Data Science book: questions

Test your knowledge of Data Science for Decision-Makers

These questions are meant to help you internalize the lessons imparted by the book Data Science for Decision-makers and Data Professionals. Want to test your knowledge after reading a chapter? We’ve prepared practice questions for every chapter. Order the book here if you don’t own a copy yet.

Chapter 1 questions

  1. What is the main goal of data science for organizations?
  2. What are the processes of the large and small BI cycles?
  3. What is the difference between contextual and transactional information?
  4. Explain why good decision-making often requires external information.
  5. Describe at least four (fundamental) developments in society and the economy that increase the importance of data science and explain them.
  6. Provide an example from everyday practice that proves that you can use data science for performance measurement and explain how it works.
  7. Describe in your own words why data science and change management are inextricably connected.
  8. Why is it difficult to calculate the returns of data science upfront?
  9. Describe how data science can accelerate and improve operational decision-making in organizations.
  10. How can algorithms help improve decisions? Provide an example.

Chapter 2 questions

  1. List the four maturity levels of data science and describe some of their characteristics. Describe how data science success relates to these maturity levels.
  2. An organization may offer new products and services without having achieved the fourth maturity level. What are some examples of things that could go wrong in this scenario?
  3. List the three most important characteristics from the definition of the intelligent, data-driven organization and how they relate to the human body.
  4. In what ways can an organism demonstrate adjusted behavior?
  5. Name the two most important effects of data science on organizational structures and explain them.
  6. What type of manager is most likely to resist data science initiatives and why?
  7. Describe, in your own words, the relationship between the PDCA cycle and data science.
  8. Explain why it’s important to start by improving the organization’s sensitivity.
  9. What, besides presenting information on the lower levels of the organization, is an important condition for flattening the organization?
  10. List the four key concepts that an intelligent, data-driven organization focuses on managing and explain why things can still go wrong if the organization lacks focus.

Chapter 3 questions

  1. What approaches can organizations use to determine their information needs? List several characteristics of each approach.
  2. What are critical success factors and what role can they play when determining the information need?
  3. What’s the difference between the critical success factors from the strategy map and the organization’s strategies?
  4. Come up with at least two extra possibilities presented by a strategy map.
  5. Describe the difference between a PI and a KPI.
  6. Why are KPIs (Key Performance Indicators) never purely financial?
  7. Describe at least three rules of thumb for determining valuable insights that can reveal great improvement potential.
  8. Besides defining indicators, what should an organization do to create a complete management model?
  9. On which points should information be cohesive and why is this important?
  10. Why do some people think that creating one version of the truth is impossible?
  11. What should intelligent organizations keep in mind when setting targets for indicators?
  12. Why is it important to establish the relationships between indicators?
  13. Describe three characteristics of data-driven working and describe the advantages in your own words.
  14. Name the three types of machine learning models and indicate the most important difference.
  15. Define the four Vs of Big Data and provide an example of a Big Data application.
  16. What does it mean for an organization to pursue an AI-first strategy?
  17. What is an important (first) step towards implementing an AI-first strategy?

Chapter 4 questions

  1. Draw the large BI cycle and the fifteen steps of the small BI cycle, and indicate where in your organization the biggest bottlenecks are (if applicable).
  2. What can be seen as the most important goal of the first five steps of the small BI cycle?
  3. Describe the ‘interpret” step of the small BI cycle.
  4. Provide some examples of mistakes, biases, and preconceptions that can factor into the internalization process.
  5. Describe the processors of the intelligent, data-driven organization and their typical information use.
  6. Why is it important that the processors use the same system?
  7. What rule of thumb can be applied to optimizing the refresh rate of reports?
  8. Name the two most important characteristics of a Business Intelligence Framework.
  9. Take the most important operational insight (KPI) in your organization and draw the organizational structure, designating the roles/functions that should have access to that insight.
  10. Describe at least 3 tips for improving decision management in your organization.
  11. Provide a clear definition of Artificial Intelligence (AI).
  12. Why does AI have a large (disruptive) impact on every industry?
  13. In your own words, describe the basic principle of an algorithm.
  14. Describe the most important difference between supervised and unsupervised learning.
  15. Describe the difference between probability and accuracy of a machine learning model.
  16. Provide at least three examples of different algorithms and describe, broadly, how each type of algorithm works.
  17. Is clustering based on proximity a form of supervised or unsupervised learning?
  18. Describe the 15 steps of the AI process and indicate where it deviates from the regular BI process.

Chapter 5 questions

  1. When and why should organizations strive for greater agility?
  2. The principle of loose coupling, high cohesion concerns the dependency between effectors. What can the organization achieve using this principle?
  3. Who and what are the starting point of agility?
  4. Based on the Netflix case study, describe how Big Data and business model innovation are inextricably connected.
  5. What is the generic function of an organization?
  6. The use of AI can lead to greater knowledge, but the AI can also keep this knowledge to itself. Explain how this principle works.
  7. What are the 7 most important characteristics of a pivoted organization? Per characteristic, describe how it can be achieved and what the crucial factors in achieving it are.
  8. Which technologies support the process of pivoting the organization?
  9. Who and what ensure that organizations need to pivot?
  10. What are the most important characteristics of the social infrastructure of the intelligent, data-driven organization?
  11. What is data literacy? What factors can help you determine whether or not someone is data literate?
  12. Why is it important to focus on aspects of behavior and culture in data science projects?
  13. How can artificial intelligence help solve the delegation dilemma?
  14. What is the importance of feedback to an intelligent, data-driven organization?
  15. Name some factors that can impede change processes and what data science can do to cancel them out.
  16. Describe the scrum process and the most important differences compared to the traditional approach.
  17. Every employee in every organization should understand what machine learning and AI are. Why is this?

