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Big Implications of Big Data

John DonovanSenior Executive Vice President, Technology and Operations, AT&T

December 12, 2015

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Source: http://www.internetlivestats.com/internet-users/

30% growth internet traffic per minute

from late 2014 to mid 2015

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Sources: www,fathomdelivers.com/blog/analytics-and-big-data/big-data-facts-and-statistics-that-will-shock-you www.erudine.com/big-data-evidence-based-decisioning/

KEY DRIVERS:

100X Internet Growth

Addressable Advertising

M2M

Data Sources

Technology – Infrastructure

Technology – Data Management

Analytics – Software

Analytics – Services

Businesses that focus on data quality could boost overall revenue by 66%

An average Fortune 1000 company that increased the usability of its data by just 10% could expect an increase of revenue of over 2 billion dollars

Big Data Presents a Market Opportunity for Businesses

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Challenges to Entering the Big Data Space

Complex Infrastructure

Balancing core capabilities and infrastructure with the rich ecosystem of best of breed big data tools and solutions

Data Science Talent

Finding the right people resources to not only answer the new big data questions, but also equipping business leaders to ask the right questions of their data

Enterprise-grade Solutions

Maintain enterprise levels of security, reliability & performance for critical applications, while integrating new tools with existing infrastructure investments

Financial Constraints

Increasing pressures to demonstrate a clear path toward ROI for analytics and big data projects before investments

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Predictive Analytics

What will happen?

Prescriptive Analytics

How can we make it happen?

Regular Analytics Big Data

Descriptive

Analytics

What happened?

Diagnostic Analytics

Why did it happen?

Hindsight Insight ForesightInformation Optimization

Analytics spectrum: from descriptive to

prescriptive

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strong network

uniquedata

powerful insights

smartsolutions

fastiteration

customer satisfaction

6

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Necessary skills needed to do the job

Data Management

Meta Data Repository

Policy Management Engine

Policy Enforcement Point

Data Anonymization

Consent Management Platform

Data Sourcing

Data Search Engine

Batch Data Router

Streaming Data Router

Access Management System

Data Set Creation

Data Ingestion

Hadoop

Offline Storage

Insight Development

Machine Learning

Analytics

Collaboration and Sharing

Published Insights

InsightPortal

Data Manager

Data Wrangler

EngineerData Scientist

• To be successful in delivering Data Insights, team members require new capabilities to do their job.• Some of the required capabilities are available commercially and some must be developed by AT&T• Required capabilities are provided as Technology Enablers

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AT&T Big Data process for insight development

TOP DOWNBig Data selects project identified

by Business Unit

BOTTOM UPBusiness Unit adopts findings

identified by Big Data

Big Data and Business Units MUST collaborate in order to

succeed

COLLABORATIVE TEAM MADE UP OF

Business Unit TeamTaking action from results of analysis

Giving context to analysisIdentifying key sources of data

Explaining complexities with raw data

Big Data TeamLearning about business problem

Acquiring data necessary for analysisCombining raw datasets for models

Developing explanatory and predictive models Communicating what models

mean

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Turning Big Data into knowledge

Hadoop Distributed File System

Unstructured Data

Structured Data

Data Sources

Internal& External

Hadoop

MapReduce

Analytics

AnalyticsRelational Database

Insights

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AT&T example: Network signal

measurement

Measured withvans driving around

auto-dialing

A few 1000 measurement pts/day

Crowd-sourcing app allowing customers

to report a dead spot

Added tens of 1000 more data

pts/day

+

TONA uses real-time data and historical measurements to capture the ripple-

effect of the disruption +59 %

improvement in identifying

customer impact

• Measure 1.9B network quality checkpoints on an average business day.  • Analyze data points in near-real time and troubleshoot problems

immediately.  • Analyze more than 100 factors known to correlate directly to customer

satisfaction

2008 20152009

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AT&T example: Managing a fleet

Big Data Analysis75,000 vehicle fleet

409,000 garage visits 43,000 tows

20,000 jump starts

Telematics On-board sensors continuously

measure and report vehicle condition and operating

performance

Predictive Analytics

+Insights led to preempted failures and downtime

Vehicle Maintenance

Preventative wasteful, if too soon

Reactivecostly, if too late

~$1.5 million in annual savings from proactively managing battery failures

Just in time maintenanceReduce waste and limit repair cost

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“Why can’t I get U-verse when everyone else on my street has it?”

“When will GigaPower be in my neighborhood?”

• Working to eliminate these questions!

• Pulled together disparate data sets to map 85 million houses.

• Uncovered ‘Red Islands’ – units that should be eligible but were incorrect in systems.

• Turned 90,000 units ‘green’ in a few weeks.

• No additional capital investment

• More customers can enjoy our premium products and services.

AT&T example: Turning U-verse eligible

“red”

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AT&T example: Customer care & repeat call reductionIdentify patterns and opportunities and root case for customer care repeat callers

# of Calls Caller Profile

4+Calls

2 - 3 Repeat Calls

1 and Done

• Customer profile should drive smarter segmentation of experience and support

• Understand digital care usage and align strategy

• Increase/decrease of equipment inside customers’ homes negatively impacts call volume

• Agent experience matters

• One and done indicative of future customer profile

Key takeaways

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AT&T example: Real estate & intelligent

buildings Corporate Real Estate

• Reduce costs through use of sensors for just-in-time maintenance on major building equipment

• Develop predictive maintenance models and quick reaction to failures

• AT&T scale allows for significant cost savings

• Successful deployment in AT&T allows commercial application

SensorReading

Historical Data over Time

NormalAnomaly

RepairHistory remove and

replace event

Correlate

Sensor Data

Repair History Data

Maintenance Data

Proactive Just-in-Time Maintenance

Statistical Models

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The AT&T Approach

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PROCESS

• Data Collection

• Data Management

• Collaboration with internal business unit partners

SKILLS

• Right skills with the right people

• Internal curriculum and advanced training with partners in education

CUSTOMER PRIVACY

• Top priority: customer trust is foundational to success

• Driving a cultural change to value data as an asset

© 2015 AT&T Intellectual Property. All rights reserved. AT&T, the AT&T logo and all other AT&T marks contained herein are trademarks of AT&T Intellectual Property and/or AT&T affiliated companies. The information contained herein is not an offer, commitment, representation or warranty by AT&T and is subject to change. All other marks herein are the property of their respective owners.