Maximize the Value of Your Data: Neo4j Graph Data Platform

CONNECTED DATA SERIES OCT 2021
Maximizing the Value
of Your Data with
Graph Data Platforms
Dr. Chris Marshall,
AVP, IDCAsia Pacific

How You Store Your Data Matters
Digital enterprises must transform data into actionable business insights to create value
Clearly, data has potential value, and some companies extract more value than others from the data that they have. Many factors drive the efficient and effective conversion of
data into information, knowledge, and value, for example, data-driven leadership, the knowledge and skills of a company’s employees, business processes, and the culture of the
organization. But another important driver of the data to insights process is the technology used to store data. Extracting value from data depends on the way it is stored, the ease
of access to it, and the extent to which we can relate that data to other contextual pieces of relevant data, information, and knowledge within the organization.
There are three common types of database management systems:
Source: IDC Worldwide Semiannual Software Tracker, 2020
Database Management Systems: CAGR 2016-2019
Relational database management systems (RDBMS) store data in tables. These are
popular when the relationships between data are stable and can be captured in high-
level table definitions. Relational databases can operate on a huge number of records
quickly and tend to use less storage space. But they are not very flexible when dealing
with more dynamic data whose context may change over time.
Non-relational database management systems (NDBMS) offer a variety of
alternative non-tabular approaches to organize, manage, store, and retrieve data.
Traditionally, NDBMS are optimized for particular data types and are typically legacy
systems not designed for newer, more dynamic workloads.
Dynamic data management systems (DDMS) can be more agile and efficient than
RDBMS and have gained traction recently, growing at 83.2% CAGR from 2016 to 2019
as advancements have brought it closer to overcoming potential problems. One such
dynamic database management system is a graph database. As the demand for
graph-powered analytical and artificial intelligence (AI)tools increases, so too has the
demand for graph databases and graph database platforms.
Dynamic
Database
Management
Systems
(DDMS)
Non-relational
Database
Management
Systems
(NDBMS)
Relational
Database
Management
Systems
(RDBMS)
83.2% 8.5% 8.1%
Scalable Data
Collection
2
Maximizing the Value of Your Data with Graph Data Platforms
Managers
End-User
Database
Management
Document-
oriented
Database
Systems
Navigational
Database
Management
Systems
Key-accessible
Database
Systems
Object-oriented
Database
Management
Systems
Graph Database
Management
Systems
Multivalue
Database
Management
Systems

Graphs, Graph Databases, and Graph Data Platforms
Graph data platforms allow you to develop and run applications on graph databases
3

Painting the Bigger Picture
Graphs allow users to examine dynamic, complex or unusual relationships between data
This is particularly useful for use cases such as fraud detection, anti-money laundering, and supply chain visibility.
Detecting fraud and money laundering activities
requires an understanding of the relationship between
complex transactions, individuals, and companies across
myriad jurisdictions. Graph databases allow users to
visualize the intentionally convoluted relationships
between entities, and provide more clues about fraud
and money-laundering activities than do traditional
approaches. Graphs also allow users to look at
relationships on a broader scale. Once certain patterns
are identified, graph analytic tools can help uncover similar
types of money laundering or fraud. Armed with actual
outcomes (fraud or not), investigators can feed this graph
data into ML models to ensure more accurate predictions.
Supply chains are complex and inextricably linked.
Any issue with an entity would create a knock-on effect
on other products. Graphs can provide individuals with
the ability to visualize the supply chain from a macro
perspective and identify the intricacies of the relationship
between each part of the supply chain. Graph analytic
tools enable enterprises to search through large amounts
of data quickly to identify how changes in costs, demand,
regulations, and supply can potentially affect them and
take steps to mitigate these issues.
Boston Scientific has a complicated global supply chain that starts
from raw materials to discrete products. This meant that when
vetting products, the root cause of any defect could hard to trace.
By using graph technology, Boston Scientific was able to create
data models that allow them to trace faulty components and
connect them to the products.
Case Study
Allianz used graph technologies to help spot fraudulent
activities in its ecosystem. This was done through visually
identifying illicit connections that fraudsters may attempt
to conceal. With customer data stored in a graph database,
Allianz was also better equipped to identify and uncover risks
and overlapping coverages.
Case Study
4

