Roadmap for Enterprise Graph Strategy

Roadmap for
Enterprise Graph Strategy
Michael Moore, Ph.D.
Executive Director, Enterprise Knowledge Graphs + AI
EY Performance Improvement Advisory
michael.moore4@ey.com
May 9, 2019

The Database Landscape is Changing
SQL RDBMS
Column
Document Key Value
Graph
SearchServerlessStreams In-Memory
Traditional Databases
& Data Warehousing
NoSQL Databases
Data Services & Data Processing
Batch MR Blockchain
2

Falling Memory Cost: 1990-2016 3

Scale Out  Scale Up
Continued increase in capacity and
dropping compute costs are challenging
scale-out commodity server assumptions,
particularly for database workloads
2018
4

Rankings Change in Popularity (db-engines.com)
*Proprietary method based on general interest, mentions, relevance in social networks, frequency of technical discussions etc.
Graph DBs
5

A graph is a visual abstraction
that describes how
different things are connected.
“If you can whiteboard it, you can graph it.”
What is a Graph?
“We send email to people, so they will visit our website and buy our product”
6

Graphs are composed of
nodes + relationships
Nodes represent entities
Relationships connect entities
“We send email to people, so they will visit our website and buy our product”
What is a Graph? 7

“We send email to people, so they will visit
our website and buy our product”
A Database specifically designed for creating, storing, and querying graphs
MATCH (e:Email)-[:SENT_TO]->
(p:Person {fullName: ’Steve Newman'})-[:VISITED]->
(w:Website)<-[:SOLD_ON]-(pr:Product)<-[:PURCHASED]-(p)
RETURN *
Semantic Representation
Graph Representation
Physical Representation
► Graphs have all possible logical relationships precomputed, much, much faster than SQL
► Graphs are fast and easy understand, develop and use
► Graphs integrate well with applications and data sources, great for real-time digital workloads
► Graphs surface, unify and mobilize data held in silos and data lakes
What is a Graph Database? 8

Graph Use Cases
► Customer 360°
► Recommendation
Engines
► Marketing Attribution
► Enterprise Search
► Fraud Detection
► Master Data
Management
► Supply Chain
► Geolocation & Routing
► Access & Asset Control
► Social Networks
► IT & Network
Management
13

Real-Time, Evolving Graph View Across the Business
Data Ingestion, Cleansing, Reduction & Pipelining
Real-time BI & ScorecardsMobile & Web Applications Data Science
access control, metadata, recos, monitoring KPIs, targets, reporting, drill down/across attribution, similarity, fraud, pathing, cliques
Marketing ROI &
Digital Experience (CMO)
Data Governance &
Data Quality (CDO)
Operations & Risk
Management (CFO)
Account Coverage &
Customer LTV (CRO)
Product Marketing &
Recommendations (CPO)
UNSTRUCTURED LEGACY SNAPSHOTS
CONFORMED &
CURATED
STREAMS
Graphs Accelerate Enterprise Data Mobilization 14

15Roadmap for Enterprise Graph Strategy
Small Team:
• Graph Architect
• Data Engineer
• Full-stack Developer
• Data Scientist
• Report Developer
Problem / Scope
What will the graph
solve?
Production BuildCloud PilotLocalhost POCGraphy Problem
Business need, Data sources Data modeling, API, example queries Data snapshot, reference architecture, API suite Hardening, scheduled & stream ETL, Live UX
Stakeholder Input
Graph Design
Data Work
APIs / Data Services
Integration / Refinement
Scale / Harden / Run
Validate
What questions can now
be answered?
Connect
Does the data support the
graph model and
semantics?
Mobilize
What data does the new
experience need?
Use Cases
What is the feedback
from the business on how
well the graph solves the
use case?
Deploy
What monitoring, testing,
process needs to be put
in place to achieve a
robust SLA?
Key Conversations

