NASSCOM ILF 2014: The Digital Enterprise - Big Data and Analytics Lead the Way: Thomas H. Davenport, International Institute for Analytics

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Text of NASSCOM ILF 2014: The Digital Enterprise - Big Data and Analytics Lead the Way: Thomas H. Davenport,...

Converging on Better Decisions

The Digital EnterpriseBig Data and Analytics Lead the Way!

Thomas H. Davenport

Babson/MIT/Harvard

December 5, 2013

Efficient, fast transactions

Agile system development

IT-enabled processes

Knowledge management

The ability to make sense of

exabytes of data: analytics!

Ranked the #1 priority at WSJ CIO

Summit last week

The Digital EnterpriseKey Capabilities

Big data begins at

online firms

& startups

No technical or

organizational

infrastructure to

co-exist with

Working wonders for

Google, eBay, & LinkedIn

but what abouteveryone else?

What happens in

20 big companies when

analytics are

well-entrenched?

Findings show evolution

of a new analytics

paradigm

Big Data in Big Companies Study

How new? Not very to many continually adding data over time

UPS Started building telematics capabilities in 1986

Excited about new sources of data, new processing capabilities

Familiar rationales for big data:

Same decisions faster Macys, Caesars

Same decisions cheaper Citi

Better decisions with more data United Healthcare

Product/service innovation GE, Novartis

Need new management paradigm

Analytics 1.0 Traditional Analytics

Primarily descriptive analytics and reporting

Internally sourced, relatively small, structured data

Back room teams of analysts

Internal decision support focus

Slowly-developed models1.0

Analytics 1.0 Data Environment

ERP

CRM

Legacy

3rd Party Apps

Reporting

OLAP

Ad Hoc

Modeling

Spreadsheets

BI and analytics packages

ETL tools

OLAP cubes

On-premise servers

Out-of-database/memory analytics

Analytics 1.0 Other Technologies

Keep inside the

sheltering confines of

the IT organization

Take your timenobodys that interested in your results anyway

Focus on the past,

where the real threats to

your business are

Analytics 2.0 The Big Data era

Complex, large, unstructured data about

customers

New analytical and computational capabilities

Data Scientists emerge

Online and startup firms create data and analytics-

based products and services

2.0

2.0 Data ProductsFrom Online Firms

GoogleSearch, AdSense, Books, Maps, Scholar, etc., etc.

LinkedInPeople You May Know, Jobs You May Like, Groups You May Be Interested In, etc.

NetflixCinematch, Max, etc.

ZillowZestimates, rent Zestimates, Home Value Index, Underwater Index, etc.

FacebookPeople You May Know, Custom Audiences, Exchange

Analytics 2.0 Data Environment

Map/Reduce

Web Logs

Images & Videos

Social Media

Docs & PDFs

HDFS

Operational Systems

Data Warehouse

Data Marts & ODS

We need to be on the bridge

Agile is too slow

Consulting =dead zone

Were changing the world

Analytics 3.0 Fast, Pervasive Impact in the Age of Smart Machines

Analytics used for data products and Industrialized

decision processes

A seamless blend of traditional analytics and big data

Analytics integral to all business functions

Rapid, agile insight and model delivery

Analytical tools available at point and time of decision

Analytics are everybodys job

3.0

TODAY

Analytics 3.0 Competing in the Data Economy

Every company not just online firms can create data and analytics-based products and services that change the game

Use data exhaust to help customers use your products and services more effectively

Continuous, real-time analytics

Start with data opportunities or start with business problems? Answer is yes!

Need data products team good at data science, customer knowledge, new product/service development

Internally, analytics built at scale and embedded into decision processes

Analytics 3.0: Data Types

Customer profiles Organization

contacts

Billing Marketing Contracts/orders Shipping Claims Call center Customer service

Purchase history Segmentation Customer value Purchasing behavior Recommendations Sentiment analysis Target marketing Satisfaction Customer

experience

management

Service tiers

Clickstream logs

Images

RSSVideos

Hosted applications

Spatial GPS

LinkedIn

Device sensors

Email

Articles

Text messages

Cloud

Mobile devicesXML

Presentations

Blogs

Website activity

Social Feeds

Twitter

Documents

Analytics 3.0 Data Management Choices

Heavy reliance on machine learning

In-memory and in-database analytics

Integrated and embedded models

Analytical apps by industry and decision

Focus on data discovery

Blended data science/business/IT teams

Chief Analytics Officers in many firms

Analytics 3.0Technology & people

3.0

Primary focus on improving management decisions at scale

Information and Decision Solutions (IT)

embeds over 300 analysts in leadership teams

Over 50 Business Suites for executive information viewing and decision-making

Decision cockpits on 50K desktops

35% of marketing budget on digital

Real-time social media sentiment analysis for Consumer Pulse

Procter & Gamble 3.0176 years old

$2B initiative in software, analytics, and

Industrial Internet

Primary focus on data-based products and

services from things that spin

Will reshape service agreements for locomotives, jet engines, turbines

Gas blade monitoring in turbines produces 588 gigabytes/day7 times Twitter daily volume

Offering new industrial data platforms and brands like Predictivity and Predix

GE 3.0120 years old

Bill Ford: The car is really becoming a rolling

group of sensors.

Fords Digital Analytics and Optimization team

has full responsibility for all B2C channels and

N. American business units

Dynamic multichannel testing and targeting withautomation and integration of SEO/SEM, CRM,

email, media, etc.

Hyper-local dealer support digital algorithm delivered 85% increase in action rate and 48%

decrease in cost per action

Ford 3.0110 years old

Recipe for a 3.0 World

1. Start with an existing capability for data management and analytics

2. Add some unstructured, large-volume data

3. Throw some product/service innovation into the mix

4. Add a dash of Hadoop and a pinch of NoSQL

5. Cook up some data in a high-heat convection oven

6. Train your sous chefs in big data and analytics

Need to embed analytics into other systems

May be role for ongoing monitoring of

embedded analytics

Software firms hold up the data mirror

Dealing with the law of large numbers on

analytical skills

Analysts often need to be embedded to have

an impact

Implications for

Software/Services Providers

Thank you!tdavenport@babson.edu