Graph Use Cases - Providing Context for AI
Graph technology and AI/ML have important synergies
Top 10 AI Use Cases in APEJ 20201
AI tools that can be graph powered make up the majority of the top 10 AI use cases today.
IDC
By 2022, at least 55% of A2000 companies will leverage
AI tools such as NLP, ML, and DL business-wide to
enable 55% of use cases across areas such as CX,
security, facilities, and procurement.2
Knowledge graphs that capture enterprise data as a graph can
use AI/ML to “understand” the context of the data and therefore
enable faster, more context-sensitive searches of the data.
Graph technologies enable many important AI use cases that
depend on the intricacies and rich interconnectedness of data
captured in graph databases. Examples include using intelligent
knowledge discovery tools, recommendation engines, and
conversational AI, among others, to conduct contextual searches,
make smarter recommendations, and offer richer insights. At the
same time, AI/ML use cases create more data and more complex
graphs, accelerating AI capabilities still further.
Therefore, IDC sees graph databases growing in lockstep with AI.
The use of AI software is growing at a rapid CAGR of 34.1% from
2019 to 2024.
Automated customer service agents
Automated threat intelligence and prevention systems
Quality management investigation and recommendation systems
Digital assistants
Fraud analysis and investigation
Smart business innovation and automation
Sales process recommendation and automation
Diagnosis and treatment systems
Program advisors and recommendation systems
Automated preventative maintenance
1
2
3
4
5
6
7
8
9
10
1
Source: IDC Spending Guide, 2021
2
Source: IDC FutureScape: Worldwide Artificial Intelligence 2021 Predictions - APEJ Implications
5

Essential Guidance
IDC recommends the following to enterprises:
Maximize your ability to synthesize data into business value
In today’s data rich world, more data does not equate to greater enterprise
intelligence. Understand the core pathways to converting data into
information, knowledge, and business value in your organization.
Identify how graph databases and platforms can help
alleviate your challenges
Databases play an important role in facilitating an organization’s conversion
of data into information, knowledge, and business value. Therefore, consider
carefully the strengths and weaknesses of the different types of databases
and how they support or hinder value creation out of your data. Understand
the benefits graphs can offer and take advantage of these to improve your
organization’s capacity to learn.
Choose the right graph database partner for your ecosystem
Capitalizing on graphs requires more than having a graph database. Look for
vendors offering graph analytics platforms that can seamlessly integrate with your
current ecosystem. Assess their overall platforms to ensure that their tools are in
line with your organization’s current and future analytical and AI needs.
6

About IDC
International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events
for the information technology, telecommunications, and consumer technology markets. IDC helps ITprofessionals, business
executives, and the investment community make fact-based decisions on technology purchases and business strategy. More than
1,100 IDC analysts provide global, regional, and local expertise on technology and industry opportunities and trends in over 110
countries worldwide. For 50 years, IDC has provided strategic insights to help our clients achieve their key business objectives.
IDC is a subsidiary of IDG, the world’s leading technology media, research, and events company.
This publication was produced by IDC Custom Solutions. The opinion, analysis, and research results presented herein are drawn
from more detailed research and analysis independently conducted and published by IDC, unless specific vendor sponsorship is
noted. IDC Custom Solutions makes IDC content available in a wide range of formats for distribution by various companies.
A license to distribute IDC content does not imply endorsement of or opinion about the licensee.
IDC Asia/Pacific
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#38-00 Fuji Xerox Towers
Singapore 079907
T 65.6226.0330
Copyright 2021 IDC. Reproduction is forbidden unless authorized. All rights reserved.
Permissions: External Publication of IDC Information and Data
Any IDC information that is to be used in advertising, press releases, or promotional materials requires prior written approval from the appropriate IDC Vice President or Country Manager.
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IDC Doc #AP241242IB
idc.com
@idc