Talk to the business, pick a graphy problem
What is a “Graphy” problem?
• Requires many entities (eg many SQL tables, 360° views)
• Involves recursion (eg. SQL self joins)
• Has complex, potentially colliding, hierarchies (eg SQL 1 to many, many-to-many)
• Based on informatics of the relationships themselves (eg collaborative filtering shared
relationship counts, shortest path segment summations for wayfinding, cost/time
minimization for supply chain, money flows for finance)
• Requires mapping, direct or indirect across data sources (eg data lake unification)
• Demands fast query results (eg digital applications, search)
• Most importantly, go talk to the business – what are the analytics you’d like to have or
customer experiences you’d like to light up – but can’t because of our current data
limitations?
• What’s the most critical data that you’d like to see connected?
• What would be an example demo that you’d find compelling (report/analysis/experience)
16

Get comfortable with Neo4j – don’t need to become an expert
• Get hands on – be fearless! Neo4j is the easiest graph database to
learn.
• Install Neo4j, Apoc procedures, set the following in Manage/Settings
#Apoc Plugin Configurations
apoc.import.file.enabled=true
apoc.export.file.enabled=true
dbms.security.procedures.unrestricted=*.*
• Go through the Cypher lessons, and learn basics graph modeling and to
load csv
LOAD CSV WITH HEADERS FROM "file:///movies.csv"
AS row
CALL apoc.load.csv(url,{}) YIELD map
• Any reasonably sized laptop should be able to handle a graph with
several million nodes and relationships You will quickly see some of the
significant benefits of connected data.
• For extra credit you can go onto github/neo4j-examples and download
starter applications for your favorite languages.
17

Design and build your POC Graph
• Start small and simple, limit yourself to 3-4 data sources, shallow extracts.
Snapshot SQL top queries for a pool of linked transactions
• Use common sense, business-friendly naming for your node labels and relationship
types. You’ll iterate this model using input from the business, and the model
should be clear and readable
• Don’t be afraid of recursion
(Employee)-[:REPORTS_TO]->(Employee) who is the boss?
• Don’t get too hung up on whether something should be a node label, property, or
relationship. Just keep in mind that node labels define set members, and that it’s
faster to search along relationships (traversal) than properties (full graph scan)
• You can use call db.schema() to see the graph schema, and we often use
http://apcjones.com/arrows/# to build illustrative schemas for conversations with
business stakeholders
• Test your graph design by writing some example queries, do this with your business
stakeholder
• Does this look right to you – is this how you would whiteboard this process? Am I
missing any key entities or relationships?
18

Example Knowledge Graph Schema
for Spend and Supply Chain Analytics
Supplier 360°
Spend Graph
• Accurately captures the
sourcing complexity of products
and services
• Enables more insightful indirect
spend analytics for products
and services
• Reconciles line-item detail to
top parent company, across
intermediate entities
• Extensible for audit, fraud
detection, tracking &
traceability
• Integrates with data lake,
reporting platforms and
transactional applications
Product Supply Chain Service Providers
Procurement
Top
Parent
Line
Item
Detail
Tracking and Traceability
Invoicing
Data fabric composed of nodes and relationships that
connect and mobilize data, using consistent semantics
19

2
Example Customer 360° Graph Schema
Product Supply Chain
Account
Transactions
Segments
Product
Interactions
20

Example B2B MDM Graph Schema
Product
Core Data Elements
Customer
& Contact
Orders
21

Example Polyglot Discovery Graph Schema
Searchable Pointers to
Unstructured blobs
Text & Metrics from
Semi-Structured data
Structured Data and Derived Entities
22

Design and build your POC Graph 23
• Breakthrough queries
• Graph algorithms
• Data unification & mobilization
• Use-case specific (Customer 360, Supply Chain, Fraud, Reco)
• Make a localhost graph->app stack so you understand how
parameterized Cypher & Bolt drivers work
• Use any of the neo4j-examples to jumpstart
• If you don’t want to spend time creating a REST API, check out
GraphQL and the GRAND stack (https://github.com/grand-
stack/grand-stack-starter)
• Focus on the business value of the new graph enabled analytics –
We can now know this to make better decisions
We can now do this for our customers

