Modern Data Challenges require Modern Graph Technology

Modern Data Challenges
Require Modern Graph
Technology
Noel Yuhanna
Vice President, Principal Analyst
San Francisco, CA
Graph Summit
April 2023

Digital transformation continues to be a top
priority for global enterprises . . .

Digital transformations must be powered by new
generation solutions to remain competitive …
Graph
AI
Cloud

4
Data has become the
most critical asset for
any business to succeed
• Improve customer
experience
• Enable innovation
• Expand markets
• Increase revenue
• Retain customers
• Deliver new
products and
services
• And more ...
51%
Commercializing
data!

5
Data spread across multiple repositories and hybrid
cloud is creating new data management challenges
...
Multiple clouds Edge
On-premises
Facebook
LinkedIn
Opensocial
Simply Hired
Google+
Twitter
Social media
Data lakes/DW
SaaS
SugarCRM
Oracle
Salesforce
Abiquo
Eloqua
SAP
AppDynamics
Cloud9
DaaS providers
Hoovers
OneSource
Reuters
Windows Marketplace
D&B
Azure
Google
AWS 5G
Smart
devices
Edge
computing
Driverless
cars
Robots
IoT
sensors

6
IT processes, data and analytics are top focus when it
comes to digital transformation
24%
26%
27%
28%
29%
34%
46%
Inventory management and distribution
Employee experience
Product design and development/engineering
Security
Customer service/experience
Data and analytics
IT processes
“Which is/will be the focus of your organization’s digital business transformation?”
Note: Only the top seven responses are reported in this chart
Base: 2,252 services decision-makers who are involved in their organization’s digital transformation efforts; Source: Forrester Analytics Business Technographics® Business And Technology Services Survey, 2021

7
7
© 2023 FORRESTER. REPRODUCTION PROHIBITED.
Data management has become critical for all….
17%
18%
18%
19%
19%
20%
20%
20%
21%
24%
24%
0% 5% 10% 15% 20% 25% 30%
Lack of technology skills
Understanding the data
Lack of foundational investments
Lack of executive support to develop big data…
Lack of collaboration between teams
Accessibility, availability, and/or readiness of data…
Lack of business competency to deal with data that…
Organizational business issues with data…
Inability to process big data and act on it at the…
Maturity of technology around data management
Maturity of technology around security
What are/were the biggest challenges in executing your vision for data, data management, data
science, and analytics?
Base: 3627 Data and analytics decision-makers
Source: Forrester's Data And Analytics Survey, 2022

8
Traditional data
architectures are
unable to support
new data
requirements
Lack of support for real-time data: Traditional
architecture mostly supports batch or micro batched
Lack of consistent, trusted data: Data is not
consistent across apps, insights, analytics
Lack of modern data governance strategy: Most
organizations are unable to secure and govern
critical business data
Lack of integrating all data: Many are unable to
integrate all data — across silos
Lack of self-service data capabilities: Most
traditional architectures do not have self-service
Lack of automation to simplify deployments:
Most traditional systems lack automation to simplify
data management function

9
9
Data quality, data integration, cloud are the top
priorities for organizations over the next 12 months
21%
21%
23%
24%
24%
25%
29%
Data governance and auditing
Specialized (IoT) analytics platforms and
solutions to monitor products, customer…
Security analytics for threat detection/hunting
Master data management
Public cloud big data services (AWS, Azure)
Data Integration
Data quality
“Which of the following are the most important components of your organization’s plans for data, data management,
data science, and analytics in the next 12 months?”
Note: Only the top seven responses are reported in this chart
Base: 1,616 business and technology professionals (500+ employees); Source: Forrester’s Future Fit Survey, 2022
© 2023 Forrester. Reproduction Prohibited.

10
10
What businesses need is a modern approach to
connecting data to support new apps, and analytics.
Apps, insights, and
analytics need
contextualized data
Connected data
Trusted data
Real-time data
Self-service data
Governed data

Graph delivers contextualization to support new
digital transformation initiatives…
Customer transaction data
Sensor data
Click-stream data
Inventory data
Point-of-sale data
ERP data
Social data
Supply chain data
Product data
Sales data
Customer data
System of record(master data)

12
• Make connections quickly and
more accurately: For new and
emerging business use cases,
faster time to value
• Data analysis performance: Takes
query, insights and predictive
analytics to the next level
• Uncover hidden connections: In
data science, and advanced
analytics
• Improve staff productivity: With
minimal coding and more analysis
• Address new business needs:
Integrates with AI/ML to deliver new
business use cases.
Why use Graph
Database?

