During his presentation, Carl will examine the range of database usages and their corresponding database models. He’ll offer insight into the process of selecting a DBMS, based on purpose, and deployment. Carl will share the benefits of graph databases, consider potential advantages, and recommend approaches to adoption. You’ll learn how the various kinds of graphs serve different purposes and walk away with insights on the future of graph databases in general. If you can think of it, you can graph it.

1. Anything is Possible: Applying Graphs to
Your Most Complex Data Problems
Carl Olofson
Research Vice President
IDC

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Agenda
1 Thoughts on Graph Databases
2 Where Graph Databases Fit in the Database Pantheon
3 Every DBMS Supports Graphs (Not Really)
4 Types of Graph Databases
5 Common Graph Database Use Cases
6 Other Potential Uses of Graph Databases
7 The Future for Graphs, and Database in General

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Thoughts on Graph Databases
Most DBMSs focus on record-keeping and structured regurgitation.
Graph databases can
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Capture the realities of the enterprise based on those realities, not on some modeler’s conception of
them, for a “real world” view.
Enable discovery of improved efficiencies, hidden issues, better insights regarding customers,
products, and operations.
Resulting in
1 Lower overall operational cost
2 More effective sales efforts over time with less effort
3 New strategies going forward

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Thoughts on Graph Databases
Graph databases are the Swiss Army knives of databases.
Semantics (document
structures)
Networks
Patterns
They
can be
used for
1
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3
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5
Data structured may be pre-defined, or open.
Relationship combinations may be anticipated or discovered.
A graph database is the perfect platform for data structures generated by
the machine learning process.
Unanticipated data
combinations
Complex data
combinations

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Where Graph Databases Fit in the Database Pantheon
Key-value stores are good at rapid storage and retrieval of simple
sets of data; well suited for caching and high speed get and put.
Document databases are good for isolated applications that don’t
share or blend their data, and where the data structure is
intimately tied to the application code.
Relational databases are well suited to handling data that is easily
rendered in two-dimensional structures (tables), and for
comprehensive query handling (as in a data warehouse).
Graph databases can handle any data structure imaginable and is
constrained only by the limitations of the infrastructure.

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Every DBMS Supports Graphs (Not Really)
Graphs enable navigation of
arbitrarily complex relationship
structures.
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Being able to do a thing doesn’t
mean you should do that thing.
2
Any DBMS can store any structure.
Therefore, any DBMS can
store a graph.
1
No one wants to write graph
maintenance and traversal code; it’s
just too hard.
6
In a document database or key-value
store, data structures must be
supported in code. So, if you want to
use them to manage a graph, your
code is managing the graph. 5
Unlike in a relational database, such
navigation is not limited based on a
fixed schema.
4
A graph DBMS is specialized for
building, traversing, maintaining, and
reporting on graphs.
9
A few RDBMS vendors do have
specialized functionality that supports
graph, but do you want all that other
relational overhead just to manage
a graph? 8
If your favorite key value store vendor,
or document database vendor, or
relational vendor says, “we support
graphs too,” just smile.
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Types of Graph Databases
Types of Graphs
• Semantic Graphs
• Knowledge Graphs
• Property Graphs
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Required Functionality
• Support for Graph Analysis
• Support for Graph Traversal
• Support for Graph-Based Transactions
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Which type do you need?
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Common Graph Database Use Cases
Fraud Detection and Analytics
Network and Database Infrastructure Monitoring for
IT Operations
Recommendation Engine & Product Recommendation
System
Master Data Management
Social Media and Social Network Graphs
Identity & Access Management
Data Privacy, Risk, and Compliance
Artificial Intelligence and Analytics
Key Verticals that Use
Graph Database
Retail
Telecommunications
Government
Life Sciences
Financial Services
Supply Chain Management
Legal Record-Keeping (Case Law)

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Examples of Value from Graphs
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Logistics – Dynamic Routing for Faster and
Cheaper Pickup and Delivery
Supply Chain Management – Overcome
Scheduling Issues
Life Sciences – Rapid Drug Discovery
Marketing – Identify Shifting Demand
Patterns and Sell More Product.

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Other Potential Uses of Graph Databases
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2
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Metadata Management, Including Schema
Capture or Discovery
Geospatial Data Capture and Analysis
Information and Knowledge Management
Training Data Management for Generative AI

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The Future for Graphs, and Database in General
Graph databases will benefit tremendously from a commonly
accepted GQL (graph query language). Leading graph DBMS
providers are working on a common, SQL-like language.
Since the lead inhibitor for graph implementation is hardware,
progress in increasing processor speeds while reducing cost is key to
broader and deeper graph deployment.
There is increasing interest in multi-model DBMS. Most start from a
base and expand. Oracle, for instance, expands from a relational
base. MongoDB expands from a document base. It could be argued
that the best base to start from is a graph base, since it can be
adapted to the other formats, but it is difficult to adapt the other
formats to it.

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Conclusions and Recommendations
Graph Databases and Their Future
• The IT world has barely scratched the surface
of important use cases for graph databases.
• Graphs can form a foundation for a range of
mixed operations that may include some
blend of tables and documents in addition to
graphs.
• AI/ML will flourish with graph databases as a
data platform.
Conclusions Recommendations
• If you are exploring graph
databases, talk to existing users
and discover the broad range of
possible applications. They go well
beyond fraud detection and pattern
discovery.
• If you are a current user of graph
databases, consult your team, and
look at all the other problem spaces
in your business where limitations
on data organization is holding you
back.
• Don’t be afraid to expand and
explore, You may be able to do
more than you think with graphs.
• Consider graphs for cases where the
organizational requirements of data what
applications can do.
• Start with a small project, and build from
there
• Learn from the experience of others… there
are numerous useful cases online.
Getting Started

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If you can think it,
you can graph it.

15. IDC.com linkedin.com/company/idc twitter.com/idc blogs.idc.com
© IDC
Carl Olofson
Email: colofson@idc.com
Twitter: @databaseguru
LinkedIn: linkedin.com/in/carlolofson