find unknown relationships and connections within data that are not easily
discovered with traditional analytics technologies that query relational database systems.
The software, from startup TigerGraph, queried data across disparate systems, including mainframe, ERP and manufacturing applications.
The task, in which JLR combined 12 data sources in a graph equivalent to 23 relational database tables, helped JLR make connections
within the data — such as what exactly it can build at the moment with its parts in hand — that it previously couldn't. The analysis also
took only 45 minutes compared to the weeks it would take to join data using relational systems, Powell says. The analysis helped JLR
potentially avoid millions of dollars in charges from suppliers for failing to fulfill minimum buy volume stipulations.
95

Jaguar Land Rover Infrastructure: Direct Integration with GCP
Authentic
Game Server
App Engine
Asynchronous
Messaging
Cloud Pub/Sub
Parallel Data
Processing
Cloud Dataflow
Raw Log
Storage
Cloud Storage
Analytics
Engine
BigQuery
Batch Load
Coworkers
Real-Time
Events
Interactive
Data
Exploration
Cloud
Datalab
BI Tools
Streaming Pipeline
Batch Pipeline
Streaming Pipeline
Batch Pipeline

Automotive - Where Does a Scalable Graph Platform Drive Business
Value?

Cybersecurity
Machine Learning
Recommendation
Systems
Supply Chain
Management
Law Enforcement
Network & IT
Resource
Utilization
Influencer &
Community
Identification
Knowledge
Graphs
Explainable AI
Social Network
Analysis
Drug Reaction
Analysis
Investment
Opportunity
Analysis
Graph Use Cases
98

Recommendation Systems
Problem:
To stand-out in the crowded eCommerce landscape,
recommendations need to be personalized based on
the browsing, search and purchase history
Solution:
By analyzing the consumer behavior with graph
analytics, our customer is generating contextual real-
time recommendations, tailored to the preferences,
interests and likely needs based on life-stage.
As a result, this ecommerce company has increased
revenue per customer visit while creating stronger
affinity through better customer experiences.
99
FORTUNE 10
HEALTHCARE
99

Optimizing Digital Marketing Campaigns with TigerGraph
Business Challenge
Running multiple campaigns across different digital channels can
give marketers a headache when it comes to analyzing the integrated
results in real time. Each campaign can be looked at in isolation but
the challenge is to track this horizontally.
Solution
• Extracting audience analytics by adding network intelligence
on top of a data lake.
• Serve interactive fine-grained analysis of campaign
performance versus audience across multiple ad-tech and
mar-tech platforms.
Business Benefits
Myntelligence enables customers to optimize their go-to-market
strategy via access to campaign results in real-time to monitor
audience reach and path-to-conversion for awareness and
performance. Brands can capitalize their own campaign knowledge
and design infinite customer journeys through AI-powered campaign
mapping, improving targeting efficiency and increasing ROI.
100
“We were looking for a high performing graph
analytics platform that would be easy to build
our solution on top of. We evaluated all the
players, including open-source solutions, and
TigerGraph emerged as the best fit.”
Selection Process
Why TigerGraph
“TigerGraph offers the performance, scalability,
and ease-of-use we needed and allows us to
connect and transform the data in our Hadoop-
based data lake so that we can deliver
contextualized insights to our customers.”
Press Release - Myntelligence press release

© 2021. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | Press Release - Ippen Digital Press Release
Hyper-Personalized Recommendations with TigerGraph
101
Business Challenge
Ippen Digital, a pioneer in helping publishing transition to
new digital revenue through more sophisticated use of
content and audience data, recognized that its current in-
house system could not deliver highly customized
recommendations
Solution
● 360-degree view of customers’ interests &
preferences based on all digital interactions
● Knowledge graph powered by combination of
machine learning and graph database
● Efficient and cost-effective solution that can scale as
Ippen Digital continues to grow
Business Benefit
Ippen now offers hyper-personalized recommendations that
drive higher engagement and revenue for the publishing
industry
“TigerGraph provides a scalable
and high-performance graph
database platform”

Cybersecurity
Machine Learning
Recommendation
Systems
Supply Chain
Management
Law Enforcement
Network & IT
Resource
Utilization
Influencer &
Community
Identification
Knowledge
Graphs
Explainable AI
Social Network
Analysis
Drug Reaction
Analysis
102
Investment
Opportunity
Analysis
Graph Use Cases

