Data Hub Case Study

Anders Rolff

Amdocs Big Data Analytics

September 2016

2 Information Security Level 2 – Sensitive © 2016 – Proprietary and Confidential Information of Amdocs

Becoming a Data Empowered Business CSP NEEDS

Drive more meaningful business insights and actions at the right time

Materially reduce costs

Expose data for internal and 3rd party vendors’ insights and innovations

CSP CHALLENGES

Extract and cleanse relevant data

Evolve technology to low cost high, volume real-time systems

Source affordable talent that know both the data & the technologies

AMDOCS SERVICES & TECHNOLOGY OFFERING

Open source data management platform pre-integrated with collectors, tools and data model

Suite of Actionable Analytics Wide set of professional services with deep data and technology expertise

Network Core

AAA CM VoLTE FM

Radio Access Network

CM Call Trace FM PM

Other

DPI Device Social

BSS / OSS

Charging EPC CRM

PRODUCTIZED DATA COLLECTORS

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Amdocs Data Empowered Offering

Contextual Experience

(ACE)

Charging Business Analytics Enterprise

Experience Management CEM Customer

Network Care

Customer Network Analytics

Next Best Network

Investment

Predictive Maintenance

Policy Profiler

Data Modelling

Big Data Infrastructure

Data Integration

Data Science

Managed Services

Visualizations

Revenue Assurance

Big Data platform with pre-integrated data collectors, a Telecoms specific data

model and Customer Profile

Full range of best in class data management services to help transform CSPs to data

empowered organizations

Library of applications with

Business or Network focus.

Third parties can also build apps to

drive innovation

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Overview

Customer Needs

•  Enable “self-service” model for business units to improve time to business insights

•  Reduce siloed analytics to achieve single version of truth

•  Shield DWH and other downstream systems from source integration complexity and changes

South American telecom operator +100M subscribers

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Strategic Objectives:

•  Leverage Amdocs’s telco-focused Logical Data Model to save cost

•  Use Amdocs Data Hub as ETL Lake to ensure centralized model and analytics governance

•  Semantic layer with MicroStrategy Visual Insights for self-service reporting

Technologies:

•  Amdocs Data Hub

•  HortonWorks HDP 2.2

•  MicroStrategy

•  Oracle GoldenGate

Solution Strategy – Hybrid Hadoop/Teradata DWH

EDW

Re

plic

atio

n Amdocs

Non- Amdocs

Prebuilt collector

Prebuilt collectors Customer

Product

Assigned Product

Order

Invoice

Account Receivable

Collection

Service Order Mngt

Customer Service

Usage

Work Force Order

Management

Billing

CRM

Ordering

Usage

Workforce Mgmt

Downstream Applications

Product Catalog

Semantic Layer

OLAP Cubes

ETL Lake (Amdocs Data Hub)

Baseline Reports Self-Service

Reports

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Data Model Customizations

0% 20% 40% 60% 80% 100%

AR area - General

Assigned Product

Collection

Customer

Customer Service

Invoice

Order

Product

Usage

5

8

2

1

1

1

1

1

1

42

9

20

40

24

15

17

14

13

New Entities Added Entities Changed Logic OOB

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

AR area - General

Assigned Product

Collection

Customer

Customer Service

Invoice

Order

Product

Usage

7

3

6

32

11

106

8

18

82

5

1

0

7

1

4

9

6

52

535

155

199

514

255

289

215

169

373

New Attributes Changed Logic OOB

Entities Attributes

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Achieving the Self-Service Goal

•  Solution Foundation: Semantic Layer and slice & dice capabilities of MicroStrategy Visual Insight

•  Business Adoption: Worked with early adopters and transformed their existing reports to Visual Insights dashboards and then used internal success to spread adoption in the business

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Benefits & Results

Approach

•  Agile implementation of modern Hortonworks-based solution

•  Implement aLDM telco-specific logical model over Big Data to provide integrated reporting and analytics-ready data

•  ETL Lake shields DWH and data consumers from source changes and complexity, need to access sources systems directly eliminated

Benefits

•  Major hardware cost reduction (~$10M) from use of commodity hardware

•  Reduced business unit dependency on IT and improved TTM for new reports and insights

•  Eliminated need for transformation of existing data warehouse

•  Single version of truth established

•  Improved data quality & data latency (near-real time)

Thank You