April 2019

THE DATA CENTRIC TOLLING ENTERPRISE

Building a Roadmap for Success

April 2019

April 2019

PRESENTERS

2

DAN MONTGOMERYNorth Highland Data & Analytics

Information Management Lead

Dan.Montgomery@northhighland.com

972-523-5367

April 2019

AGENDA

3

Background / Challenge

Objectives & Expected Benefits

Approach

Current State Insights

Future State

Strategy & Roadmap

1

2

3

4

5

6

Questions7

April 2019

BACKGROUND / CHALLENGE

4

TxDOT Tolling Overview

- Vision: "A customer-focused service provider that creates economic opportunities, stimulates investment

and enhances quality of life by supporting high-performing transportation systems in Texas and beyond."

- Mission: "Exceed customers' expectations and provide leading mobility solutions by delivering and

growing an integrated, safe, reliable and efficient highway system."

- 440M annual transactions

- $410M annual toll revenue collected

- 275 Toll Road Miles

- Toll roads in the Austin, Dallas, and Houston metros

- TxDOT Tolling was looking to make strategic decisions by leveraging data coming from three primary

sources as well as other sources from multiple origins

April 2019

OBJECTIVES & EXPECTED BENEFITS

5

The following are the key objectives and anticipated benefits of becoming a Data Centric Organization

KEY OBJECTIVESCreate a sustainable and actionable Data Warehouse and Business Intelligence strategy

Develop a best practice Data Governance framework to support the operationalization of the DW/BI roadmap

KEY BENEFITS• Promotes fact-based decision making through the effective utilization of data assets

• Creates a common language for critical information

• Aligns business strategy with information

• Enhances data trust

• Standardizes processes

April 2019

APPROACH - DATA COMPONENT MODEL

6

The data component model consists of 12 components that align your information to the business strategy through

people, process, and technology

GOVERNANCE• People (governance organization including data stewards)

• Process (best practices and procedures)

• Technology (tools to support data governance)

OPERATIONAL COMPONENTS• Master Data Management

• Data Quality

• Metadata Management

• Analytics

• Dashboards, Scorecards, and Reporting

• Security and Privacy

• Data Integration

• Data Strategy and Architecture

PROJECT EXECUTION COMPONENTS

• Project Management

• Change Management

• Organizational Alignment

BUSINESS

STRATEGY

Master Data

Management

Data Strategy

& Architecture

Data

Quality

Metadata

Management

AnalyticsDashboards

Scorecards

Reporting

Security &

Privacy

Data

Integration

April 2019

APPROACH - THE PHASES OF THE DW/DG/BI SOLUTION

7

Where you are today Where you want to be How you get there

Siloed, manual data operations

limiting the ability to fully

leverage data

Data-centric operations

promoting analytical insight and

enabling the realization of

strategic initiatives

Advance data competency

through the execution of key

BI/DW/DG activities

Together, we set out to accomplish the key objectives through the execution of three phases

DISCOVERY FUTURE STATE ROADMAP

April 2019

CURRENT STATE INSIGHTS

8

DATA AS AN

ASSET

SILOED

OPERATIONS

METADATA

MANAGEMENT

DATA

CONTINUITY

DATA

INTEGRATION

DATA

GOVERNANCE

Limited view of the role

of data as an asset

(transactional output vs

data as a driver of

analytics)

Siloed functional analysis

creates operational

inefficiencies

Reliance on function-

specific knowledge vs.

common metadata

definitions across the

division

Reconciliation between

source systems requires

extensive manual effort

to maintain data

continuity, further limited

by vendor contracts

Decentralized BI

capability reduces the

ability to integrate data

sources for robust

analytics

Lack of data governance

hinders progress in

building an integrated

environment for BI and

analytics

Significant effort spent

on data reconciliation

vs. data analysis

Duplication of effort

due to lack of

knowledge sharing and

organizational

alignment

Lack of cross-

functional knowledge

management, risk with

employee retention,

implications for

customer sentiment

Potential misalignment

across reporting output

requiring additional

reconciliation; access

limitations due to

vendor reliance

Limited capability to

derive action-oriented

insight and effectively

manage public

relations / bond

reporting

Inhibiting ability to take

data competency to the

next level

Several common themes on the current state of tolling data operations, with the following demonstrating some examples.

Tolling agencies can expand operations (through interoperability) and achieve strategic initiatives with the current state of data operations

TH

EM

EIM

PLIC

ATIO

N

1 2 3 4 5 6

April 2019

FUTURE STATE

9

Siloed, manual data operations limiting

the ability to fully leverage data

HOW YOU GET THERE

TODAY FUTURE

STRATEGIC INITIATIVES

1. Increase TxTag participation rate

2. Replace back-office system and

operator

3. Become nationally interoperable with all

toll facilities in the U.S. and Mexico

4. Build a data warehouse to store and

manage records

5. Build TxDOT Toll Operations Division

staff to operate with the knowledge to

manage a world class customer service

organization that is both customer

focused and efficient.

