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 about

everyone 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 – Macy’s, 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 time—

nobody’s 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

• Google—Search, AdSense, Books, Maps, Scholar, etc., etc.

• LinkedIn—People You May Know, Jobs You May Like, Groups You May Be

Interested In, etc.

• Netflix—Cinematch, Max, etc.

• Zillow—Zestimates, rent Zestimates, Home Value Index, Underwater Index, etc.

• Facebook—People 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

We’re 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 everybody’s 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/day—7 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.”

• Ford’s Digital Analytics and Optimization team

has full responsibility for all B2C channels and

N. American business units

• Dynamic multichannel testing and targeting with

automation 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