4 - A team game

Question 1

What is the most important qualification for success in data transformation?

Answer 1

A willingness to do things differently.

Question 2

What are the three key quotients mentioned for a successful data team?

Answer 2

• Practical quotient (PQ)
• Adaptability quotient (AQ)
• Retention quotient (RQ)

Question 3

What does cognitive diversity refer to?

Answer 3

The inclusion of people with different perspectives and problem-solving approaches.

Question 4

What is the standard recruitment process often criticized for?

Answer 4

Focusing on acquired skills.

Question 5

Who was the first known Chief Data Officer (CDO)?

Answer 5

Cathryne Clay Doss, appointed in 2002 by Capital One.

Question 6

What role does a CDO play in a company?

Answer 6

Leads the data team.

Question 7

What is one skill that team members need according to the text?

Answer 7

The ability to link abstract ideas to real life.

Question 8

True or False: Technical skills alone are sufficient for success in data transformation.

Answer 8

False.

Question 9

What should be avoided in team recruitment for data transformation?

Answer 9

Focusing solely on technical skills like programming languages.

Question 10

Fill in the blank: The ability to __________ is crucial for problem-solving in data transformation.

Answer 10

adapt

Question 11

What does the term ‘Turingness’ refer to in the context of team attributes?

Answer 11

The ability to think creatively and apply abstract ideas to real-world problems.

Question 12

What is the significance of teamwork under time pressure in data projects?

Answer 12

It enhances practical problem-solving skills.

Question 13

What is a common issue with résumés in the recruitment process?

Answer 13

They are often optimized for algorithms rather than reflecting true problem-solving skills.

Question 14

What experience did the author have that sparked an interest in problem-solving?

Answer 14

Participating in the Airmen Selection Test (AST) at age nine.

Question 15

What does the author suggest is often the real constraint to success in data projects?

Answer 15

A lack of willingness to rethink and innovate.

Question 16

What is the ‘résumé problem’ in recruitment?

Answer 16

Résumés focus on learned skills rather than problem-solving abilities.

Question 17

What did the author learn from rowing at an international level?

Answer 17

The value of teamwork and discipline in problem-solving.

Question 18

What can a successful data team member be compared to in terms of skills?

Answer 18

• Chemist
• Entrepreneur
• Engineer

Question 19

According to the text, what is often the outcome of ‘code-first’ thinking in data projects?

Answer 19

Lack of progress due to unclear problem definitions.

Question 20

What is the impact of previous failed attempts at data projects on new initiatives?

Answer 20

They create a scarring effect, leading to distrust in new teams.

Question 21

What is a key attribute that the author looks for in team members?

Answer 21

The ability to share and communicate effectively.

Question 22

What is a potential downside of quick solutions in problem-solving?

Answer 22

They might add another layer of dysfunction that complicates future problem-solving.

Quick fixes can lead to recurring issues in organizational problems.

Question 23

What skill set should recruiters prioritize when building a team?

Answer 23

Business focus, ability to share and discuss problems, holistic decision-making.

Wider skill sets are preferred over a narrow ‘code-first’ mentality.

Question 24

What does RQ stand for in the context of recruiting?

Answer 24

Retention Quotient.

RQ refers to the ability to store and recall information easily.

Question 25

Why are traditional intelligence measures like IQ and RQ not sufficient in recruiting?

Answer 25

They do not encompass emotional intelligence (EQ), practical quotient (PQ), and adaptability quotient (AQ). ## Footnote A balance of these quotients is necessary for effective team dynamics.

Question 26

Define Practical Quotient (PQ).

Answer 26

The ability to think about problem-solving in a business-related manner and solve data problems effectively. ## Footnote High PQ relates to conceptualizing and delivering solutions.

Question 27

What is Adaptability Quotient (AQ)?

Answer 27

The ability to adapt thinking across different domains, such as programming in multiple languages. ## Footnote High AQ indicates flexibility in problem-solving.

Question 28

What is the 'heaven and hell problem'?

Answer 28

A scenario where one must choose between two doors, one leading to a solution and the other to failure, with one person lying and one telling the truth. ## Footnote This problem tests critical thinking and problem breakdown skills.

Question 29

What is a good way to describe a car from a data perspective?

Answer 29

Different types, classification by make and model, functionality, and the information provided by its dashboard. ## Footnote This approach emphasizes understanding data definitions and user needs.

Question 30

What should you be cautious of when presented with pre-selected team members from HR?

Answer 30

Question why they are available and avoid inheriting overly specialized skills. ## Footnote Specialists may not fit the broader needs of your team.

Question 31

What is cognitive diversity?