CURRENT STATE OF DATA OPERATIONS

• Limited view of the role of data as an

asset (transactional output vs data as a

driver of analytics)

• Siloed functional analysis creates

operational inefficiencies

• Reliance on function-specific

knowledge vs. common metadata

definitions across the division

• Reconciliation between source systems

requires extensive manual effort to

maintain data continuity, further limited

by vendor contracts

• Decentralized BI capability reduces the

ability to integrate data sources for

robust analytics

• Lack of data governance hinders

progress in building an integrated

environment for BI and analytics

Data-centric operations promoting

analytical insight and enabling the

realization of strategic initiatives

Advance maturity across the data component

model through the execution of key BI/DW/DG

initiatives and projects

Focus of today’s discussion

April 2019

FUTURE STATE – DW/DG/BI BICC & DATA GOVERNANCE

10

The Business Intelligence Competency Center (BICC) and Functional Data Governance Organization were selected during the Future

State Visioning Session as the core pillars to the Future State Organization to drive the advancement of data competency

BUSINESS INTELLIGENCE

COMPETENCY CENTER

The BICC is comprised of a

centralized team closely aligned

with functional partners across

the division

FUNCTIONAL DATA

GOVERNANCE

The data governance organization

is built around the functional area

requirements and usage of data

April 2019

FUTURE STATE - ORGANIZATIONAL MODEL

11

The data competency organization integrates the BICC and DG structure to operationalize the future state

BI Analyst

TxDOT Toll Operations Division

TOLL EQUIPMENT

& FACILITIES

REVENUE &

BUDGET

OPERATIONS

BOS

ENGINEER &

PLANNING

COMMUNICATION

& MARKETING

PMO (Project)

BUSINESS

SERVICESBICC

BI Analyst

To Be Discussed

Data Governance Steering Committee

Data Governance Manager / BICC Leader

DC

Data

Steward

Data

Steward

Data

Steward

Data

Steward

Data

Steward

Data

Steward

Data

Steward

DC = Data Custodian

DC DC DC DC DC DC DC DC DC DC DC DC DC DC

Note: BI Analyst and Data Steward roles to potentially be fulfilled by the same named resource

BI AnalystBI AnalystBI Analyst BI Analyst BI Analyst

BICC Leader, Pgm. Mgr.,

BI Bus. and Tech Arch.,

Data Arch., Report and

ETL Developer, Bus.

Analyst, DBA, QA/Tester,

Chg. Mgmt., Metadata

Specialist

BI Analyst

*individuals on the transition team

April 2019

FUTURE STATE - REFERENCE ARCHITECTURE

12

Tableau

Back Office Vendor

BI Tool

Data Discovery

Tools

TBD - Advanced

Analytics Platform

TOAD / Other

DATA STORAGE

Data

INF

O P

OR

TA

L

AN

AL

YTIC

S W

OR

KB

EN

CH

DA

TA

SC

IEN

CE

LA

BO

RA

TO

RY

ANALYTICS ACCESS

Customer

HubMetadata

Store

Event Data?

CURATED- Conformed

dimensions

- Subject areas

BUSINESS

ACCESS- Data Marts

- ROLAP

SOURCE TYPES DATA

MOVEMENT

DATA WAREHOUSE

Archive Data

MASTER DATA

MANAGEMENT- Customer Data Hub

- Repeatable process

DATA QUALITY

MANAGEMENT- Source data quality

- KPI validation & audit

SECURITY & PRIVACY

CONTROL- Data classification

- Tokenization management

- Access control/monitoring

Processes

and Controls

ARCHITECTURAL

STANDARDS- Data/Platform/ETL

standards

DATA

GOVERNANCE- Data ownership

- Data stewardship

- Data lifecycle management

METADATA

MANAGEMENT- Data lineage

- Data dictionaries

RAW- Landing

- Validation

(optional)

Marketing

Social Media

Twitter, FB

T&R Data

TxTAG

Web Site

Weather

Events

TBD

Back Office

New

Cust. Svc.

Conduent

Road Usage

Lonestar

Crash Data

CRIS

Vehicle

DMV

Back Office

Conduent

Expense

Peoplesoft

Lane Data

TransCore

Informatica

Back Office Vendor ETL

Tool

Talend

April 2019

STRATEGY – FOCUS ON BUSINESS “ANALYTICS” NEEDS

13

CA

TE

GO

RY

TOLLING OPERATIONS

Understanding operational data, both from

the user’s and lane’s perspectives

TRAFFIC PATTERNS

Discerning trends in traffic

and the factors that

influence them

CUSTOMER INSIGHT

Describing customers and

measuring how well they

are being served

FINANCIAL PERFORM.

