Big Data

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

Featuring Babson College Professor Tom Davenport, author of Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

MARCH 3, 2014

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Thomas DavenportPresidents Distinguished ProfessorManagement and Information TechnologyBabson College

Author of Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

#HBRwebinar @HBRExchange

MARCH 3, 2014

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

#HBRwebinar @HBRExchange

MARCH 3, 2014

Thomas DavenportPresidents Distinguished ProfessorManagement and Information TechnologyBabson College

Author of Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

Big Data @ Work

Thomas H. Davenport

Babson/MIT/International Institute for Analytics

Harvard Business Review Videocast

March 3, 2014

Whats New About Big Data?

My definitionToo big for a single serverToo unstructured for a relational databaseToo fast-moving to fit into a warehouse

Need data scientists to manipulate it A variety of new technologies to

manage it

Requires a new approach to management and decision-makingEvidence-based, fast, continuous decisions

8 | 2013 Thomas H. Davenport All Rights Reserved

What to Do with All This Stuff?

9SOURCE: McKinsey Global Institute ; Digital Universe Study, IDC

Global data storageExabyte

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Global data storageExabytes

20151413121110090807062005

About 0.5% of this data is analyzed in any way!

10

Industries and Their Use of Big Data

Data Streamsfrom Operations/CustomerRelationships

Use of Data for Decision-Making and Products/Services

Limited

Extensive

Limited Extensive

Disadvantaged

Underachieving Big Data Competitors

OverachievingCPGHealth Care

InvestmentsTelecom

11

Functions and Their Use of Big Data

Data Streamsfrom Operations/CustomerRelationships

Use of Data for Decision-Making and Products/Services

Limited

Extensive

Limited Extensive

Disadvantaged

Underachieving Big Data Competitors

OverachievingOperationsHR

MarketingFinance, Sales

What Can You Do with Big Data?

12

Save money with big data technologies (Citi)

Make the same decisions faster (Caesars, UPS)

Make new types of decisions (United Health, Schneider)

Develop new products and services (Nest/Google, GE, Monsanto)

How to Prospect for Big Data Projects

13

Big pile of data Big pile of business/customer problems

Where Are Your Big Data Applications?

14

Discovery Production

Cost savings

Faster decisions

New decisions

Products/services

Whos in Charge?

15

Discovery Production

Cost savings IT innovation IT operations

Faster decisions Analytics group Business unit/function

New decisions Analytics group Business unit/function

Products/services R&D/product devt Product devt/mgt

Building Big Data Capabilities

16

Data . . . . . . . . big, small, structured, unstructuredEnterprise . . . . . . . .integrated big and small data

analytics

Leadership . . . . . . . . . . . . . . .passion and commitmentTargets . . . . . . . . . . . . . . . . . . where to start?Technology. . . . . . . . new architecturesAnalysts . . . . . data scientists

Actions in Each DELTTA Category

17

Data More external, all types combined

Enterprise One analytics leader, one support group

Leadership Experimentation, deliberation, investment

Targets Get something going that matters

Technology Hadoop etc., multiple storage options

Analysts Different roles and tracks, but everybody together

Big Data Technologies

18

Hadoop, Pig, Hive, etc. for spreading big data processing across massively parallel servers

In-memory processing, in-database analytics Machine learning for rapid model generation and

testing

Natural language processing Visual analytics software Storage and processing options

Hadoop Traditional data warehouse or mart Discovery platform

Cloud-based analytics

Who Is Working with Big Data?

19

Small startups On West or E. Coasts In online, media, healthcare Big data only Product/service focus

Big firms Traditional or online businesses Variety of industries Big + small data analytics Need new management model

for the combination

Analytics 1.0

20

1.0

Traditional Analytics

Primarily descriptive analytics and reporting

Internally sourced, relatively small, structureddata

Back room teams of analysts Internal decision support focus Slow models and decisions

Analytics 2.0

21

Complex, large, unstructured data about customers

New analytical and computational capabilities

Data Scientists emerge Online firms create data-based products

and services Online data tracked relentlessly

2.0

The Big Data Era

Analytics 3.0

22

3.0

Fast, Pervasive Analytics at Scale

A seamless blend of traditional analytics and big data

Analytics integral to the business, everybodys job

Rapid, agile insight and model delivery Analytical tools available at point of decision Companies use analytics for decisions at scale

and analytics-based products and services

TODAY

3.0 Obstacles

23

Front-line workers who dont want analytics and big data to tell them how to do their jobs

Product managers who dont understand data products

Customers and partners who think they own the data

Internal managers and customers who dont understand analytics

Managers who dont like black box decisions

3.0 Companies, Old and New

24

Procter & Gamble (177)

Schneider Electric (171)

GE (121) JP Morgan

Chase (119)

Ford (111) UPS (108)

Centenarians

Intuit (31) Google (16) LinkedIn (11) EnerNOC (13) Facebook (10) Foundation

Medicine (5)

Zillow (9)

Youngsters

25 | 2014 Thomas H. Davenport All Rights Reserved

Questions?

OCTOBER 17, 2012

To ask a question click on the question icon in the lower-right corner of your screen.

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This webinar was made possible by the generous support of SAS.

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www.sas.com/bigdata

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MARCH 3, 2014