The document discusses how Lockheed Martin is leveraging graph technology in their data management strategy. It provides an overview of Lockheed Martin as a company with over 110,000 employees and 375+ facilities. It then discusses how graph databases can be used to create a master data graph that connects different types of data like product data, manufacturing data, supply chain data, and more. This allows for insights into relationships across domains and enables data-driven decisions. The benefits of a graph approach are also summarized as gaining new insights, enabling complex relationship questions, and supporting master data management and data science.

1. LOCKHEED MARTIN PROPRIETARY INFORMATION
Graph Government Days, March 5, 2020
How to Effectively Leverage Graph Technology in Your Data
Management Strategy
MASTER DATA GRAPH
Ann Grubbs
Space Chief Data Engineer
© 2018 Lockheed Martin Corporation. All Rights Reserved.

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Lockheed Martin

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Lockheed Martin

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Lockheed Martin

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Lockheed Martin
110,000
Employees
375+
Facilities
Bethesda
Maryland

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Lockheed Martin
110,000
Employees
375+
Facilities
Bethesda
Maryland

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Lockheed Martin Space
GOES
GPS III

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Lockheed Martin Space
GOES
GPS III

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Lockheed Martin Space
GOES
GPS III

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Lockheed Martin Space
GOES
GPS III

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Work Biography
People
Management
Graph
Evangelist
Database
Developer
Project
Management
Data
Engineering
Software
Development
Data
Architecture

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Real Me

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Real Me

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4th Industrial Revolution

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Digital Transformation
Industries are undergoing major transformations.
Personal
Monitoring
and
Controlling
Agriculture
Automation
Energy and
Security
Smart Homes
and Cities
INTERNET
OF
THINGS
INTERNET
OF
THINGS
Everyday
Things
Only thing certain in life is death,
taxes, and the proliferation of data

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Data Volume Explodes

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Eng Quality Test IOT
Data Volume Explodes
Supply
Chain

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Eng Quality Test IOT
Data Volume Explodes
Supply
Chain

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Product DNA
System Models Physics Models Program Performance
Mechanical Models Electrical Models
Knowledge and Expertise
Cost DataTest Data
Sustainment Models

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4 Styles of Master Data Management
Registry
Coexistence Consolidation
Centralized

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Registry Style - Master Data Management
In the registry style, the Systems of Record for
master data are the source systems. That is,
changes to master data continue be made
through existing source systems – data
ownership does not move to the MDM hub. Only
enough information to match and provide the
linkage between very similar or matching records
is stored and a trusted view of this information is
derived and supplied to the consumer on
demand. In this style, a single view of the party
is not persisted – master data is owned and
modifications to it continue to be made via the
source systems.

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MasterData
MasterData
MasterData
MasterData
System Models Physics Models Program Performance
Mechanical Models Electrical Models
MasterData
MasterData
MasterData
MasterData
MasterData
Knowledge and Expertise
Cost DataTest Data
Sustainment Models
Product DNA

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MasterData
MasterData
MasterData
MasterData
System Models Physics Models Program Performance
Mechanical Models Electrical Models
MasterData
MasterData
MasterData
MasterData
MasterData
Metadata Repository / Microservice Catalog
Data Governance / Data Protection
Knowledge and Expertise
Cost DataTest Data
Sustainment Models
Product DNA

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Final Result

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Master Graph Model Example
Design
Quality
Supply
Chain
Mfg
Part

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Master Graph Model Example
Design
Test
Part
Inspect
Contract
PO
Program
Supplier

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Master Graph Model Example
Part
Test
Design
Inspect
Program
Contract
PO
Supplier

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Data Graph Benefits
Product
Structure
Release
Package
Change
Master
Manufacturing
BOMs
Manufacturing
Plan
CAD Model
Production
Order
Manufacturing
Work
Instructions
Shop Floor
Fabrication
Parts List
Gain New Insight into the Data Landscape.
Efficient Analysis for the Complex Relationship Questions
Enable Data Driven Decisions
MASTER DATA MANAGEMENT
DATA SCIENCE
CROSS DOMAIN ANALYTICS

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Data Graph Benefits
Provide the ability to conduct what-if analyses. How do changes to the design affect
manufacturing? Are investments in advanced design models worth the cost because it
improves improve quality? What happens to the supply chain and project schedule if
there is an event at a location?
Define the DNA of a Product
By extracting the relationships in our data across all domains and storing these
relationships we can solve business problems that require a broad understanding of
the business entities that we manage.
Identify Correlations or Inter-Relationships
Many tools provide the taxonomy (parent child relationship) but our data was really
an ontology (infinite number of relationship types). Business context can not be
adequately described at parent child.
Determine Influences
Cost, schedule, quality dimensions of our data
Provide the ability to understand how business entities influence from one domain
to another domain.

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Insights in the Data Network
• By creating a graph of the data sets we create a data network that provides the
capability to analyze the function of the system and the relationships of the data
sets
– Where is there redundancy in the data
– Where are there gaps in the system
– Where are the related data sets in the system
– Where are the roadblocks in the flow of data
– How efficient is my system
– How can we make the system more efficient

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Polyglot Persistence Model
TRANSACTIONAL SYSTEMS
ENTERPRISE DATA WAREHOUSE / DATA LAKE
BUSINESS
GLOSSARY
MASTER
DATA
MICROSERVICE
PLATFORM
Multiple Ways to Store
and Retrieve Data–
the Right Tool for
the Right Job!

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DataHub

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MISTAKES ARE MEANT FOR
LEARNING, NOT REPEATING
Do not model based
on the data only
Need to understand the
questions – what are you
trying to solve?
Only store the data needed to traverse
relationships or to answer specific
relationship questions
Polyglot persistence
is key – right tool for
the job
Get help, very different approach -
Donner party vs Lewis and Clark
Don’t be afraid to throw
it all away and start over
Careful, thoughtful,
systematic approach
You could get a lot of push
back from leaders or peers –
be willing to invest in risk

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• Business driven Data Management Initiative
• Understand the state of the data first
• Define the data
• Find the master data sets and where the data sets intersect
• Find the data owners
• Prepare for Data Governance
• Org based on data set in prioritized business cases
• Opportunity learn with small group prior to rolling out across
enterprise
• Define a process to manage policies
LMPI/ECI MARKING BANNER, IF REQUIRED
Don’t Try to Boil the Ocean

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KEEP
CALM
Seek
Guidance
I’M AN
EXPERT