Data ManagementB i g D a t a C h a l l e n ge s a n d O p p o r t u n i t i e s i n Tra n s i t

Ro b Ay e rsP re s i d e n ta E

About UsA y e r s E l e c t r o n i c S y s t e m s , L L C ( a E )

• Technology Company Founded in 2008

• Based in Richmond Virginia

• Provider of Standards-Based Products and Services

• Customer Base is Primarily Surface Transportation

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Agenda

➢Big Data Concepts/Technologies

➢Big Data Trends

➢Transit Data Sources

➢Transit Opportunities

➢Transit Challenges

Big Data C o n c e p t s a n d Te c h n o l o g i e s

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Concepts & Technologies

➢Data Repositories

➢Data Marts/Data Warehouses

➢Data Mining

➢Data Lakes

➢ Internet of Things (IoT)

➢Data Governance & Cybersecurity

Data Repository“ D e s t i n a t i o n d e s i g n a t e d f o r d a t a s t o r a g e ”

➢ Structured Data from Multiple Source Systems

➢ Data may be Normalized for Distribution

➢ Break down “Silos”

➢ Makes Enterprise Data Available to Multiple Consumer Applications

➢ Transit Data Examples: Schedules, Vehicle Data, Stoppoint Inventory, Assignment Data

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Data Warehouse“ S t o r e o f d a t a f r o m a w i d e r a n g e o f s o u r c e s u s e d t o g u i d e m a n a g e m e n t d e c i s i o n s ”

➢ Structured Data from Multiple Source Systems

➢ Data is “Cleaned” & “Transformed” on Ingestion

➢ Makes Enterprise Data Available to Multiple Consumer Applications

➢ Optimized for Canned and Ad Hoc Reporting on Large Data Sets

➢ Includes Analytics Package to Create Dashboards & Reports

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Data Mining“ D i s c o v e r i n g p a t t e r n s a n d r e l a t i o n s h i p s i n l a r g e d a t a s e t s ”

➢ Sequence of Processes not a System

➢ Structured Data from One or Multiple Source Systems

➢ Data Selected Organized Based on Type(s) of Analyses to be Performed

➢ Generally More Focused Analyses than Data Warehouse

➢ Many Types of Analyses Used

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Data Lake“ S y s t e m o r r e p o s i t o r y o f d a t a s t o r e d i n i t s n a t u r a l f o r m a t ”

➢ Unstructured & Structured Data

➢ Schema on Read

➢ User Integration

➢ Generally Need Data Scientists

➢ Often used Distributed Storage

➢ Huge Datasets

➢ Beware the “Swamp”

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Internet of Things (IoT)“ I n t e r n e t c o n n e c t i v i t y f o r c o m p u t e r s e m b e d d e d i n e v e r y d a y d e v i c e s ”

➢ “Everything Everywhere”

➢ Provides monitoring and/or control

➢ Security/Privacy are ongoing concerns

➢ By 2020 Cisco predicts there will 7 times as many devices connected as people

➢ Data volumes dependent on device type

➢ Processing Speed Issues

➢ Who owns & controls the data?Solutions Start Here™ 10

Facebook Users 2016 Internet Users 2012

Fitbit Users 2017Mirai Botnet Attack 2016

Data Governance & Cybersecurity“ I n t e r n e t c o n n e c t i v i t y f o r c o m p u t e r s e m b e d d e d i n e v e r y d a y d e v i c e s ”

➢ Data Consistency Across Enterprise Applications

➢ Data Quality Throughout

➢ Rise of Cyber Bad Actors:

➢ Recreational Hackers

➢ Criminal Enterprises

➢ Industrial Spies

➢ Governments

➢ Transparency Advocates

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Big Data Tr e n d s

Trends

➢ Increase in Number and Size of Datasets

➢ More Data Sources

➢ More & Better Data Management Tools

➢ More Data Quality Concerns

➢ More Privacy Concerns

➢ More Security Concerns

➢ Rising Customer Expectations

➢ More Analytic Opportunities

➢ “Future Shock”

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Big Data Tr a n s i t D a t a S o u r c e s

Transit Data SourcesO p e r a t i o n a l S y s t e m s

➢ Bus Dispatching/AVL Systems

➢ Automatic Train Supervision

➢ Passenger Information Systems

➢ Scheduling Systems

➢ Incident Management Systems

➢ General Order Systems

➢ Signaling/Train Control Systems

➢ SCADA Systems

➢ Fare Collection Systems

➢ Call Taking Systems (Paratransit)

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Transit Data SourcesM a i n t e n a n c e / D i a g n o s t i c S y s t e m s

➢ Event Recorders

➢ Asset Management Systems

➢ Work Order Systems

➢ Network Management Systems

➢ Log File Tailing

➢ Alarm Systems

➢ SCADA Systems

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Transit Data SourcesA d m i n i s t r a t i v e S y s t e m s

➢ Payroll

➢ Timekeeping

➢ “Pick” Systems

➢ Training Systems

➢ Other HR Systems

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Transit Data SourcesU n s t r u c t u r e d S y s t e m s / S o u r c e s

➢ Customer Complaints

➢ Social Media (Twitter, Facebook)

➢ Email

➢ Documents

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Big Data Tr a n s i t O p p o r t u n i t i e s

Transit OpportunitiesL e v e r a g i n g D a t a t o C h a n g e Yo u r B u s i n e s s

➢ Enterprise Data Warehouses

➢ Cross Functional Data Insights

➢ Enterprise Asset Management

➢ Consolidation of Maintenance and Asset Information

➢ Key Source System for Data Warehouse

➢ Organization of Unstructured Data

➢ Connections/Patterns

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Big Data Tr a n s i t C h a l l e n g e s

Transit ChallengesO v e r c o m i n g E x i s t i n g P r o c e s s e s

Te c h n i c a l P a r t i s E a s y

➢ Inertia

➢ Construction Management Mentality

➢ Agile Resistance

➢ You Don’t Know What You Don’t Know

➢ Long Development Cycles

➢ Resources/Skillsets

➢ Budgets

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Summary

> Technology & Data are Available

> Unprecedented Insights/Understanding are Possible

> Institutional Barriers are Formidable

> Payoffs can be Substantial

Thank You.Rob Ayers

(804)307-5060

Rob@aE.Solutions

www.aE.Solutions