Providing insight into the

agency’s past, current, and

future financial health

SU

BC

ATE

GO

RY Tag

Using information

sent from driver’s

TxTags

LaneGathering

Information from

lane equipment

Interop.Collecting tag data

used in

interoperable

settings

RegularIdentifying typical

traffic patterns that

are seen on tolls

apart from

idiosyncratic events

Event/WeatherTracking and

predicting how

idiosyncratic events

affect traffic

patterns

BehaviorMeasuring driver

qualities based on

certain actions and

characteristics

ServiceDetermining the

level of service that

is offered to

customers

RevenueCalculating

received and

anticipated

payment and

funding

ExpenseComputing the

costs incurred and

is expected to be

incurred

US

E C

AS

ES

1. TxTag stats

2. iToll Analysis

3. TxTags on Home

4. Tag Usage

1.Daily Monitoring

2. Monthly

Monitoring

3. Speeds GP vs ML

4. Trans. Type

5. Axle Class

6. Postings

1. Away Tag

Analysis

2. TxTags on Away

1. Traffic Control/

Management

2. Value of Time

3. Safety

4. Predictive

Equipment

Maintenance

1. Timeline/

Duration

2. Lane Variation

3. Managed Lanes

Predictive Analysis

1. Type of Accounts

2. Zip Code Analysis

3. Trend and

Predictive Cust.

Behavior

4. Habitual Violator

5. Trips

1. Customer

Account Analysis

2. Fraud

3. Cust. Service

Stats Analysis

4. Call times

5. Website

Response Time

6. Page Refresh

7. Click to Pay

1. Revenue

Recognition

2. Rejected

Transactions

3. Collections

4. Aging Analysis

5. Invoice Stats

6. Lane to Back

Office

1. Budget &

Expense Monitoring

2. Contract &

Encumbrance

Information

3. TCC Metrics

4. TSA

Each data use case has been grouped with similar use cases, which will be developed during the Phased Builds

1 2 3 4

Note: In addition, dashboard views for reporting to various stakeholders – including legislature, districts, bondholders, and drivers –

may be compiled on an as needed basis using the above use cases.

April 2019

STRATEGY – QUICK WINS AND OTHER KEY ACTIVITIES

14

QUICK

WINS

FOUNDATIONAL

ACTIVITIES

PHASED

DW BUILD

Objective Maximize immediate value of the

FS DW / DG / BI solution

Standup DW / DG / BI infrastructure

for sustainable operations

Accelerate operationalization of the

DW / BI solution

Example of Key

Activities

• Create centralized event tracker

• Define terms of reference

• Standup DG steering committee

• Conduct organizational alignment assessment and present key recommendations

• Formalize the business strategy with a detailed execution plan to ensure cross-functional alignment

• Develop communication plan to begin socializing the value/benefits of the DW / DG / BI solution

• Build source system data visualization to demonstrate immediate value

• Confirm technology platform for the data warehouse (long-term viability, short-term solution)

• Select/confirm appropriate required technologies/tools

• Detail high level DW data architecture

• Define metrics / KPIs across functions

• Establish BICC

• Setup DG program (People, Process, Technology)

• Collect District / Region requirements for DW / BI

• Determine interoperability requirements

• Define metadata solution / establish data literacy

• Develop Master Data Management (MDM) Program

• Execute Phased build of DW / BI (multiple iterations to deliver business case capabilities)

• Each phase/iteration is intended to deliver one to many business case capabilities/functionality. We will be grouping the “business case capabilities/functionality” into phase/iterations as part of the roadmap phase

• The average timeframe for each phase/iterations is approx. 90 days but could vary from 60 to 120 days

1 2 3

The key activities around future state realization have been grouped into three key categories

April 2019

ROADMAP - DATA USE CASE PRIORITIZATION

15

Use cases will be identified and prioritized based on those use cases with the perceived highest value and are most

feasible to implement

FEASIBILITY CRITERIA –

• # of source systems

• Quality of the data

• Ease of source data access

• Volume of historical data required to be loaded

• Number of data transformations required

BUSINESS VALUE CRITERIA –

• Tied to Strategic initiatives

• # of business functions impacted

• Efficiency gains

• Improved quality / usability

• Actionable output (dashboard view vs. detail

spreadsheet view)

LOW HIGH

HIG

H

Business Value

Imp

lem

en

tati

on

Fe

asib

ilit

y

HIGH PRIORITY*

Use Case 1

Use Case 2

Use Case 5Use Case 4

Use Case 3

*DW/BI development groupings to be determined based on Value/Feasibility plus other criteria, such as common source systems and Division priority.

April 2019

ROADMAP – ACTIVITY EXAMPLES

16

ACTIVITY 1 2 3 4 5 6 7 8 9 10 11 12 13

Stand up DG Steering Committee

Formalize the business strategy

Conduct organizational alignment assessment

Develop communication/change management plan

Set up DG program

Define metrics / KPIs across functions

Create centralized event tracker

Define first 50 terms of reference

Develop source system data visualization

Confirm technology platform for the data warehouse

Establish BICC

Select/confirm required technologies/tools

Collect District / Region requirements for DW / BI

Determine interoperability requirements

Detail high level DW data architecture

Define metadata solution / establish data literacy

Develop Master Data Management (MDM) Program

Execute phased build of DW/BI*

Months

*Phased build to include multiple iterations, informed by data use case prioritization

April 2019

QUESTIONS?

THANK YOU!

Building a Roadmap for Success

April 2019