1. Big Data :
The next frontier for innovation,
competition, and productivity
McKinsey Global Institute
報告人：郭惠民
2012/06/26
1

2. Contents
1.Mapping global data: Growth and value creation
2.Big data techniques and technologies
3.The transformative potential of big data in five
domains
4.Key findings that apply across sectors
5.Implications for organization leaders
6.Implications for policy makers
2

3. What is Big data?
What do we mean by "big data"?
“Big data” refers to datasets whose size is beyond the ability of typical
database software tools to capture, store, manage, and analyze.
This definition is intentionally subjective and incorporates a moving
definition of how big a dataset needs to be in order to be considered
big data—i.e., we don’t define big data in terms of being larger than a
certain number of terabytes (thousands of gigabytes).
We assume that, as technology advances over time, the size of
datasets that qualify as big data will also increase. Also note that the
definition can vary by sector, depending on what kinds of software tools
are commonly available and what sizes of datasets are common in a
particular industry. With those caveats, big data in many sectors today
will range from a few dozen terabytes to multiple petabytes (thousands
of terabytes).

4. Big Data
1.Mapping global data: Growth and value creation
2.Big data techniques and technologies
3.The transformative potential of big data in five
domains
4.Key findings that apply across sectors
5.Implications for organization leaders
6.Implications for policy makers
4

5. Growth and value creation
 The volume of data is growing at an exponential
rate
 The intensity of big data varies across sectors but
has reached critical mass in every sector
 Major established trends will continue to drive
data growth
 Traditional uses of it have contributed to
productivity growth — big data is the next frontier
5

6. Growth and value creation - 1
6

7. Growth and value creation - 1
7

8. Growth and value creation - 2
8

9. Growth and value creation - 2
9

10. Growth and value creation - 3
10

11. Growth and value creation - 3
11

12. Growth and value creation - 4
12

13. Big Data
1.Mapping global data: Growth and value creation
2.Big data techniques and technologies
3.The transformative potential of big data in five
domains
4.Key findings that apply across sectors
5.Implications for organization leaders
6.Implications for policy makers
13

14. Big Data Techniques and Technologies
Techniques
 A/B Testing  Optimization
 Association rule learning  Pattern recognition
 Classification  Predictive modeling
 Cluster analysis  Regression
 Crowdsourcing  Sentiment analysis
 Data fusion and data integration  Signal processing
 Data mining  Spatial analysis
 Ensemble learning  Statistics
 Genetic algorithms  Supervised learning
 Machine learning  Simulation
 Natural language processing (NLP)  Time series analysis
 Neural networks  Unsupervised learning
 Network analysis  Visualization
14

15. Big Data Techniques and Technologies
Techniques
 Data Mining, Data Warehousing, Business Intelligence
 Association rule learning, Classification, Cluster analysis, Data fusion
and data integration
 Artificial Intelligence
 Machine learning, Supervised learning, Unsupervised learning,
Natural language processing (NLP), Neural networks, Ensemble
learning, Sentiment analysis
 Statistics, Algorithm, Operation Research
 Statistics, Simulation, Regression, Time series analysis, Genetic
algorithms, Optimization, Pattern recognition, Predictive modeling,
Spatial analysis
 Social Psychology, Cognition Science
 Crowdsourcing. A/B Testing, Network analysis
 Others
 Signal processing, Visualization
15

16. Big Data Techniques and Technologies
Technologies
 Big Table  MapReduce
 Business intelligence (BI)  Mashup
 Cassandra  Metadata
 Cloud computing  Non-relational database
 Data mart  R
 Data warehouse  Relational database
 Distributed system  Semi-structured data
 Dynamo  SQL
 Extract, transform, and load (ETL)  Stream processing
 Google File System  Structured data
 Hadoop  Unstructured data
 HBase  Visualization
16

17. Big Data Techniques and Technologies
Technologies
Business Intelligence and Software Tools
Business intelligence (BI), Data mart, Data warehouse, Mashup
Extract, transform, and load (ETL), Visualization
Computing Model and Programming Language
Cloud computing, Distributed system, Hadoop, MapReduce, R,
Stream processing
Database
Relational database, SQL, Big Table, HBase, Cassandra,
Non-relational database,, Metadata.
Data Characteristic and Storage system
Structured data, Semi-structured data, Unstructured data
Google File System, Dynamo.
17

18. Big Data
1.Mapping global data: Growth and value creation
2.Big data techniques and technologies
3.The transformative potential of big data in five
domains
4.Key findings that apply across sectors
5.Implications for organization leaders
6.Implications for policy makers
18

19. The transformative potential of big data
 Health care (United States)
 Public sector administration (European Union)
 Retail (United States)
 Manufacturing (global)
 Personal location data (global)
19

20. The transformative potential of big data
20

21. Big Data
1.Mapping global data: Growth and value creation
2.Big data techniques and technologies
3.The transformative potential of big data in five
domains
4.Key findings that apply across sectors
5.Implications for organization leaders
6.Implications for policy makers
21

22. Key findings that apply across sectors
 big data creates value in several ways
 While the use of big data will matter across sectors,
some sectors are poised for greater gains
 Big data offers very large potential to generate value
globally, but some geographies could gain first
 There will be a shortage of the talent organizations
need to take advantage of big data
 Several issues will have to be addressed to capture
the full potential of big data
22

23. Key Findings -1
big data creates value in several ways
 Creating transparency
 Enabling experimentation to discover needs,
expose variability, and improve performance
 Segmenting populations to customize actions
 Replacing/supporting human decision making
with automated algorithms
 Innovating new business models, products and
services
23

24. Key Findings - 2
24

25. Key Findings - 2
25

26. Key Findings - 3
26

27. Key Findings - 4
27

28. Key Findings - 4
28

29. Key Findings - 4
29

30. Key Findings - 4
30

31. Key Findings - 5
Several issues will have to be addressed to
capture the full potential of big data
Data policies
Technology and techniques
Organizational change and talent
Access to data
Industry structure
31

32. Big Data
1.Mapping global data: Growth and value creation
2.Big data techniques and technologies
3.The transformative potential of big data in five
domains
4.Key findings that apply across sectors
5.Implications for organization leaders
6.Implications for policy makers
32

33. Implications for organization leaders
 Inventory data assets: proprietary, public, and
purchased
 Identify potential value creation opportunities
and threats
 Build up internal capabilities to create a data-
driven organization
 develop enterprise information strategy to
implement technology
 Address data policy issues
33

34. Implications for policy makers
 Build human capital for big data
 Align incentives to promote data sharing for the
greater good
 Develop policies that balance the interests of
companies wanting to create value from data and
citizens wanting to protect their privacy and security
 Establish effective intellectual property frameworks
to ensure innovation
 Address technology barriers and accelerate R&D in
targeted areas
 Ensure investments in underlying information and
communication technology infrastructure
34

35. Executive summary
 Data have swept into every industry and business
function and are now an important factor of production
 Big data creates value in several ways
 Use of big data will become a key basis of competition
and growth for individual firms
 The use of big data will underpin new waves of
productivity growth and consumer surplus
 While the use of big data will matter across sectors, some
sectors are poised for greater gains
 There will be a shortage of talent necessary for
organizations to take advantage of big data
 Several issues will have to be addressed to capture the
full potential of big data
35

36. 簡報完畢
敬請指導
36