Chapter 6 questions

Architecture

  1. Why is working with an architecture a necessity when creating an intelligent, data-driven organization?
  2. Name the six main components of the ideal architecture of the intelligent organization.
  3. What is an enterprise data hub?
  4. Describe at least five principles that can dictate the architecture.
  5. What is the most important advantage of following the principles?

Data warehouse and ETL

  1. Why is it okay that some data in the data warehouse is stored multiple times?
  2. How can a data warehouse clear the way for pivoting the organization?
  3. Name three alternatives to a data warehouse.
  4. What is Changed Data Capture (CDC) and what is the advantage of this?
  5. What do the letters ETL stand for?
  6. Describe some benefits of a star schema.
  7. Which aspect of the large BI cycle when checking the data quality in the data warehouse?
  8. What is the advantage of a data warehouse bus?
  9. When does the organization also need data marts and cubes in addition to a data warehouse?
  10. Why should both the Central Data Warehouse (CDW) and the data marts use the same dimensions?
  11. Describe the difference between anonymizing and pseudonymizing data.
  12. Why is a Data Vault (without a Central Data Warehouse) hard to couple with one version of the truth?

Portals & Mobile BI

  1. Describe the two main components of a portal.
  2. A portal supports every process of the BI cycle. What technologies play an important role in this?
  3. For what type of user is mobile BI ideal?

Metadata

  1. Metadata is information about data. What role does it play in the development of a data warehouse?
  2. What are the most important functions of a metadata repository?
  3. Name the five ambition levels of metadata and indicate how they relate to the large BI cycle.

Data lakes

  1. What data does an intelligent organization store in a data lake and why?
  2. Describe the most important disadvantages of a data lake.

Chapter 7 questions

  1. What instruments play an important role in discovering issues and relationships in the processes and the business? These instruments also play a role in a different phase of the decision-making process. Which one?
  2. What instrument is best suited to discovering the cause of an issue?
  3. What programming languages are often found under the hood of data science instruments (reports, dashboards, algorithms)?
  4. What is the first step that should be taken when the organization starts using machine learning?
  5. What is the purpose of visualizations like graphs or metaphors? And what is the danger of using them?
  6. What is data storytelling and how could this principle contribute to better decisions?
  7. Why should the data-pixel ratio of data visualizations be kept as low as possible?
  8. Position the various data science applications on Treacy and Wiersema’s triangle and indicate why the information needs and data science applications depend on this.
  9. Come up with an example of a big data analytics application in your organization and describe the three most important challenges.
  10. Why can performance management never work efficiently without data science? Name at least two reasons.
  11. What is the most important goal of Competitive Intelligence (CI) and why is it so important?
  12. Name at least three benefits of Customer Analytics applications.
  13. When do you need Business Activity Monitoring applications? And which components within the data warehouse architecture should the organization employ?
  14. What is the goal of process mining, and which three basic forms are there?
  15. Describe the most important benefit of data science for supply chain applications.

Chapter 8 questions

  1. List at least four points that distinguish data science projects from traditional system development projects.
  2. What is the specific danger of data science projects that don’t start from the business?
  3. Which activities are performed during the blueprint phase?
  4. Why and for whom is creating awareness important? What instruments can you use in order to create more awareness of data science in your organization?
  5. How large, as a percentage, should the initial scope of the first data science project be in order to successfully complete it? And why?
  6. Why is it important to perform a data quality audit?
  7. Name at least four aspects that are important to research when evaluating data science software.
  8. Testing a data science system can be a lengthy and exciting process. Why is that?
  9. Why does it not make a lot of sense to spend a lot of time contemplating the embedding and changes to the organization during the planning phase?
  10. Name at least four obstacles and risks of data science projects divided across the three categories.
  11. The critical success factors of data science are mainly to do with (choose one answer):
    A. Technology
    B. Behavior and culture
    C. Correctness of information
    D. Having good KPIs
  12. Why is the project manager considered a bridge builder?
  13. Why does the project need a sponsor on the executive level?
  14. Who typically makes the (more complex) reports and analyses?
  15. List the three most important competencies of a data scientist.
  16. What role does the privacy officer play in a data science project?

Chapter 9 questions

  1. What do we mean by map and compass and how do they broadly relate to each other?
  2. Provide at least one example that proves that the various dimensions of the maturity levels can’t be too far out of lockstep.
  3. How can the organization’s environment be characterized for the maturity level “innovating”?
  4. Why is mobile BI on the map on the level of “improving”?
  5. Name the two primary focus areas of data science governance.
  6. Why does data science governance become more important as the organization climbs the maturity ladder?
  7. Provide an example of an important effect of “responding” to the “registering” step of the large BI cycle.
  8. Name at least four tasks and responsibilities of the BI Competency Center.
  9. Why is the position of the BI Competency Center in the organization so important?
  10. Name and describe, in your own words, the six steps to take to arrive at a comprehensive data science roadmap.
  11. Name at least 12 products and services (of the 32) that a data science team can deliver.
  12. Describe two ethical principles that should be applied when it comes to algorithms.

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