big data at work: dispelling the myths, uncovering the...
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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
In collaboration with
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OCTOBER 17, 2012
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OCTOBER 17, 2012
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Thomas DavenportPresident’s 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 DavenportPresident’s 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
What’s 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)
Where Are Your Big Data Applications?
14
Discovery Production
Cost savings
Faster decisions
New decisions
Products/services
Who’s 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, unstructured
Enterprise . . . . . . . .integrated big and small data analytics
Leadership . . . . . . . . . . . . . . .passion and commitment
Targets . . . . . . . . . . . . . . . . . . where to start?
Technology. . . . . . . . new architectures
Analysts . . . . . 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, everybody’s 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 don’t want analytics and big data to tell them how to do their jobs
Product managers who don’t understand data products
Customers and partners who think they own the data
Internal managers and customers who don’t understand analytics
Managers who don’t 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
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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