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
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
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
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