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© 2018 Lockheed Martin Corporation. All rights reserved.
CQSDI 2018, Cape Canaveral, FL
ANALYTIC SOLUTIONS WITH
DISPARATE DATA
John Schroeder and Chad HallLockheed Martin Aeronautics
Enterprise Integration Advanced Analytics
© 2018 Lockheed Martin Corporation. All rights reserved.
We see disparate data sources as a business process problem.
The analytics process defines how the data needs to come
together.
© 2018 Lockheed Martin Corporation. All rights reserved.
• Managing Disparate Data• Organizational Influences
• Early Identification
• Analytics Phases of Development
• Architecture Considerations
• Tool Considerations
• Human Resources example
• Quality Analytics Solution example
OVERVIEW
© 2018 Lockheed Martin Corporation. All rights reserved.
ORGANIZATIONAL INFLUENCES
Enterprise Integration
Advanced Analytics Process Excellence
Managing disparate data is as much about the organization
and its processes than anything else
© 2018 Lockheed Martin Corporation. All rights reserved.
ADVANCED ANALYTICS CAPABILITIES
Capability Description
Computer-coded, rules based software that automates manual activities by performing repetitive rules-based tasks; can be interspersed with human checkpoints at key milestones or for exception management
An application of machine learning grounded in statistical inference that enables computer interpretation of various forms of human language (text, images, or speech)
Advanced analytics characterized by their ability to continuously learn from training data rather than relying on a static ruleset; these algorithms detect patterns in data and adjust program actions accordingly, whether supervised or unsupervised
The ability to synthesize layers of complex datasets to create multidimensional visual representations of decision-support tools, outputs, dashboards, KPIs, and reports; advances include the transition to multi-platform, mobile-enabled
Engagement of sensors and other digital observation technology (e.g. RFID) to convert non-electrical inputs/events into digital information for analysis and decision making
ARPA & Cognitive
Automation
MLMachine
Learning
NNatural Language
Processing (NLP)
VAdvanced
Visualization
D Digitization
© 2018 Lockheed Martin Corporation. All rights reserved.
EARLY UNDERSTANDING OF DISPARATE DATA
BUSINESSPROBLEM
Identification Prioritization and Selection
Planning & Development
Test & TrainEnhance & Maintain
Product Loop
Early identification of disparate data necessary for project prioritization and planning
© 2018 Lockheed Martin Corporation. All rights reserved.
ANALYTICS PHASES OF DEVELOPMENT
Hypothesis
Can we scale the solution?
Build & roll-out
application
Confirm business value?
Prototype Industrialize O&M
Maintain &Enhance
Solution Completeness
DisparateData
Considerations
• Speed• Resources
• Cadence• Architecture
• Requirements• Testing• Scalability
• Troubleshooting• Maintenance• Resources
© 2018 Lockheed Martin Corporation. All rights reserved.
ARCHITECTURE CONSIDERATIONS
Organize Analyze Deliver
Analytic
Capabilities
Analyze
Optimize
Forecast
Report
Plan
Discover
Collaborate
Predict
Model
Tem
pora
ry S
tora
ge
Self-Service
Data
Preparation
Vir
tua
liza
tio
n
Logical Data Warehouse
Traditional
Database
In-Memory
Columnar
“Big Data”
Distributed
ProcessingData
Integration
Transform
Aggregate
Data Sources
Streaming / In Motion
Staging / At Rest
External
Operational Systems
Acquire
IOT
Data Governance
Business Objects
Mobile Display
Visuals
Analytic Dashboard
Advanced Analytics
Connections
Services
Delivered Reporting
Self Service&Data Science
Opportunity to blend disparate data across entire architecture
© 2018 Lockheed Martin Corporation. All rights reserved.
TOOL CONSIDERATIONS
Organize Analyze Deliver
Analytic
Capabilities
Analyze
Optimize
Forecast
Report
Plan
Discover
Collaborate
Predict
Model
Tem
pora
ry S
tora
ge
Self-Service
Data
Preparation
Vir
tua
liza
tio
n
Logical Data Warehouse
Traditional
Database
In-Memory
Columnar
“Big Data”
Distributed
Processing
Data
Integration
Transform
Aggregate
Data Sources
Streaming / In Motion
Staging / At Rest
External
Operational Systems
Acquire
IOT
Data Governance
Business Objects
Mobile Display
Visuals
Analytic Dashboard
Advanced Analytics
Connections
Services
Delivered Reporting
Self Service&Data Science
© 2018 Lockheed Martin Corporation. All rights reserved.
DISPARATE DATA CONSIDERATIONS:
A HUMAN RESOURCES EXAMPLE
© 2018 Lockheed Martin Corporation. All rights reserved.
HUMAN RESOURCES BUSINESS PROBLEM
Where is the supply of talent and will it meet our needs?
What is the hiring lead time required to source and train talent?
Are resources available to train new hires?
Do forecasts adequately account for production floor volatility and risk?
