Big Data

Question 1

Definition of Big Data

Answer 1

Set of data that includes the characteristics of the 3Vs (Vol, Vel, Var) to an extent that makes the data unsuitable for management based on relational DBMS

Question 2

Volume

Answer 2

Amount of data to be stored

Question 3

2 processes of Volume

Answer 3

Scaling up - updating existing systems
Scaling out - adding new servers

Question 4

Velocity

Answer 4

Speed of incoming data and processing

Question 5

Velocity of processing can be broken into 2 categories:

Answer 5

1. Stream processing - Processing of real time data through algorithms
2. Feedback loop - Analyzing data for actionable outcomes

Question 6

Example of feedback loop

Answer 6

1. User clicks on a book link
2. Data of the cust and book is captured
3. Data is analyzed to find out other items and books that users might be interested in
4. List of recommendations on items and books users might be interested in is listed
5. Information on the book requested, plus other recommendations is returned to the user

Question 7

Variety

Answer 7

The type of format the data is stored/captured as

Question 8

Diff. types of variety

Answer 8

1. Structured - Organized in a pre-defined format (Relational DBSM)
2. Unstructured - Data without specific format (multimedia files)
3. Semi-structured - A mix, with some structured elements

Question 9

Other characteristics

Answer 9

1. Veracity - Trustworthiness of data
2. Value - Usefulness for insights
3. Variability - Meaning of data can change with context
4. Visualization - Ability to present data clearly
5. Polyglot Persistence - Using diverse data storage technologies

Question 10

Hadoop ecosystem

Answer 10

The de facto standard for big data processing due to these 2 components:
1. Hadoop Distributed File System (HDFS) - Low-level distributed file system process used to manage big data sets across multiple computers.
2. MapReduce - programming model used to process large data sets

Question 11

Big Data in Fintech

Answer 11

1. Data Visualization - Custom dashboards for clear business insights
2. Data Mining - Analyzing large volume of unstructured data for insights
3. Real time processing - Track users’ activities for user analysis

Question 12

Data Analysis in Fintech

Answer 12

1. Risk analysis - Using AI/ML (Machine Learning) for better risk assessment
2. Decision making - Using AI/ML to decide the optimal course of actions in complex situations
3. Automation - Reduce delays, enhance customer service

Question 13

Data Security

Answer 13

1. Customer profile - Creates detailed profile for identity protection
2. Fraud Detection - AI can help prevent Fraud or prevent cyber threats

Question 14

Cust Centric Services

Answer 14

1. Personalization: AI analyzes behavior for tailored financial services.
2. Financial Advice: AI offers financial guidance similar to human advisors.

Question 15

Financial Inclusion

Answer 15

Cost Reduction: Automation lowers operational costs, enabling affordable financial services and promoting inclusion.

Question 16

Money Laundering & Terrorism Prevention

Answer 16

1. Real-Time Monitoring: AI analyzes transaction patterns to spot suspicious activities.
2. Anomaly Detection: Machine learning identifies irregular transactions efficiently.

Question 17

In-Branch Security

Answer 17

Incident Response: AI enhances security by analyzing breaches and providing preventative insights.