You might have read in The Economist magazine, "Data is the new oil." But what does that practically mean and how does it relate to the "Innovation Gap" most CXO and technology leaders are trying to address with their data initiatives? On this webinar we’ll discuss the source of this innovation gap and how leading organizations are using new technology to connect their existing master data to gain insights to build apps that work at the speed of business. You’ll learn how winning organizations connect their systems of analysis and systems of insight with their systems of engagement and interaction to deliver a superior digital customer experience. We’ll also cover an exciting new role emerging within this digital economy and how you can position yourself for advancement as your organization transforms.

1. What does MDM
Have to do with
Innovation?

2. 1. Gap in Current Landscape
2. Introduction to Case Study #1
3. Connected Data in Retail
4. Case Study #2
5. Closing the Insights to Action Loop
6. Revisit Case Study #1 & Demo
7. Distinguishing Features of Neo4j
8. Conclusion
Agenda
Nav Mathur
Sr. Director Global Solutions @ Neo4j
@nav_mathur, in/navmathur

3. Current Landscape

4. Customer MDM
GAP
Product Data
RDBMS
Supply Chain Data
RDBMS
Employee Data
RDBMS
Data in the Enterprise
Propel
Workday
ADP
RDBMS
Customer Data
MDM-Systems
Employee MDMSupply Chain MDMProduct MDM
Web Interface
File Transfer
API-call
Database
Enterprise data
sourced from partners
Salesforce
Enterprise data in the
cloud
Trello
ServiceNow
Apps & Business Processes

5. Customer MDMEmployee MDMSupply Chain MDMProduct MDM
Data Lake
(Big Data)Data Warehouse
Reporting Analytics
Emerging
Applications
• Cross Sell
• Recommendations
• KYC Use Cases
• Risk Management
• Impact Analysis
• Compliance Traceability
• Data Governance
Applications depend
on your Master Data

6. Customer MDM
MDM-Systems
Employee MDMSupply Chain MDMProduct MDM
Data Warehouse
Data Lake
(Big Data)
Requires Data Queried at the Speed of Business
(often in Real-Time)
Emerging
Applications
• Cross Sell
• Recommendations
• KYC Use Cases
• Risk Management
• Impact Analysis
• Compliance Traceability
• Data Governance
Applications depend
on your Master Data

7. Customer MDM
MDM-Systems
Employee MDMSupply Chain MDMProduct MDM
Data Warehouse
Data Lake
(Big Data)
Batch Processed, Slow Queries
Highly Connected Data that you
can Query in Real-Time
Emerging
Applications
• Cross Sell
• Recommendations
• KYC Use Cases
• Risk Management
• Impact Analysis
• Compliance Traceability
• Data Governance
“Innovation Gap”
Applications depend
on your Master Data

8. Customer MDM
MDM-Systems
Employee MDMSupply Chain MDMProduct MDM
Data Warehouse
Data Lake
(Big Data)
GRAPH DB
Emerging
Applications
• Cross Sell
• Recommendations
• KYC Use Cases
• Risk Management
• Impact Analysis
• Compliance Traceability
• Data Governance
Applications depend
on your Master Data

9. UW Video - Part 1

10. Graphs & MDM in RETAIL

11. Front-end is eas, getting your data right is notFront-end is easy

12. Product Data
Real-time
(local) inventory
Promotions
Routing and
delivery
Real-time
Recommendations
Personalization
Geo-data
Payment
options
Social Data

13. Product Data
Real-time
(local) inventory
Promotions
Routing and
delivery
Real-time
Recommendations
Personalization
Geo-data
Payment
options
Social Data

14. Product Data
Real-time
(local) inventory
Promotions
Routing and
delivery
Real-time
Recommendations
Personalization
Geo-data
Payment
options
Social Data

15. Purchases
RELATIONAL DB WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE STORE
Product
Catalogue
DOCUMENT STORE
Category Price ConfigurationsLocation
Silos & Polyglot Persistence
Purchase ViewReviewReturn In-store PurchasesInventory LocationCategory Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory
Products Customers / Users
Location

16. Data Lake
Purchases
RELATIONAL DB
Product
Catalogue
DOCUMENT STORE WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE STORE
Recommendations require an operational
workload — it’s in the moment, real-time!
Good for Analytics, BI, Map Reduce
Non-Operational, Slow Queries

