lOMoARcPSD|58847208
Big Data
(Lecture
Notes)
, lOMoARcPSD|58847208
What is Big Data
• Examples of Big Data; ERP, Card Readers, Television, Fridge
• No universal definition of what big data is (varies)
• Big Data = Always a notion of size (byte)
• 1 byte historical = 1 character (1000 bytes = 1000 characters)
Characteristics of Big Data (4Vs)
• Volume (Quantity of Data)
• Variety (Diversity of Data)
-Structured (Predefined data and format); understandable by
computers better than humans
-Unstructured (JPEG, Multimedia); understandable by humans better
than computers
-Semi-structured (HTML); partially understood by humans and
computers
• Velocity (How much data produced over time)
-important when you need to incorporate ‘REAL TIME DATA’
(GPS, CCTV)
-obsolete over time, data degenerates over time
• Veracity (Quality and trustworthiness of Data)
Garbage in, Garbage out
(GPS thinks you’re in location A when you are at B,
CCTV Data grainy, under exposed)
What is Analytics
, lOMoARcPSD|58847208
• Big data alone is useless
• Usefulness is determined by the analyzation of the data
Definition
“Process of developing actionable decisions based on insights
generated from (historical) data”
Process: Series of iterative steps
Action decisions: Analytics aims to support decision-making
Insights: Patterns obtained through analysis
Developing: Growing, not immediate
Historical: Stored Data (In parenthesis because in today’s world
people use both historical and real-time data)
• 3 Major Types
- What is Happening currently? (Descriptive analytics)
- What will Happen in the Future? – Empirical based on past data &
experiences (Predictive analytics)
- What to do about it? (Prescriptive analytics) – Especially in complex
situations, it will recommend an action; using descriptive and
predictive.
Critical Success Factors
2
, lOMoARcPSD|58847208
• Clear Need
- Valid (Using it in line with your company’s purpose)
-Invalid (Everyone use so I use)
• Strong, committed leadership
- Senior Leadership support; shows they value analytics
- Demonstrate they value it
- Incentives for employees to participate
Lack of buy-in from the top means success is lower
• Strong IT infrastructure
- Right tools
- Right people
Does not guarantee SUCCESS, merely INCREASES it.
3
Big Data
(Lecture
Notes)
, lOMoARcPSD|58847208
What is Big Data
• Examples of Big Data; ERP, Card Readers, Television, Fridge
• No universal definition of what big data is (varies)
• Big Data = Always a notion of size (byte)
• 1 byte historical = 1 character (1000 bytes = 1000 characters)
Characteristics of Big Data (4Vs)
• Volume (Quantity of Data)
• Variety (Diversity of Data)
-Structured (Predefined data and format); understandable by
computers better than humans
-Unstructured (JPEG, Multimedia); understandable by humans better
than computers
-Semi-structured (HTML); partially understood by humans and
computers
• Velocity (How much data produced over time)
-important when you need to incorporate ‘REAL TIME DATA’
(GPS, CCTV)
-obsolete over time, data degenerates over time
• Veracity (Quality and trustworthiness of Data)
Garbage in, Garbage out
(GPS thinks you’re in location A when you are at B,
CCTV Data grainy, under exposed)
What is Analytics
, lOMoARcPSD|58847208
• Big data alone is useless
• Usefulness is determined by the analyzation of the data
Definition
“Process of developing actionable decisions based on insights
generated from (historical) data”
Process: Series of iterative steps
Action decisions: Analytics aims to support decision-making
Insights: Patterns obtained through analysis
Developing: Growing, not immediate
Historical: Stored Data (In parenthesis because in today’s world
people use both historical and real-time data)
• 3 Major Types
- What is Happening currently? (Descriptive analytics)
- What will Happen in the Future? – Empirical based on past data &
experiences (Predictive analytics)
- What to do about it? (Prescriptive analytics) – Especially in complex
situations, it will recommend an action; using descriptive and
predictive.
Critical Success Factors
2
, lOMoARcPSD|58847208
• Clear Need
- Valid (Using it in line with your company’s purpose)
-Invalid (Everyone use so I use)
• Strong, committed leadership
- Senior Leadership support; shows they value analytics
- Demonstrate they value it
- Incentives for employees to participate
Lack of buy-in from the top means success is lower
• Strong IT infrastructure
- Right tools
- Right people
Does not guarantee SUCCESS, merely INCREASES it.
3