Knowledge Graphs
Problem:
Struggling to match invoices with customer
accounts
Solution:
Our customer creates digital versions of millions
of invoices each day and is using TigerGraph to
match them with pre-existing customer accounts
without human intervention.
Saves 1000s of hours per year in manual labor
and improving employee productivity. (RDBMS
and other graph technologies were tested, but
failed).
103

PRODUCT SLIDES
104

Graph
RDBMS
Document
Key Value
Analytics
Business
Intelligence
Optimization &
Simulation
Data Connectedness
105
Advanced
Analytics
Data Complexity
Machine
Learning & AI
OLAP Queries
Reports &
Dashboards
Data
Discovery
Predictive
Analytics
Data
Modeling

Delivering New Graph Based Solutions with TigerGraph
106

Complementary Interoperability
Seamless integration enables
businesses to accomplish more
with existing investments in third-
party infrastructure:
● High-speed data
loading
● REST API for queries
and updates
● JSON output
Data
Sources
CSV/Tex
t
Social
RDBMS
Hadoop
Spark
Log Files
Enterprise
Data
Infrastructure
BI
Analytics
Visualization
Dashboards
Reports
Data
Warehous
e
Master
Data
Stores
TigerGraph
Graph Storage
Engine
Graph Processing
Engine
Graph Data
Storage
Graph Data
Compression
Parallel
Processing
Graph
Partitioning
GSQL Graph
Query
Language
REST API
RESTPP / Kafka / Loader
GraphStudio
Visual UI / SDK
API
Stream
Infrastructure
On Premises Cloud Hybrid
ETL
Loader
107

For Example
Company Data
Control Relationships
Subsidiaries
Shareholders (etc)
Company
10
8
Company

Data Processing Workflow Example
109

https://www.tigergraph.com/cloud/
Start in minutes, build in hours and deploy in
days with the industry’s first and only
distributed graph database-as-a-service.
START FREE
110

TigerGraph Cloud Starter Kits
are built with sample graph
data schema, dataset, and
queries focused on a specific
use case such as Fraud
Detection, Recommendation
Engine, Supply Chain Analysis
and/or a specific industry such
as healthcare, pharmaceutical
or financial services.
111

TigerGraph GraphStudio™
is our simple yet powerful
graphical user interface.
GraphStudio integrates all
the phases of graph data
analytics into one easy-to-
use graphical user
interface. GraphStudio is
great for ad-hoc, interactive
analytics and for learning
to use the TigerGraph
platform.
112

Amazon Web Services
Advanced Partner
Microsoft Azure
Gold competency certified & co-sell ready
Google Cloud Services
Google Cloud Partner
https://www.tigergraph.com/cloud-marketplaces/
TigerGraph - Cloud Marketplaces
113

DEMO/END SLIDES
114

Selected Paid Customers
Financial
Services
Media, Tech
&
eCommerce
Telecom
Healthcare,
Manufacturing,
Energy &
Government

116
The TigerGraph Difference
Feature Design Difference Benefit
Real-Time Deep-Link Querying
5 to 10+ hops
● Native Graph design
● C++ engine for high performance
● Storage Architecture
● Uncovers hard-to-find patterns
● Operational, real-time
● HTAP: Transactions+Analytics
Handling Massive Scale ● Distributed DB architecture
● Massively parallel processing
● Compressed storage reduces
footprint and messaging
● Integrates all your data
● Automatic partitioning
● Elastic scaling of resource usage
In-Database Analytics & Machine
Learning
● GSQL: High-level yet Turing-
complete language
● User-extensible graph algorithm
library, runs in-DB
● ACID (OLTP) & Accumulators
(OLAP)
● Avoids transferring data
● Richer graph context
● Graph-based feature extraction for
supervised machine learning
● In-DB machine learning training
● No-code migration from RDBMS
● No-code Visual Query Builder
● Democratize self-service analytics
to derive new-insights from
legacy/external data stores

117
Starter Kits and Developer Portal for Graph+ML
1. Content-based movie recommendation: similarity, k-
nearest neighbor + latent factor
2. Entity resolution: Link & merge similar entities, based
on similar properties and neighbors
3. Low-rank approximation of graph relationships
4. Graph feature engineering for anti-fraud ML
dev.tigergraph.com

Get Started for Free
● Get the Free Enterprise License at tigergraph.com
● Try TigerGraph Cloud with free tier - tigergraph.com/cloud
● Learn from 40+ on-demand sessions at tigergraph.com/graphaiworld
● Take a Test Drive - Online Demo at tigergraph.com/testdrive
● Join the Community at tigergraph.com/community
@TigerGraphDB /tigergraph /TigerGraphDB /company/TigerGraph
11
8

DEMO
119

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