17. Purchases
RELATIONAL DB
Product
Catalogue
DOCUMENT STORE WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE STORE
Connector
Apps and Systems
Real-Time
Queries

18. Customer
Adress
Store
Phone
Customer
Email
EmailAdress
Phone
Product
Product
Category
Y
Street
Region
Product
Store
Street
Category
X
Simple Enterprise Graph
Customer Graph
Product Graph
Supply Graph

19. Customer Graph
Customer
Adress
Store
Phone
Customer
Email
EmailAdress
Phone
Product
Product
Category
Y
Street
Region
Product
Store
Street
Category
X
Product Graph
Supply Graph
Simple Enterprise Graph

20. Customer Graph
Customer
Adress
Store
Phone
Customer
Email
EmailAdress
Phone
Product
Product
Category
Y
Street
Region
Product
Store
Street
Category
X
Product Graph
Supply Graph
Power of Graphs - Unlock the power of Master Data
Real-time product
recommendations
Fraud
Detection
Real-time supply
chain management
Risk Management

21. Customer MDM
MDM-Systems
Employee MDMSupply Chain MDMProduct MDM
Data Warehouse
Data Lake
(Big Data)
GRAPH DB
Emerging
Applications
• Cross Sell
• Recommendations
• KYC Use Cases
• Risk Management
• Impact Analysis
• Compliance Traceability
• Data Governance
Applications depend
on your Master Data

22. “How Adidas is using Neo4j to deliver the most relevant
and compelling content to its consumers”
Case Study: adidas

23. “We speak different languages across domains”
CONTENT DIGITAL
ASSETS
ANALYTICS MASTER DATA
CONSUMER
DATA
PRODUCT DATA SOCIAL BIG DATA
Adidas’ Data Silos
adidas

24. PAUL POGBA
JUVENTUS
SOCCER
CONTENT
MEN
ITALY
CAMPAIGN
CONTENT
JERSEYFeatures Athlete
Plays
For
Features Campaign
Features Product
Related Content
For Market
For Gender
Features Sports Category
Content Graph
adidas

25. Shared Metadata Service
360° View of Customer
CONTENT
PRODUCT
DATA
MASTER
DATA
BRAND ASSET
DATA
adidas.com
CRM
Wholesale
Retail
Big Data
adidas

26. Strengths
• Native Graph data model naturally represents connections
between data — stitching together master data from multiple,
disparate sources with added flexibility of a schema optional store
• Real-Time query processing speed enables sense & respond
architecture model to operationalize master data; open sourced
CYPHER query language
• Complementary to existing enterprise MDM products — supports
use of best of breed MDM
• Enables Meta-data management and canonical models for data
integration; ease of mapping highly complex data intuitively
• Excellent technology for building Data Governance platform
including Data Lineage, Reference Data and versioning
• Subscription based, tiered pricing model
Key Customers
adidas
AMD
RBC
DIY MDM with Neo4j

27. 1. Gap in Current Landscape
2. Introduction to Case Study #1
3. Connected Data in Retail
4. Case Study #2
5. Closing the Insights to Action Loop
6. Revisit Case Study #1 & Demo
7. Distinguishing Features of Neo4j
8. Conclusion
Agenda
Nav Mathur
Sr. Director Global Solutions @ Neo4j
@nav_mathur, in/navmathur

28. How Neo4j closes
the loop between
Insight to Action

29. Customer MDM
MDM-Systems
Employee MDMSupply Chain MDMProduct MDM
Data Warehouse
Data Lake
(Big Data)
Batch Processed, Slow Queries
Highly Connected Data that you
can Query in Real-Time
Emerging
Applications
• Cross Sell
• Recommendations
• KYC Use Cases
• Risk Management
• Impact Analysis
• Compliance Traceability
• Data Governance
“Innovation Gap”
Applications depend
on your Master Data

30. The “Timeless" Pattern
Real-Time Processing
Recommendations
based on activity
from yesterday
Batch Processing
Overnight/Intermittent
Loading and Calculations
Results in lag between activity
& knowledge response
System-wide local pre-calculations
are computationally inefficient
Real-Time Writes &
Writes
Up-to-the-moment freshness
“Just-in-time” processing
most efficient for “local” queries
Recommendations
that reflects your
latest activity

