PROACTIVELY DE-RISKING UPCOMING CIS IMPLEMENTATION AND ACCELERATE SELF SERVICE CUSTOMER ANALYTICS JOURNEY

Southwest Gas and Infosys Experience

Oct 1st, 2019

Presentation agenda

01

02 About Infosys , Southwest Gas and the CIS Program

03 Early Data & Analytics Focused Journey

04 Data Conversion & Analytics Solution

05 Results & Lesson learnt

06 Questions

Session Context

$12.1 Billion in revenues

116 development

centers globally & 6 US

innovation hubs

99% of our business is

repeat business

228,000+ employees

globally

25,500engineers

trained in 3+ programming

languages

60,000employees

trained in new

technology areas

$500 Million innovation fund

135,000+ employees

trained in Design Thinking

Establishing US

Education Center in Indianapolis Infosys - ChennaiInfosys - LondonInfosys - Sydney

Infosys - Indiana Infosys - Hyderabad Infosys - Pune

Infosys - BangaloreInfosys - Palo AltoInfosys - Poland

3

Clients CIS Ecosystem Support/Operations MDMS / AMI Consulting/Transform

Southern California Edison Custom

Northeast Utility Custom

Florida Power & Light Customer/1

FPWC

APS

EDF Energy

Essent

IRWD

R-APDRP

EON Custom

Ausgrid

SSE (Professional Services Model of Engagement)

Custom

Welsh Water

Custom

Infosys Utilities: Decades of Large IOU CIS/MDM Experience

Products and solutions for the utilities industry

23+ years of utilities experience

45+ clients globally

6000+ domain experts

15 development centers

6 out of top 10Europe utilities | US utilities

Infosys positioned in Winner’s Circle in HfS

Utility Operations Report - 2017

Worldwide Digital Transformation Service Providers for Utilities 2017

DERMS Providers for Utilities

4

Why Pre-work on Data & Analytics is advisable

Data transformation

is complex

Discovery phase may take time

Focus on data starts

before design

Data & Reporting goes hand in

hand

Introduce team to concepts of real-time & In

memory computing

Orient the team towards self-

service

New CIS system is as good as the data Analytics tells how good your data is

About Southwest Gas & Project

Territory: Arizona, Nevada andCalifornia

2M+ customers and 30K+ miles of pipeline

95% Customer Service Satisfaction

~2,300 Employees; 450+ field techs

Project Horizon Overview

7

Project Horizon is essential in Southwest Gas’ mission: enrich the lives of customers and employees within our communities by providing safe and reliable natural gas service.

We are replacing our aging Customer Service System (CSS) with new utility billing and customer information system (CIS) software to support enhanced customer insights and communication preference management.

With Project Horizon we will be positioned to offer personalized, multi-channel experiences and deliver an exceptional level of service.

The project team consists of 25+ business partners, 70+ employees with 30+ subject matter experts companywide, representing all areas of our business.

The project involves many parts of our business

8

12 organizational units moving from a mainframe green screen to a web-based system

100+ business processes impacted by the new customer system technology

1,000+ system end userscurrently using CSS that will benefit from Project Horizon

end users

Customer Service

Meter Reading

Billing & Exceptions

Customer Engagement

Payment & Credit

Collections

Self-service

FICA

AccountingCredit &

Billing Ops (CABO)

Call Center

Construction

Customer Engagement

Customer Service (Field)

Dispatch

Energy Solutions

Engineering

Key Account Management

Regulatory

Technical Services

• Piloted data profiling/ cleansing approach

• Cleansed 100+ data elements

• Data Stewards Identified

• Moved towards on-demand, self service reporting

• Used 2 major source of data for reporting

• Data quality metrics established with DQM

• Customer & Address Profiling

• Data Conversion& reporting Strategy Finalized

• Identify other sources

• Staging environment sized and setup

• Data reconciliation started

• Data Mapping of all master data and ETL build

• Second and third cleansing iterations

• Start building report on CSS Data

• Pre-mock data conversion runs

• Unit Testing of converted data

• Reconciliation Framework

• Data Mapping of all transactional data and ETL build

• Reporting out of non CSS data

• Fourth cleansing iteration

• Mock data runs

• Data Reconciliation

• Trials Runs – 100%

• Production Run

• Reconciliation

• Report deployment per security group

• DQM in SAP

Pre-Planning Plan & Analyze Design and Build Build & Test Deploy & Stabilize

The Early Focused Data & Analytics Journey at Project Horizon

Early 2019

Late 2019

Early 2020

Late 2020

Early 2021

EARLY FOCUS

Step 1- Data Profiling

Used SAP Cloud based Application - Agile Data Preparation (ADP) for Profiling

• Check distinct values for any discrepancies • Check pattern for erroneous entries

Step 2- Data Cleansing & Governance

DQM to ensure data remains clean.

Again used ADP

Step 3-Extract Data From Legacy

• Used Informatica Power Center to extract the data from mainframe

• Prepare data mapping from legacy to target SAP Tables

• Configure transformation/ cleansing rules during import

Step 4-Data Modeling

Service Orders

Serv. Order History

Meter InventoryCustomer

CSS_Recv Billing History

Generic Data model Aligned with new CIS data model

Step 5 -Reporting

• KPI Driven• Smart Filter• User flexibility in adding

fields & filter• Introduce meaningful

graphs & maps

Customer/Premise Segment• 15 out of 77 segments having error• 4538 records having error• About 0.23% of the records

4538

206 50

5000

Pass 1 Pass 2 Pass 3

ERR

OR

CO

UN

T

PASS

CUSTOMER/PREMISE

Service order Segment• 6 out of 45 segments having error• 27 records having error• About 0.03% of the records

Service order History Segment• 6 out of 14 segments having error• 16819 records having error• About 0.04% of the records

Others Segments• 1 out of 4 segments having error• 3 records having error• About 0.003 % of the records

Survey Segment• 1 out of 2 segments having error• 201 records having error• About 0.002% of the records

Summary Bill Segment• 1 out of 5 segments having error• 543 records having error• About 0.17% of the records

273 0

0

50

Pass 1 Pass 2 Pass 3

ERR

OR

CO

UN

T

PASS

SERVICE ORDER

168191207 23

020000

Pass 1 Pass 2 Pass 3

ERR

OR

CO

UN

T

PASS

SERVICE ORDER HISTORY

31

0024

Pass 1 Pass 2 Pass 3

ERR

OR

CO

UN

T

PASS

OTHERS

20117 6

0200400

Pass 1 Pass 2 Pass 3

ERR

OR

CO

UN

TPASS

SURVEY

543

12 00

5001000

Pass 1 Pass 2 Pass 3

ERR

OR

CO

UN

T

PASS

SUMMARY BILL

Key Results: Improved Data Quality

15

Key Learnings and Benefits from an Early Focus on Data & Analytics

Get an early handle at source, quality, sizing and technology decisions

Faster readiness for data Reconciliation & ITC’s

Gives business enough time for getting comfortable with data & perform profile/cleansing

More focused requirement gather sessions (KPI, security)

Users are generating their own reports instead of dependent on analysts

Enterprise wide analytics platform enabled to add any type of source & do composite analytics

Bene

fits

Don’t cleanse everything & Prioritize at source

Business takes data ownership with DQM

KPI’s during requirement gathering & push for self service

Lear

ning

s

Questions

17

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