Neo4j, Inc. All rights reserved 2021
1
Anticipate, Shift & Respond
The Era of Contextualized Intelligence
Nik Vora,
Vice President, APAC

2
Opportunities from 2020-21
Brittleness
→ Antifragile
Unintended consequences
→ Uncovered dependencies
Low signal
→ Relevancy
Perishable insights
→ Responsiveness
938 of
Fortune 1000
had a T1-2 supplier
impacted by the
pandemic
75% of executives
aren’t confident in
their data quality
Good forecasts are just
less bad
forecasts

Networks of People Transaction Networks
Bought
B
ou
gh
t
V
i
e
w
e
d
R
e
t
u
r
n
e
d
Bought
Knowledge Networks
Pl
ay
s
Lives_in
In_sport
Likes
F
a
n
_
o
f
Plays_for
Risk management,
Supply chain, Orders,
Payments, etc.
Employees, Customers,
Suppliers, Partners,
Influencers, etc.
Enterprise content,
Domain specific content,
eCommerce content, etc
K
n
o
w
s
Knows
Knows
K
n
o
w
s
3
Everything is Naturally Connected

4
And There’s Exponential Growth in Connections
Data models
the real world. So,
data is becoming
increasingly
connected.
The world is
becoming
increasingly
interdependent.
We can ignore this or
tame and leverage
connected data
to have clarity,
be nimble and thrive.

5
Anticipate Shift Respond
The downstream
effects
Your approach
to data
With contextualized
intelligence

6
The Bigger
and Smaller Picture
Data connections creates context
● Bring together fragmented and
inaccessible data
● Map complex, multidimensional
relationships and feedback loops
Through context we reveal meaning
● Uncover what’s been oversimplified
● Track dependencies and see ripple
effects
Successful entrepreneurs
are 30-48% more likely to
think holistically

Driving Intelligence into Data with
Knowledge Graphs
CONTEXT
Knowledge Base
Semantics
Static
Shallow
Context
Knowledge Graph
Graph Queries
Graph Algorithms & ML
Graph Visualization
Dynamic
Deep Context
Data
Ingestion
No Context
7
GRAPH

Use Context to Unlock Uses Otherwise Unattainable
“We found Neo4j to be literally thousands of times faster than our prior MySQL
solution, with queries that require 10-100 times less code. Today, Neo4j provides
eBay with functionality that was previously impossible.” — Volker Pacher, Senior Staff Engineer
8
DOCUMENT COLUMN KEY-VALUE
VALUE
KEY
KEY
VALUE
VALUE
KEY
Relational Databases
Don’t handle relationships
well
Practically limited to three
degrees of separation
Other NoSQL Databases
Don’t handle relationships at all
No language or structures for
handling relationships: joins done
through the application
Graph Databases
Natively store & query
relationships
Queries run 1000x faster at
scale, up to 1000+ hops

9
The Neo4j Graph Data Platform
Analytics &
Data Science
Tooling
Graph
Transactions
Data Orchestration
Development &
Administration
Drivers & APIs Discovery & Visualization
Graph
Analytics
AI

10
Used Neo4j Graph Data Science
algorithms to create unique, while still
anonymous, profiles based on cookies
and user behavior.
Increased Engagement
over 600%
Media conglomerate
with $3.2 Billion revenue
Magazines such as
People, Travel+Leisure,
Better Homes & Gardens,
Entertainment Weekly

11
Leading the way with data lineage and
governance solutions based on Neo4j to
help the organization with impact
analysis, systems migrations and master
data management.
Largest independent
broker-dealer in U.S.
Leading provider of
investment and business
solutions for independent
financial advisors.
Customer Graph for CCPA

12
Ignio captures and analyzes every
element of a customer’s business
operation and uses Neo4j to automate
the understanding of connections.
Digitate is a unit of
TCS, the leading global
provider of IT services,
digital and business
solutions with over
400,000 employees.
ignio AI Platform:
Award-Winning AI Solution

13
Thank you!
Contact us at apac@neo4j.com

Maximize the Value of Your Data: Neo4j Graph Data Platform

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