31. Insight Action
The “Timeless" Pattern

32. Insight Action
“Data Warehousing/
Analytic/OLAP/Off-Line”
“Real-Time / Transactional/
Operational/OLTP”
Key Analytics Patterns

33. Insight Action
“Data Warehousing/
Analytic/OLAP/Off-Line”
“Real-Time / Transactional/
Operational/OLTP”
Key Transactional (‘Real Time’) Patterns

34. Insight Action
Data Professionals
Direct Access to Data
Customer + Employers
+ Autonomous Devices
Access via Applications
“Data Warehousing/
Analytic/OLAP/Off-Line”
“Real-Time / Transactional/
Operational/OLTP”
GRAPH
Neo4j closes the Insight to Action Gap

35. Data science could keep United out of more trouble
The right data analytics can sometimes prevent you from doing stupid things that
alienate your customers
- InfoWorld Headline
http://www.infoworld.com/article/3191285/analytics/data-science-could-keep-united-out-of-more-trouble.html

36. Money
Transferring
Purchases Bank
Services
Neo4j powers
360° view of
transactions in
real-time
Neo4j
Cluster
SENSE
Transaction
stream
RESPOND
Alerts &
notification
LOAD RELEVANT DATA
Relational
database
Data Lake
Visualization UI
Fine Tune Patterns
Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party
Data
Data-set used
to explore
new insights
Insights to Action at the speed of Business

37. Customer MDM
MDM-systmems
Employee MDMSupply Chain MDMProduct MDM
Data Warehouse
Data Lake
(Big Data)
Batch Processed, Slow Queries
Highly Connected Data that you
can Query in Real-Time
Emerging
Applications
• Cross Sell
• Recommendations
• KYC Use Cases
• Risk Management
• Impact Analysis
• Compliance Traceability
• Data Governance
Applications depend
on your Master Data
“Innovation Gap”

38. UW Video - Part 2

39. UW Demo

40. Distinguishing features of
Neo4j?

41. Neo4j: Right for the Enterprise
ACID Transactions
• ACID transactions with causal consistency
• Neo4j Security Foundation delivers enterprise-
class security and control
Hardware Efficiency
• Native graph query processing and storage
requires 10x less hardware
• Index-free adjacency requires 10x less CPU
Agility
• Native property graph model
• Modify schema as business changes
without disrupting existing data
Developer Productivity
• Easy to learn, declarative openCypher
graph query language
• Procedural language extensions
• Open library of procedures and
functions APOC
• Neo4j support and training
• Worldwide developer community
… all backed by Neo’s track record of
leadership and product roadmap
Performance & Scalability
• Index-free adjacency delivers millions
of hops per second
• In-memory pointer chasing for fast
query results
• Billions of nodes, hundreds of millions
of operations/day

42. Once you have master data that’s when the fun starts
The value of master data multiplies by connecting data silos together
Enabling innovative and disruptive uses of this connected data
creates true business value, helping to leapfrog the competition
(Example: Google, LinkedIn and Amazon)
Move from managing data to finding strategic uses of data, working
with enterprise architects and business leaders
Transform from a Data Managers / Scientists to “Data Strategists”
Conclusion

43. Data Strategist
Connects Data
Derives insights
Turns insights to action
• Data strategy development reverses the data
pipeline. It starts by asking what data and
insight are critical to the business’ growth.
• Data strategy should be aligned with the
business strategy, prioritized to address the
biggest opportunities and highest risks.
• Data strategist thinks like a business operator.
Credit: Victoria Zhang
Drives innovation

44. What Does MDM Have
to do with Innovation?
Everything!
Making data & insights available
at the right time and place
at the Speed of Business…

45. Register for a brown-bag graph talk with
your team @ https://neo4j.com/brownbag/
Spend 1 hr to discuss your project/initiative
with me and validate your solution
architecture / concept. Email me at
nav@neo4j.com, limited to first 5.
Nav Mathur
Sr. Director Global Solutions @ Neo4j
@nav_mathur, in/navmathur
Thanks!
Next Steps