13
• Improve customer experience.
• Increase automation of internal
processes.
• Improve operational efficiency
and effectiveness.
• Increase employee productivity.
• Improve existing products and
services.
The top benefits of
Graph are aligned
with the top business
requirements of digital
transformations.
Note: Top five responses are shown. “Don’t know,” “other,” “none of these, and “we are not
using artificial intelligence (AI) technologies” responses were excluded.
Base: 3,139 data and analytics decision makers whose firm is interested in using/planning to
use/currently using AI; Source: Forrester Analytics Global Business Technographics® Data
And Analytics Survey

14
14
Native graph vs. Non-native/multi-model databases
› Key advantages of native graph:
• High performance – low latency access
• Improved scalability
• Optimized query processing – nodes/edges
• Improved administration – simplicity
• Better data consistency/integrity
• Key focus – driving innovation
• Comprehensive APIs for graph
• Broad tooling
• Improved support

15
15
Graph – Use Cases Are Many – Endless Possibilities
› 360-degree view of customer
› Social network Apps – Facebook, twitter, LinkedIn.
› Data Integration/ MDM
› Fraud and risk analysis
› Analysis of communication/network management
› Recommendation engines
› Master data
› Access control
› Hybrid Operational-Analytical Apps and Translytical
› And others

16
16
Leading industries – expanded to others ..
› Financial services
• Fraud detection, portfolio mgt, Upsell/cross sell, stock analysis, risk
analysis
› Healthcare and life sciences
• Clinical trials, patient management, drug research, disease tracking,
prescription management, insurance analysis,
› Retail
• Customer churn analysis, recommendation engine, customer
experience, customer intelligence and product revenue analysis
› Others – Oil And Gas, Government, Telco, ….…

17
17
Case study: Financial services company uses graph
technology to support fraud analysis
› Background
• With billions of events everyday, this large financial services company was facing a
major challenge to detect, alert, and process fraudulent activities.
• Data was spread across Oracle, SQL, Hadoop, Hive, files, streams . . .
• Integrating data across these sources was a challenge, and with new sources being
added, such as clickstream, web logs, and social media feeds, it had to look at a new
approach.
› Solution
• Used connected graph data platform to store, process and leverage unstructured data,
including logs and streams, and built models that integrated all relevant data sets in real
time to accurately assess if any given activity was a fraud.
• Unlike other banks and financial services companies that quite often had false positives,
this financial services company was quite accurate in its analysis

18
18
Case study: Retailer leverages graph technology to
deliver customer analytics
› Background
• Big data spread across clickstream, social media, blog, several databases, logs, and data
repositories
• Wanted integrated view across billing, revenue, and other customer data to better
understand its customers and their usage patterns
• Retailer also wanted real-time insights, immediate access to billed and unbilled
revenue, and ability to upsell and cross-sell new products.
› Solution
• Retailer used a combination of Hadoop, streams, replication, Hive, NoSQL, as
connected data within a lake to deliver actionable insights
• Some integration took place in Hadoop, others in-memory and Spark.
• Plans to add more data sources — geolocation, customer preferences . . .

19
19
Case study: Manufacturing organization uses graph
technology for IoT analytics
› Background
• A large manufacturing company with hundreds and thousands of machinery and
components and more than a dozen plants wanted a solution that could minimize
machinery failures.
• Some of the machine equipment was getting old, but the company wanted to ensure
that replacements were being done for the right machines, parts, etc.
› Solution
• They installed sensors and additional devices to collect data that fed into the connected
data fabric along with other data sets. It streamed data to Hadoop in its data center,
processed the data with historical data to determine machines likely to fail, wear out,
and have parts issue.
• Overall, the manufacturer claims to have eliminated many hours of machine outages
every month and, thus, have related to savings of millions over the year.

Data is a huge prerequisite to AI success!

. . . however, messy data without Context can
dramatically slow the AI process

22
Separate data engineering tasks from ML model
building tasks to make model building faster, more
focused on business use cases
Data
Connection
Data
acquisition
Data source
Data source
Data source
Data source
N
Feature
engineering
Data engineering Model building
Data + Graph
Modeling

Machine learning algorithms analyze data to create
predictive models.

Graph and AI can help determine which shipments to
prioritize and where to reroute to.
ML can help predict supply chain issues while there is
still time to remediate.

ML can help predict who will launch what cyberattack
before it happens.
Graph and AI can help determine what systems are
more vulnerable and need attention.

Graph and AI can determine the best way to retain
customers and improve customer experience.
ML can help predict customers likely to churn.

Graph and AI can help determine when to shut the
production line down to minimize cost and deliver best
business performance.
ML can help predict machine faults before they shut
down the production line.

Graph technology takes AI/ML to the next level …
invest in it and make it part of your digital
transformation strategy to gain competitive edge

Graph database adoption continues to accelerate across
all industries
14%
23%
35%
47%
65%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2015 2020 2025 2030

30
Forrester Graph
Data Platforms
Wave Report

Graph technology continues to evolve expanding
across various platforms and architectures
Graph
Database Applications
Graph databases started out with developers building custom Apps

SaaS/Commercial Applications
Embedded
Graph Database
Embedded graph databases expanded to cover
building modern SaaS/Commercial Apps

AI/ML/Data Science
Platforms
MultiModel Data
Platforms
Graph technology is now seen expanding
into new platforms and architectures
Graph
Technology
Data Fabric
Data Mesh
EDW / Data Lakes/
Lakehouse
Edge Platforms/Apps
22%
18%
15%
7%
10%
18%

Future of Cloud
Infrastructure
Clouds
SaaS
Clouds
Industry
Clouds
Domain
Clouds
Use Case
Clouds
Cloud Evolution

35
Thank You.
Noel Yuhanna
Vice President, Principal Analyst

Modern Data Challenges require Modern Graph Technology

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