24Neo4j - Power BI Integration with GraphQL
Graph Database
Neo4j GraphQL API
2
3
4
1. Client issues GraphQL query
2. GraphQL API sends Cypher query to Neo4j
3. Response data sent to Client
4. Data updated in PBI report
GraphQL schema, registered in Neo4j
m query cURL wrapper
PBI report
1

Neo4j – React Integration with GraphQL (GRAND Stack)
25

Pick and build your demo application for your snapshot graph 26
• Pick a cloud or on-prem
• Use Marketplace images if possible
• Start with a single instance VM for Neo4j, (~ RAM 50% of SQL size)
• Attach external drives so you can scale the server
• Determine your stack architecture
• Understand your data processing requirements
• Install Python – very good for performing batch operations, pip neo4j-driver
• Leverage Neo4j’s high speed loader
• Determine what cleansing needs to occur
• If you need help reach out to SI partner or Neo4j services

Pick and build your demo application for your snapshot graph 27
• MVP data domains
• Graph database, app-informed
• Simplest data service
• MVP app experience
• Add new experiences, same data
• Add new data domains
Nodejs, .Net, Python, React, Swift, Tableau, etc.
REST, Bolt
Michael’s I-Frame model For Graph ROI

Example Cloud Architecture
for Sales & Marketing
Microsoft, SAP & Adobe are partnered on the Open Data Initiative (ODI)
Snowflake and Adobe announced partnership
Blobs FilesQueuesTables
Azure Cloud Storage
AI Sandbox
(Azure ML Studio)
Stream ETL
(Azure Event Hub)
Audience
Manager
Campaign
Target
Experience
Manager
Analytics
Marketo
Engage
Adobe Experience Cloud (Azure)
Scheduled
ETL
Data
Reduction
(Azure Spark)
Cloud Data Lake
In-Memory
Document Store
Web / Mobile Applications
Data Catalog
(Azure Data
Catalog)
AZURE VPC
In-Memory
Knowledge Graph
Data Services APIs
REST
Ingest Batch
StoreIngest Real-time
SearchConsolidate
Connect & Unify
Detect & Recommend
Mobilize
Execute
Reporting
(Tableau, PBI)
Semantic
Layer
Analytics
Layer
ADF
Data Models
(Azure Analysis
Services)
Analyze
Discover Predict
Reduce
Standardize
Sales
Marketing
Product
28

Example Graph Data Processing – Structured, Semi-Structured, Unstructured
Extract XML,
Convert to JSON,
Load JSON with
Azure Blob URI
Extract and Load
Azure Blob URIs
Extract and Load
• Document Metadata
• Named Entities
• Map Relationships
• Text Summaries
Graph Analytics & Queries
Graph Full Text Search
Couchbase Full Text Search
Pointers to Azure Blob URIs
Graph storage for unified many-to-many access to cross-domain data
Document storage for searchable access to semi-structured data
Blob storage repository for large, raw and unstructured data
blobs
Unstructured:
Semi-Structured:
Load CSV to Graph
Structured:
JSONs
Reports/Applications
Data Mobilization and Graph Unification – Full Lineage and Auditability
nodes & relationships
29

Enterprise Knowledge Graph Development with Neo4j
• Locate and validate data lake tables
• Design test graph schema
• Estimate graph size from nodes, relationships and properties
• Configure Neo4j server to minimize SSD disk contention
• Prepare Hive queries to generate graph-form tables (nodes, relationships)
• Validate key uniqueness, string handling, character types, relationship mappings
• Export graph form tables to gzip csv files
• Iteratively test data loader scripts, file by file
• On successful completion of hydration, apply constraints and indexes, refactor as needed
Graph-form TablesData Lake Tables CSV.gz Files Load Script Data Store
EXTRACT EXTRACT HIGH SPEED LOADER
IMPORT DONE in 1h 29m 16s 530ms.
Imported:
458356377 nodes
2176603843 relationships
9064981812 properties
Peak memory usage: 9.46 GB
30

