analytic solutions with disparate data -...

© 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


We see disparate data sources as a business process problem.

The analytics process defines how the data needs to come

together.


• Managing Disparate Data• Organizational Influences

• Early Identification

• Analytics Phases of Development

• Architecture Considerations

• Tool Considerations

• Human Resources example

• Quality Analytics Solution example

OVERVIEW


ORGANIZATIONAL INFLUENCES

Enterprise Integration

Advanced Analytics Process Excellence

Managing disparate data is as much about the organization

and its processes than anything else


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


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


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


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


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


DISPARATE DATA CONSIDERATIONS:

A HUMAN RESOURCES EXAMPLE


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?


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


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


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


DISPARATE DATA CONSIDERATIONS:

A QUALITY ANALYTICS SOLUTION EXAMPLE


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)


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


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


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.


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/


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


BUSINESS RAMIFICATIONS

Do More Corrective Action with Less Time

Align Resources to Effect Greater Business Costs

Improved Customer Satisfaction

analytic solutions with disparate data -...

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