Answer 31

Differences in how people prefer to think, focusing on problem-solving styles rather than background diversity. ## Footnote It includes various thinking preferences such as analytical, practical, relational, and experimental.

Question 32

Name the four modes of thinking according to the Herrmann model.

Answer 32

* Analytical * Practical * Relational * Experimental ## Footnote Each mode represents different problem-solving approaches.

Question 33

What is a key reason data teams fail to deliver value?

Answer 33

Lack of alignment of roles to create business value and unclear purpose of roles. ## Footnote Proper structure and clarity are essential for success.

Question 34

What should be ensured when creating a data and analytics team?

Answer 34

All capabilities are represented in the overall structure and no capability gaps exist. ## Footnote This ensures comprehensive problem-solving and effectiveness.

Question 35

What does data product management involve?

Answer 35

Ownership of data products and ensuring their alignment with business needs. ## Footnote This role is crucial for successful data implementation.

Question 36

True or False: Cognitive diversity can help prevent a team from agreeing on a single answer to every problem.

Answer 36

True. ## Footnote Diverse thinking promotes varied solutions and creativity.

Question 37

What is the main purpose of having a clear data process?

Answer 37

To enable teams to know how they work together.

Question 38

What are the key activities of Data Product Management?

Answer 38

* Business problems are clearly expressed and understood by the business. * Detailed requirements are written and signed off by the business. * Pipeline of all work is managed and prioritized. * Roadmap for each product is managed and aligned to the overall business strategy. * Data roadmaps are communicated to customers, suppliers, execs, business sponsors, data stewards, and practitioners.

Question 39

Who does the Data Product Management team interact with?

Answer 39

* Data science * Data engineering * Data operations * Data architecture

Question 40

What is the role of the Data Science team?

Answer 40

To develop and continuously improve models, AI, and ML capabilities that deliver value to the business.

Question 41

What are the key activities of the Data Science team?

Answer 41

* Best practice data science capabilities are developed. * The 'art of the possible' use of DS is understood and explained. * Data models, statistical models, and algorithms are developed. * Models and algorithms are kept up to date. * Latest data sets from data engineering are used. * Business recommendations are made based on data models.

Question 42

What are the key activities of the Data Engineering team?

Answer 42

* Product roadmaps are followed for capturing data sources and integrating with the data model and data infrastructure. * Data quality and data roadmaps are used across the organization. * Quality and governance of all data are managed to a high standard. * Data products such as warehouse, data lake, or APIs are developed. * APIs and data feeds are built. * Dashboards and reports are developed in line with product roadmaps.

Question 43

What is the focus of Data Security and Privacy?

Answer 43

To ensure that the company’s data assets are protected, secure, and used ethically.

Question 44

What are the key activities related to Data Security and Privacy?

Answer 44

* Data privacy and data security policies are written and maintained. * Data is properly classified and used in line with company privacy and data security policies. * GDPR and other data regulations are understood and implemented. * Privacy and security are designed into all data products.

Question 45

What is the role of Data Operations?

Answer 45

To ensure that data products are deployed, accessible, and properly supported across the business, customers, and suppliers.

Question 46

What are the key activities of Data Operations?

Answer 46

* Data products are accessible by the intended audience during agreed operating hours. * The environment for operating data products is stable and performant. * Data products are supported in line with SLAs.

Question 47

What are the key activities of the Data Architecture team?

Answer 47

* All data activities have the right environment to capture, store, transmit, manage, and access data efficiently. * All data tools support an integrated, end-to-end data process. * The data model is designed and used as the single view of data across the organization. * Data across platforms and applications is integrated using APIs and data feeds.

Question 48

What is the role of Data Oversight?

Answer 48

To ensure independent oversight of data capabilities and operations.

Question 49

What are the key activities of Data Oversight?

Answer 49

* The work by data teams is independently scrutinized for alignment to business goals and objectives. * Data teams are supported and roadblocks removed.

Question 50

What is the focus of the Data Community?

Answer 50

To ensure data is at the heart of everything and that the work of data teams is given relevant and specific feedback.

Question 51

What are the key activities of the Data Community?

Answer 51

* Specific and relevant input to products, product roadmap, and projects. * Data teams’ output is tested.

Question 52

What are the limiting factors for the success of a data team?

Answer 52

* Lack of alignment of roles to creating business value. * Clarity and purpose of roles. * Ensuring nothing falls through the cracks between different roles.

Question 53

True or False: Coding skills are more important than problem-solving ability in leading a data team.

Answer 53

False

Question 54

Fill in the blank: When recruiting, cast a _______.

Answer 54

[wide net]

Question 55

What is vital to the success of a data team job?

Answer 55

Interaction with the entire business, not just those who understand data.