Do people “safety stocks” have adequate buffer to account for time and forecast based volatility?
© 2018 Lockheed Martin Corporation. All rights reserved.
THE PROBLEM LANDSCAPE
Talent Need People Safety Stock
L&DTalent Pipeline & Lead Lime
Environmental Scan
Not surprisingly, each team’s data doesn’t talk to one another
Talent Acquisition Workforce Planning Learning & Development
Pipeline
RatiosStaffing
Plans
Advanced
ModelTraining
Capacities
The
Process
Teams
Data
Database
Database
3rd party data
© 2018 Lockheed Martin Corporation. All rights reserved.
What is the right course when:
• The data is in many different places, and in different formats
• The objective is clear, but the “solve” isn’t
• We can’t wait for a fully baked IT solution
CONSIDERATIONS
© 2018 Lockheed Martin Corporation. All rights reserved.
EXAMPLE SOLUTION SET
Visualize InsightsAnalyticsData Ingestion, Cleaning,
Blending Modeling
• ETL functions in the power of the analytics professional
• Connects and combines disparate data sources
• Integration with R Studio, Python, and Statistical Analysis
• Advanced statistical analysis
• Forecasting
• Visualize analytics
• Publish and share on Tableau server
© 2018 Lockheed Martin Corporation. All rights reserved.
DISPARATE DATA CONSIDERATIONS:
A QUALITY ANALYTICS SOLUTION EXAMPLE
© 2018 Lockheed Martin Corporation. All rights reserved.
QUANTUM uses natural language processing and machine learning to analyze a population of non-conformance text documents to connect the dots quickly and accurately to other related non-conformances
QUANTUM(Quality Analytics Text Unstructured Mining)
© 2018 Lockheed Martin Corporation. All rights reserved.
QUALITY ENGINEERING PROCESS
Quality Engineering Process
Corrective Action Decision
Potential Issue Identification
Issue Investigation Launch
Root Cause Analysis
Issue Investigation Close Decision
Corrective Action Execution
Review repetitive non-conformance categorization
Review repetitive part numbers
Pull documents
Read
Process
Assess
Identification of defect causality
Pursue leads via disparate data sources
Generate insights, think more broadly
New non-conformances – continually adjust
Connect the dots
Quality Engineering Process
© 2018 Lockheed Martin Corporation. All rights reserved.
CONNECTED DISPARATE DATA
Data Products
Apply Artificial Intelligence to disparate
information sources to align engineering
support with the most significant business
impact
Provide comprehensive analytics solution set,
enabling engineering to go directly to the
problem solving process
Visualize results from engineering change
activity with operations performance outcomes
Deliver Return-On-Investment guidanceCorrection
DOCs
Request Logs
QAR Logs
Field Logs
Performance Metrics
Change DOCs
Connected Disparate Information SourcesBusiness Objectives
Quality
ENGR RequestEngineering
ENGR Change
Corrective Action
Operations
Sustainment
© 2018 Lockheed Martin Corporation. All rights reserved.
NATURAL LANGUAGE PROCESSING
Words GrammarPart of Speech Tagging Meaning of Words in Context
Meaning of Whole is Built from its Parts
Text: John likes to watch movies.Noun (NN)
Pronoun
Proper Noun (NNP)
Adjective
Verb (V)
Adverb
Preposition
Interjection
Conjunction
Syntactic Analysis: John / NNP likes / V to watch / V movies / NN.
Semantic Analysis: John / Person likes to watch movies / Thing.
Pragmatic Analysis: Social Conversation
A computer cannot understand text, but it can simulate understanding. To do so it needs to understand the rules of natural language.
John likes to watch movies.
Mary likes movies too.
John also likes to watch
football games.
© 2018 Lockheed Martin Corporation. All rights reserved.
MACHINE LEARNING
Determine whether a home is in San Francisco or New York
• Uses features (e.g. elevation) to categorize data
• Adding features for further distinction
• Find relationships between each pair of dimensions
• Machine learning methods use statistical learning to identify patterns
• Clustering - groups based on inherent features
Elevation
Elevation
Year Built
Bathrooms
Bedrooms
Price
Square
Feet
Price / sq. ft.
S.F.
N.Y.
Price / SQ FT
The computer learns as more data is providedReference: www.r2d3.us/visual-intro-to-machine-learning-part-1/
© 2018 Lockheed Martin Corporation. All rights reserved.
QUANTUM SOLUTION SET
Visualize InsightsAnalyticsData Ingestion, Cleaning,
Blending Modeling
• Scalable ETL in production environment
• Connects and combines disparate data sources
• Integration with R
• R: Advanced clustering algorithms
• HANA: text libraries
• Visualize analytics
• Publish and share on Tableau server
© 2018 Lockheed Martin Corporation. All rights reserved.
BUSINESS RAMIFICATIONS
Do More Corrective Action with Less Time
Align Resources to Effect Greater Business Costs
Improved Customer Satisfaction