Lecture_3

Question 1

When we start to map value in data ecosystem, what is the first thing need to be considered?

Answer 1

The value of data. How unique the data is and how to use the data by whom.

Question 2

What are the potential problems as the data ecosystem has been evolving?

Answer 2

Raw data and actual application of data-derived insights are totally different.

Question 3

What is non-rivalrous nature of data?

Answer 3

One of the uniqueness of data asset. It means data can be sued by multiple parties simultaneously.

Question 4

What is sheer-diversity

Answer 4

one of the uniqueness of data asset. Including data type,

Question 5

What is the data use case for cost & revenue optimization?

Answer 5

Predicitve maintenance, product improvements

Question 6

What is the data use case for marketing & advertising

Answer 6

By analyzing customer transactional & behavior data from multiple sources.

Question 7

What is the data use case for marketing intelligence?

Answer 7

Data is used to deliver strategic insight such as where does the next campaign occur.

Question 8

What is the data use case for makret-making?

Answer 8

Use data to match clients’ needs and develop efficient matching.

Question 9

What is the data use case for AI training

Answer 9

Machine learning requires hug quantities of training data.

Question 10

Five steps for a functional ecosystem.

Answer 10

1. data generation & collection.
2. data aggregation.
3. data analysis
4. data infrastructure

Question 11

What is data genration role?

Answer 11

Source and platform where data are initially captured.

Question 12

What is data aggregation?

Answer 12

Process and platforms for combining or clean up the data from multiple sources.

Question 13

What is data analysis?

Answer 13

Visualization data. Collect insight from the data.

Question 14

What is data infrastructure?

Answer 14

Involving hardware & software

Question 15

When do factors drive data value up at data genration stage?

Answer 15

Certain data type will have higher value if collection barries are extremely high or data can’t be legally shared between parties.

Question 16

When do factors drive data value down at data generation stage?

Answer 16

Growth in available proxies and expansion of open access will increase suuply.

Question 17

When do factors drive data value up at data aggregation stage?

Answer 17

More applications being developed and aggregation process is technically challenging or requires a neutral third party

Question 18

When do factors drive data value down at data aggregation stage?

Answer 18

Technology makes data aggregation easier.

Question 19

When do factors drive data value up at data analysis stage?

Answer 19

Talent shortage.
Deep sector expertise needed.
Close relationshiop to actual use or implementation clarifies value.

Question 20

When do factors drive data value down at data analysis stage?

Answer 20

Scope could be limited as solutions will be for vertical applications.

Question 21

The data generation and collection value will go down, while data analysis value will go up and data aggregation will remain the same.

Answer 21

True.

Question 22

Credit card application ecosystem

Answer 22

See slide 17

Question 23

What drive data collection?

Answer 23

Supply and demand

Question 24

As supply of data wil expand, the generation of raw data will become less valuable.

Answer 24

True

Question 25

In supply site, the market is shaped by

Answer 25

Difficulty of collection, access and availability of substitute data

Question 26

On the demand side, the market is shaped by

Answer 26

Easy of use, network effects, and value of the ultimate uses of data.

Question 27

When we need a disruptive model?

Answer 27

Assets are underutilized. Supply/demand mismatch. Dependence on large amounts of personalized data. Large value in combining data from multiple sources. R&D is core to the business model. Decision making is subject to human biases. Speed of decision making limited by human constraints. Large value associated with improving accuracy of prediction.

Question 28

Orthogonal data can be used in

Answer 28

Business model such as insurance and health care companies.

Question 29

Example of orthogonal data usage

Answer 29

Usually health care companies relied on medical histories and examination, but with leveraging telematics data, news sets of orthogonal data can gain insights into customer behavior.

Question 30

What is hyperscale real-time matching?

Answer 30

Hyperscale digital platforms can use data and analytics to meet both types of needs and have notable impact when: 1. demand and supply fluctuate frequently. 2. Poor signaling mechanisms produce slow matches. 3. Supply side assets are under utilized.

Question 31

What is the use case for hyperscale real-time matching?

Answer 31

Transprotation disruption. Use supply and demand fluctuate

Question 32

Use case for radical personalization

Answer 32

Tailored offers for different customers based on their preference and characteristics

Question 33

What is the first step in creating value from data?

Answer 33

Ensure access to all relevant data.

Question 34

How to define data lake?

Answer 34

Integrate all data from structured, unstructured to internal and external

Question 35

Analytics can improve four aspects of decision making.

Answer 35

1. Speed/adaptability 2. Accuracy. 3. Consistency/reliability 4. Transparency

Question 36

Data and analytics can overcome human limitations in decision making

Answer 36

True

Question 37

Archetype of disruption

Answer 37

1. Business models enabled by orthogonal data. 2. Hyperscale, real-time matching. 3. Radical personalization. 4. Massive data intergration capabilities. 5. Data-driven discovery. 6. Enhanced decision making.

Question 38

What is the use case for data-driven discovery?

Answer 38

Data and algorithms can enhance innovation process.