Big Data :The next frontier for innovation, competition, and productivity      McKinsey Global Institute        報告人:郭惠民   ...
Contents1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potenti...
What is Big data?What do we mean by "big data"?“Big data” refers to datasets whose size is beyond the ability of typicalda...
Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potenti...
Growth and value creation The volume of data is growing at an exponential  rate The intensity of big data varies across ...
Growth and value creation - 1            6
Growth and value creation - 1            7
Growth and value creation - 2            8
Growth and value creation - 2            9
Growth and value creation - 3            10
Growth and value creation - 3            11
Growth and value creation - 4            12
Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potenti...
Big Data Techniques and TechnologiesTechniques   A/B Testing                            Optimization   Association rule...
Big Data Techniques and TechnologiesTechniques Data Mining, Data Warehousing, Business Intelligence    Association rule ...
Big Data Techniques and TechnologiesTechnologies   Big Table                                MapReduce   Business intell...
Big Data Techniques and TechnologiesTechnologies           Business Intelligence and Software Tools    Business intelligen...
Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potenti...
The transformative potential of big data Health care (United States) Public sector administration (European Union) Reta...
The transformative potential of big data                  20
Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potenti...
Key findings that apply across sectors big data creates value in several ways While the use of big data will matter acro...
Key Findings -1big data creates value in several ways Creating transparency Enabling experimentation to discover needs, ...
Key Findings - 2      24
Key Findings - 2      25
Key Findings - 3      26
Key Findings - 4      27
Key Findings - 4      28
Key Findings - 4      29
Key Findings - 4      30
Key Findings - 5Several issues will have to be addressed tocapture the full potential of big dataData policiesTechnology...
Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potenti...
Implications for organization leaders Inventory data assets: proprietary, public, and  purchased Identify potential valu...
Implications for policy makers Build human capital for big data Align incentives to promote data sharing for the  greate...
Executive summary Data have swept into every industry and business  function and are now an important factor of productio...
簡報完畢敬請指導 36
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Big data_郭惠民

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Big data_郭惠民

  1. 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. 2. Contents1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potential of big data in five domains4.Key findings that apply across sectors5.Implications for organization leaders6.Implications for policy makers 2
  3. 3. What is Big data?What do we mean by "big data"?“Big data” refers to datasets whose size is beyond the ability of typicaldatabase software tools to capture, store, manage, and analyze.This definition is intentionally subjective and incorporates a movingdefinition of how big a dataset needs to be in order to be consideredbig data—i.e., we don’t define big data in terms of being larger than acertain number of terabytes (thousands of gigabytes).We assume that, as technology advances over time, the size ofdatasets that qualify as big data will also increase. Also note that thedefinition can vary by sector, depending on what kinds of software toolsare commonly available and what sizes of datasets are common in aparticular industry. With those caveats, big data in many sectors todaywill range from a few dozen terabytes to multiple petabytes (thousandsof terabytes).
  4. 4. Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potential of big data in five domains4.Key findings that apply across sectors5.Implications for organization leaders6.Implications for policy makers 4
  5. 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. 6. Growth and value creation - 1 6
  7. 7. Growth and value creation - 1 7
  8. 8. Growth and value creation - 2 8
  9. 9. Growth and value creation - 2 9
  10. 10. Growth and value creation - 3 10
  11. 11. Growth and value creation - 3 11
  12. 12. Growth and value creation - 4 12
  13. 13. Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potential of big data in five domains4.Key findings that apply across sectors5.Implications for organization leaders6.Implications for policy makers 13
  14. 14. Big Data Techniques and TechnologiesTechniques 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. 15. Big Data Techniques and TechnologiesTechniques 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. 16. Big Data Techniques and TechnologiesTechnologies 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. 17. Big Data Techniques and TechnologiesTechnologies 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. 18. Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potential of big data in five domains4.Key findings that apply across sectors5.Implications for organization leaders6.Implications for policy makers 18
  19. 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. 20. The transformative potential of big data 20
  21. 21. Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potential of big data in five domains4.Key findings that apply across sectors5.Implications for organization leaders6.Implications for policy makers 21
  22. 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. 23. Key Findings -1big 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. 24. Key Findings - 2 24
  25. 25. Key Findings - 2 25
  26. 26. Key Findings - 3 26
  27. 27. Key Findings - 4 27
  28. 28. Key Findings - 4 28
  29. 29. Key Findings - 4 29
  30. 30. Key Findings - 4 30
  31. 31. Key Findings - 5Several issues will have to be addressed tocapture the full potential of big dataData policiesTechnology and techniquesOrganizational change and talentAccess to dataIndustry structure 31
  32. 32. Big Data1.Mapping global data: Growth and value creation2.Big data techniques and technologies3.The transformative potential of big data in five domains4.Key findings that apply across sectors5.Implications for organization leaders6.Implications for policy makers 32
  33. 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. 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. 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. 36. 簡報完畢敬請指導 36

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