Big data_郭惠民

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Big Data : The next frontier for innovation,

competition, and productivity

McKinsey Global Institute

報告人：郭惠民　 2012/06/26

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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

What is 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).

What do we mean by "big data"?

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Big Data







Growth and value creation

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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

Growth and value creation - 1

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Big Data







Big Data Techniques and Technologies

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A/B Testing Association rule learning Classification Cluster analysis Crowdsourcing Data fusion and data integration Data mining Ensemble learning Genetic algorithms Machine learning Natural language processing (NLP) Neural networks Network analysis

Techniques Optimization Pattern recognition Predictive modeling Regression Sentiment analysis Signal processing Spatial analysis Statistics Supervised learning Simulation Time series analysis Unsupervised learning Visualization


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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

Techniques


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Big Table Business intelligence (BI) Cassandra Cloud computing Data mart Data warehouse Distributed system Dynamo Extract, transform, and load (ETL) Google File System Hadoop HBase

Technologies MapReduce Mashup Metadata Non-relational database R Relational database Semi-structured data SQL Stream processing Structured data Unstructured data Visualization


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Technologies

Cloud computing, Distributed system, Hadoop, MapReduce, R, Stream processing

Structured data, Semi-structured data, Unstructured data

Google File System, Dynamo.

Data Characteristic and Storage system

Relational database, SQL, Big Table, HBase, Cassandra, Non-relational database,, Metadata.

Database

Computing Model and Programming Language

Business intelligence (BI), Data mart, Data warehouse, Mashup Extract, transform, and load (ETL), Visualization

Business Intelligence and Software Tools

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Big Data







The transformative potential of big data

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Health care (United States)

Public sector administration (European Union)

Retail (United States)

Manufacturing (global)

Personal location data (global)

The transformative potential of big data

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Big Data







Key findings that apply across sectors

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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

Key Findings -1

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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

Key Findings - 2

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Key Findings - 2

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Key Findings - 3

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Key Findings - 4

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Key Findings - 4

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Key Findings - 4

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Key Findings - 4

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Key Findings - 5

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Several issues will have to be addressed to capture the full potential of big dataData policies

Technology and techniques

Organizational change and talent

Access to data

Industry structure

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Big Data







Implications for organization leaders

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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

Implications for policy makers

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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

Executive summary

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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

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