Knowledge Graph Analytics
Graph
Reasoning
Graph
Search
Graph
Deep Learning
Graph
Topology
“A knowledge graph acquires and integrates information into an
ontology and applies a reasoner to derive new knowledge.”
- Johannes Kepler University, Linz Austria
31

Example Cloud Architecture for Graph Analytics Sandbox
AWS virtual private cloud
Amazon
S3
AWS CLI
Distributed
Processing
Staged Data for
Graph Loading
Amazon EC2
Sandbox r3.8xlarge
32 CPU, 244GB RAM, RHEL
root
2x320GB
/ext/import
1TB
/ext/data
1TB
Structured Tables
& Raw JSON
Amazon EMR
• Neo4j Browser
• Python & R scripting (Bolt)
• Data Services APIs (Bolt)
• Business Intelligence
• Mobile and Web applications
• ERP integration
• Flat file & Streaming updates
• Graph Analytics
• Machine Learning
• Data Visualization
• Automation
VPN
Virtual
Machine
32

Go to Production 33
• Follow your IT best practices
• Security, assume you’ll be breached
• Deploy full environment set – Prod cluster, Stg cluster,
Test, Dev
• DevOps - leverage Jenkins, Ansible
• Wrap your solution in test automation
• Do load testing against your APIs to look for additional
optimization opportunities (Gatling)
• Monitor your logs (Splunk, Dynatrace)
• Monitor your common queries, refactor or reindex as
needed
• Leverage the I-Frame model to provide more value

34Roadmap for Enterprise Graph Strategy
Small Team:
• Graph Architect
• Data Engineer
• Full-stack Developer
• Data Scientist
• Report Developer
Problem / Scope
What will the graph
solve?
Business need, Data sources Data modeling, API, example queries Data snapshot, reference architecture, API suite Hardening, scheduled & stream ETL, Live UX
Stakeholder Input
Graph Design
Data Work
APIs / Data Services
Integration / Refinement
Scale / Harden / Run
Validate
What questions can now
be answered?
Connect
Does the data support the
graph model and
semantics?
Mobilize
What data does the new
experience need?
Use Cases
What is the feedback
from the business on how
well the graph solves the
use case?
Deploy
What monitoring, testing,
process needs to be put
in place to achieve a
robust SLA?
Key Conversations

EY Cross-Sector Graph Experience: MDM, 360°, AML/Fraud, Recommenders 35
Fortune 100 Tech Company
Use Case:
Global B2B Account 360° view and
marketing attribution
Approach:
Neo4j graph with 500M nodes
and 2.2B relationships,
representing all known business
accounts, contacts and marketing
touches. Mastered data from
17disparate transactional sources
in Azure Data Lake. Supported in-
graph analytics for marketing
attribution and next best action
recommendations across global
geographies
Duration:
16 weeks to working graph
Fortune 100 Footwear Company
Use Case:
Converged Brick & Mortar +
Online Shopper 360° View
Approach:
Neo4j graph with 2B nodes and
relationships, representing sales
transactions for 40M shoppers
across 275 physical stores and the
ecommerce platform. Algorithmic
extraction and profiling from raw
XML records in AWS Hadoop,
MDM record concordance and in-
graph analytics for product
associations, store analytics and
recommendation services.
Duration:
12 weeks to working graph,
ongoing project through 2018
Fortune 500 Cruise Line Company
Use Case:
Shipboard and Shoreside
Recommendation Engine
Approach:
Neo4j graph deployable to
shipboard VM Ware data centers,
with streaming updates from
large shoreside Neo4j graph
integrating data from Azure
Cerebro, Adobe Experience
Manager and legacy transactional
systems. In-graph
analytics,services API,
recommendation engine for next
best activity for passengers
surfaced via mobile app
Duration:
12 weeks to working graph,
ongoing project through 2018
Fortune 100 Investment Firm
Use Case:
Enhanced Anti-Money Laundering
and Fraud Detection using
Graph+AI
Approach:
Neo4j graph of account 360° view
representing activity of 2M
accounts over 4 years. MDM and
entity extraction for account and
party identity elements from
enterprise Oracle system.
Network clustering, feature
engineering and graph embedding
in TensorFlow deep learning
classifier for suspicious activity
patterns across accounts and
between parties.
Duration:
Fortune 100 Tech Company
Use Case:
B2B Local Marketing Events
Recommendation Engine
Approach:
Neo4j graph and personalized
next best event recommendation
engine for B2B field marketers.
Reconciles physical and digital
event attendees with corporate
account structures for 10K
accounts and 5M contacts
Entities mastered from
transactional data in SQLServer
and Azure Data Lake.
Microservices APIs support data
syndication to martech
applications and PowerBI
reporting.
Duration:

