Session 6: Value of AI and Analytics

Question 1

the Four V’s of Big data

Answer 1

1. volume: scale of data
2. variety: different forms of data
3. velocity: analysis of streaming data
   veracity: uncertainty of data (quality of data)

Question 2

3 approaches to monetize their data

Answer 2

1. improving internal business processes and decisions
2. wrapping information around core products and services (improving a given product)
3. selling information offering to new and existing markets (selling data)

Question 3

two major obstacles when monetizing data

Answer 3

1. the accessibility and quality of the data: cannot monetize data no one can use
2. the lack of accountability: All three approaches to data monetizing require committed leaders

Question 4

What are data brokers

Answer 4

sell and buy personal data. Key aspects: advertising technology, business IT, risk data, Marketing data, customer management

Question 5

two ways to infer causality

Answer 5

1. observational data: assumptions and data to deal with the lack of random data
2. experimental data: random assignment to different conditions (a/b testing)

Question 6

what is churn management

Answer 6

identify the valuable customers who are likely to churn from a company and executing proactive steps to retain them

Question 7

three levels of analytic prowess (expertise)

Answer 7

1. Aspirational: basic analytics, for financial and supply chain, use spreadsheets, silos data
2. Experienced: to guide strategy, daily operations, use analytics tools
3. transformed: sophisticated analytics users, enterprise data are integrated, use comprehensive portfolio tool

Question 8

6 Challenges in creating value from business analytics projects

Answer 8

1. data: manage and availability of data
2. value: using analytics for improved decision making; measuring customer value impact
3. people: skill shortage
4. technology: restriction of existing platforms
5. process: producing credible analytics
6. organization : overcome resistance to change; work with academia; big data and analytics strategy

Question 9

3 stages of AI

Answer 9

1. assisted intelligence: requires human assistance and interpretation
2. augmented intelligence: machine learning augments human decisions
3. autonomous intelligence: AI decides and executes autonomously

Question 10

5 main challenges of AI

Answer 10

1. data management
2. data access
3. ethical and unbiased AI
4. Talent
5. organizational and cultural changes

Question 11

4 major areas to use AI

Answer 11

1. automation: AI, IoT
2. better decisions:
3. Personalization
4. positive change

Question 12

Misinformation vs Disinformation vs Malinformaition

Answer 12

misinformation: unintentional mistakes
disinformation: fabricated or deliberately manipulated
malinformaition: abuse of private information with the intent to harm

Question 13

identify a situation when information adds value for an organization

Answer 13

transparency, concealing vs displaying mentioned in lecture.

Question 14

what is in the AI canvas

Answer 14

• prediction: identify the key uncertainty that you would like to resolve
• judgment: determine the payoffs to being right vs being wrong, consider both false positives and false negatives: how do you value different outcomes and errors?
• action: what are the action that can be chosen?
• outcome: what are your metrics for task success?
• training: what data do you need to train the predictive algo?
• Input: what data to you need to run the predictive algo?
• Feedback: how can you use the outcome to improve algo?
• how will this AI impact on the overall workflow?