Neo4j Innovation Lab – Bringing the Best of Data Science and Design Thinking Together

Design Thinking 💙 Data-Science
Connections, August 25, 2021
The importance of understanding data-
structures when designing products
Alessandro Svensson, Head of Neo4j Innovation Lab

Neo4j Innovation Lab
Hello 👋
Alessandro Svensson, Neo4j

Head of Neo4j Innovation Lab

The intersection of product design & data-science
01

02

03

04

05
Introduction to the Neo4j Innovation Lab

Product designers and data-scientists

Data structures & the data value chain

Real world examples

Data Thinking Prototyping Framework
Today’s Agenda

01
Introduction to Neo4j
Innovation Lab

Help companies accelerate innovation and digital transformation
through graph thinking.
What we do
We generate and prototype graph projects together with
customers and prospects.
How we do it
Expected Outcome
To provide a deep understanding of graph thinking and the
new possibilities in innovation and digital transformation that is
enabled by adopting Neo4j and graphs.
Innovation Lab Sprint — 3 day workshop
Methodology

Viability/Value
Proposition
Discovery

Ideation
Neo4j / Graph
Data Science
A Day in the Life in the Neo4j Innovation Lab

Viability/Value
Proposition
Discovery

Ideation
Neo4j / Graph
Data Science
The Innovation Lab
Sweet Spot
A Day in the Life in the Neo4j Innovation Lab

Storyboarding/

Mockups
Identify Stakeholders / Synopsis

Functional – Emotional – Social Needs
Sprint scope/

Define Goals
Define Target Use
Case
Reference Graph
Data Model
Executive Presentation

Solutions Roadmap
Graph Related
Questions
Source data to

populate model
Build Query /
Pipeline
Solutions
Framework
Build Prototype/Wireframes
Behind the Scenes
On Stage
Methodology

1. Defining Use Case
UX/UI Lead
Data Scientist
Head of Innovation Developer
CIO
Business Analyst
Head of Customer Success

2. Data modeling
Adam Cowley, Neo4j
Field Engineer
Director of AI
Data Scientist
Eric Monk, Neo4j PS-
consultant
Innovation Lab
Leader

3. Crafting Prototype

02
Product design and
data science roles in
today’s organisations

“Data Thinking”
Design

Thinking
Data-science
Data
Thinking

Reduction of friction
Shared goal of products designers and data scientists:

User Experience vs Underlying Architecture
User Experience of an App The Underlying Architecture

Different types of design practitioners
Animation: https://dribbble.com/goodgiven
• Graphic design

• Visual design

• UX design

• UI design

• UX/UI design

• Design research

• Interaction design
• Product design

• Web design

• Learning design

• Process design

• Design strategy

• Etc…

Design is an ‘everything process’

• Data Scientist

• Data Analyst

• Data Engineer

• Data Architect

• Data Storyteller

• ML Scientist
• ML Engineer

• BI-Developer

• Database Administrator

• Technology Specialized Roles

• Etc…
Data Workers
https://towardsdatascience.com/10-different-data-science-job-titles-and-what-they-mean-d385fc3c58ae

Multi-faceted area of work
Data
Capture
Data
Cleaning
Data
Modeling
Data Analysis
Visualization
Deployment

& Scale
Evolve

Compute complexity
Reduce Friction
On stage

User Experience
Underlying Data

Behind the scenes
Where design meets data work

Compute complexity
On stage

User Experience
Underlying Data

Behind the scenes
https://dribbble.com/shots/15633889-Investment-Mobile-Apps
https://dribbble.com/shots/14951197-Social-app-design-Light
Investment App Social Media App

On stage

User Experience
Underlying Data

Behind the scenes
https://dribbble.com/shots/15633889-Investment-Mobile-Apps
https://dribbble.com/shots/14951197-Social-app-design-Light
Investment App Social Media App

02
The Data Value Chain
& Data Structures

Data Value Chain
Raw data Captured data Organized data Processed data

Data Value Chain
💡

Conceptual, no value

Data Value Chain
💡

🧐

Potential value

Data Value Chain
💡

🧐

Potential value
🤑

Valuable

Data Value Chain
💡

🥳

Very valuable
🧐

Potential value
🤑

Valuable

Data Value Chain
Power analytics
Power applications
💡

🥳

Very valuable
🧐

Potential value
🤑

Valuable

“A database is an organized collection
of structured information, or data,
typically stored electronically in a
computer system.”
Tools for organizing data (databases)
— Wikipedia

Relational Database
This is what people typically think about

Key Value Wide column Document Relational Search Graphs Multi-model

Wide Column
Stores
Raw data
💡
{
}
{
}
Document Relational

SQL
NoSQL (Native) Graphs

Architecture:

Choosing the
right tool for

the job

Databases
Potential Raw
Data to Capture
Data Warehouse

Data Lakes
Graph
Architecture:

Choosing the
right tool for

the job

Databases
Potential Raw
Data to Capture
Data Warehouse

Data Lakes
This makes sense for
connected problems i.e
recommendation engines
💡
Graph
Architecture:

Choosing the
right tool for

the job

04
Real world case studies

Case Study: Google Image Recognition
https://www.youtube.com/watch?v=Y2VF8tmLFHw
Sundar Pichai

CEO, Google

Knowledge Graphs
Neural Networks + Graphs
Training of neural
networks models
Inference in
real-time
Restaurant
Reviews
Budget-

category
Cuisine
Location

On stage Behind the scenes
Neural networks Knowledge graph
+
“Augmented reality photo app”

Case Study: Last Mile Delivery
https://dribbble.com/shots/6990419-Finery-Wishlist

Case Study: Last Mile Delivery
https://dribbble.com/shots/6990419-Finery-Wishlist
Same day
1-4 hours
2-4 hours

PRODUCTS
INVENTORY
(real time)
PROMOTIONS
(real time)
LOGISTICS
(real time)
CONTENT

Neo4j Innovation Lab Store Location

T-shirt
Summer
Clothes

Zip Code
T-shirt
Summer
Clothes

Zip Code
T-shirt
Summer
Clothes
Store Location
Truck
Bike

Zip Code
T-shirt
Summer
Clothes
Store Location
Truck
Availability slots
Bike

05
Data Thinking Prototyping
Framework

Data Thinking Prototyping Framework
Business Problem/Opportunity
What is the business problem/opportunity you want to
solve for with graphs and connected data, and why is it a
relevant problem to solve?
Data Model &
Ingestion
What does your data-model look like,
and how should you ingest the data?
Underlying Solutions
Architecture
How will graphs be fitted into your overall
architecture?
People, Teams &
Skills
What are the stakeholders, organizations
and skillsets required to be successful?
“Money” Queries
& Algorithms
What are the queries and algorithms
relevant to extract value
Value unlock
What is the value that will be unlocked
due to connecting your data?
Applications &
Services
What are the tangible touch
points?
BUSINESS
ORGANISATION
Data Capture &
Strategy
What are the essential data-points and behaviors
relevant for your use case to capture?
DATA

3 Key takeaways
Your apps are as good as the data that powers them.
What happens “behind the scenes, shows up on stage”.
👆
Use data-thinking to trigger your imagination. Mapping data
strategy gives you a lot of ideas for function.
✌
You don’t have to know every technology in detail,
a basic understanding of data-structures goes a
long way in choosing the right tool for the right job.
🤟

Neo4j Solutions Days
https://go.neo4j.com/solution-days

Time for Q&A
Thank you for listening 🙏

Neo4j Innovation Lab – Bringing the Best of Data Science and Design Thinking Together

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