Better Questions
How can I get more business value and deeper
insights from the data I already have?
How can I get a better understanding of my customers to
create more relevant experiences?
How can I more effectively mobilize and
syndicate the data I’m ingesting?
What is the next best action I can take?
Thank
You!
36

Michael Moore, Ph.D.
Executive Director
► Michael Moore is an Executive Director and Practice Lead for Graph + AI
in EY’s Tech Consulting Emerging Technology (ET) Group
► Joined EY in 2017, based in the Seattle, WA office
► Ph.D. University of California, Berkeley
► B.S. & B.A. University of California, Santa Cruz
► Society Consulting – Graph Architect
Schema, ETL & systems design for a high-performance Neo4j graph database encompassing the totality
of Microsoft’s B2B data on Azure VM. Graph database supports multi-touch marketing attribution
analytics and multi-dimensional event-based audience segmentation & recommendations for direct
marketing. Provided POC graph reporting and visualization interfaces. Neo4j Enterprise edition, Python,
Node.js, nGraph, Javascript.
► Microsoft Corporation – General Manager
Management of core BI infrastructure and measurement capabilities supporting Microsoft's global
marketing budget cascade, campaign reporting, pipeline reporting, incentive reporting, ROMI reporting,
social and web analytics on Microsoft.com for the Global Marketing Operations team. Management of
complex projects across multiple subsidiaries, agencies and vendors. Strategic focus on foundational
database, digital and social marketing capabilities including: marketing ROI, customer & channel partner
engagement, marketing conversion, sales pipeline, dynamic personalization, data mining, predictive
modeling, behavioral segmentation, privacy governance, web enablement, tracking & measurement,
and internal & external data quality, and instrumentation process control.
► Grey San Francisco – VP Analytics
Responsible for ongoing campaign reporting, ROI analysis, creative and placement optimizations for
agency clients. Architected and deployed an enterprise OLAP reporting solution on Oracle RAC /
Microstrategy to improve quality and efficiency of analytics operations. Provided advanced analytical
services to clients in retail, tech, banking and automotive, including consulting, regression modeling and
data mining.
Profile Select professional experience
Skills and tool knowledge
► Michael Moore, Ph.D. is an Executive Director in the Advisory Services
practice of Ernst & Young LLP. He is the National practice lead for
Enterprise Knowledge Graphs + AI in EY’s Data and Analytics (DnA) Group.
► Michael has industry and solution in customer experience, customer
service, e-commerce, ad-serving, web and media analytics, consumer
loyalty and churn, marketing optimization, enterprise and partner pipeline,
and social media
► He specializes in graph database architecture, graph-based advanced
analytics, machine learning and recommender systems. Michael is certified
Neo4j Professional, and has active enterprise graph engagements in
financial services, tech, oil & gas, retail and hospitality sectors.
37

Roadmap for Enterprise Graph Strategy

More Related Content

What's hot (19)

Similar to Roadmap for Enterprise Graph Strategy (20)

More from Neo4j (20)

Recently uploaded (20)

Roadmap for Enterprise Graph Strategy