Big Data
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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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MARCH 3, 2014
Big Data at Work: Dispelling the Myths, Uncovering the Opportunities Thomas Davenport President’s Distinguished Professor Management and Information Technology Babson College Author of Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
MARCH 3, 2014
#HBRwebinar @HBRExchange
Big Data at Work: Dispelling the Myths, Uncovering the Opportunities Thomas Davenport President’s Distinguished Professor Management and Information Technology Babson College Author of Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
MARCH 3, 2014
#HBRwebinar @HBRExchange
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 definition Too big for a single server Too unstructured for a relational database Too 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-making Evidence-based, fast, continuous decisions
8 | 2013 © Thomas H. Davenport All Rights Reserved
What to Do with All This Stuff? Global data storage Exabyte
Global data storage Exabytes
8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 2005
06
07
08
09
10
11
12
13
14
2015
About 0.5% of this data is analyzed in any way! SOURCE: McKinsey Global Institute ; Digital Universe Study, IDC 9
Industries and Their Use of Big Data
Extensive Data Streams from Operations/ Customer Relationships
Underachieving
Big Data Competitors
Telecom
Investments
Health Care Disadvantaged
CPG Overachieving
Limited
Limited
Extensive
Use of Data for Decision-Making and Products/Services 10
Functions and Their Use of Big Data
Extensive Data Streams from Operations/ Customer Relationships
Underachieving
Big Data Competitors
Finance, Sales
Marketing
HR Disadvantaged
Operations Overachieving
Limited
Limited
Extensive
Use of Data for Decision-Making and Products/Services 11
What Can You Do with Big Data? 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)
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How to Prospect for Big Data Projects
Big pile of data
Big pile of business/customer problems
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Where Are Your Big Data Applications? Discovery
Production
Cost savings Faster decisions New decisions Products/services
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Who’s in Charge? 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
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Building Big Data Capabilities
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
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Actions in Each DELTTA Category 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
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Big Data Technologies 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 18
Who Is Working with Big Data?
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
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Analytics 1.0 Traditional Analytics
1.0
Primarily descriptive analytics and reporting
Internally sourced, relatively small, structured data
“Back room” teams of analysts
Internal decision support focus
Slow models and decisions
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Analytics 2.0 The Big Data Era
2.0
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
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Analytics 3.0 Fast, Pervasive Analytics at Scale
3.0
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
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3.0 Obstacles 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
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3.0 Companies, Old and New Centenarians Procter & Gamble (177) Schneider Electric (171) GE (121) JP Morgan Chase (119) Ford (111) UPS (108)
Youngsters Intuit (31) Google (16) LinkedIn (11) EnerNOC (13) Facebook (10) Foundation Medicine (5) Zillow (9)
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25 | 2014 © Thomas H. Davenport All Rights Reserved
Questions? To ask a question … click on the “question icon” in the lower-right corner of your screen.
OCTOBER 17, 2012
Thank you for joining us! This webinar was made possible by the generous support of SAS. Learn more at www.sas.com/bigdata
MARCH 3, 2014 In collaboration with
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