This document introduces the concept of big data by defining it as data sets that are too large or complex for traditional data processing tools to handle due to their volume, velocity and variety. It describes the four characteristics (4Vs) of big data as volume, velocity, variety and veracity. Additionally, it identifies key enablers that have contributed to the growth of big data, such as increases in data storage capacity, processing power, and data availability from various sources like social networks and mobile devices. Finally, the document discusses techniques for analyzing big data and provides examples of domains like healthcare, retail and manufacturing that are being transformed by its potential.

1. Introduction to Big Data
Ma XinJie
Signal Processing for Big Data
2016/09/13
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2. Big-Data in numbers
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3. Big-Data in numbers
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4. What is Big Data?
• There is not a consensus as to how to define Big Data
“A collection of data sets so large and complex that it becomes difficult to
process using on-hand database management tools or traditional data processing
applications.” – wiki.
“Big data refers to data sets whose size is beyond the ability of typical database
software tools to capture, store, manage and analyze.” - The McKinsey Global
Institute, 2011.
#1. Not defined as a certain number
#2. Can vary by sector
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6. Characterization of Big Data:
volume, velocity, variety, Veracity (V4)
• Volume – challenging to load and process (how to index, retrieve)
• Variety – different data types and degree of structure (how to
query semi-structured data)
• Velocity – real-time processing influenced by rate of data arrival
• Veracity (verifying inference-based models from comprehensive
data collections)
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7. Characterization of Big Data:
volume, velocity, variety, Veracity (V4)
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8. 7 Key Insights
• 1. Data have swept into every industry and business
function and are now an important factor of production.
• 2. Big Data Creates Value In Several Ways
• 3. use of big data will become a key basis of competition
and growth for individual firms
• 4. The Use Of Big Data Will Underpin New Waves Of
Productivity Growth And Consumer Surplus
• 5. While The Use Of Big Data Will Matter Across Sectors,
Some Sectors Are Poised For Greater Gains
• 6. There Will Be A Shortage Of Talent Necessary For
Organizations To Take Advantage Of Big Data
• 7. Several Issues Will Have To Be Addressed To Capture The
Full Potential Of Big Data
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9. Mapping global data: Growth and
value creation
• Why Big-Data?
• Key enablers for the appearance and growth
of “Big Data” are:
– Increase of storage capacities
– Increase of processing power
– Availability of data
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10. Enabler: Data storage
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11. Enabler: Computation capacity
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12. Enabler: Data availability
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13. Type of available data
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14. Data available from social networks
and mobile devices
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15. Big data techniques and technologies
• What matters when dealing with data?
Scalability
Streaming
Context
Quality
Usage
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16. Techniques for analyzing big data
• Association rule learning
• Classification
• Cluster analysis
• Data fusion and data integration
• Data mining
• Genetic algorithms
• Regression
• Signal processing
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17. Big data technologies
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18. The transformative potential of big
data in five domains
• 5 representative domains where the
importance of big data is growing
• Health care (United States)
• Public sector administration (European Union)
• Retail (United States)
• Manufacturing (global)
• Personal location data (global)
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19. Key findings that apply across sectors
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22. Thanks
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