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
- What is the main goal of data science for organizations?
- What are the processes of the large and small BI cycles?
- What is the difference between contextual and transactional information?
- Explain why good decision-making often requires external information.
- Describe at least four (fundamental) developments in society and the economy that increase the importance of data science and explain them.
- Provide an example from everyday practice that proves that you can use data science for performance measurement and explain how it works.
- Describe in your own words why data science and change management are inextricably connected.
- Why is it difficult to calculate the returns of data science upfront?
- Describe how data science can accelerate and improve operational decision-making in organizations.
- How can algorithms help improve decisions? Provide an example.
Chapter 2 questions
- List the four maturity levels of data science and describe some of their characteristics. Describe how data science success relates to these maturity levels.
- 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?
- List the three most important characteristics from the definition of the intelligent, data-driven organization and how they relate to the human body.
- In what ways can an organism demonstrate adjusted behavior?
- Name the two most important effects of data science on organizational structures and explain them.
- What type of manager is most likely to resist data science initiatives and why?
- Describe, in your own words, the relationship between the PDCA cycle and data science.
- Explain why it’s important to start by improving the organization’s sensitivity.
- What, besides presenting information on the lower levels of the organization, is an important condition for flattening the organization?
- 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
- What approaches can organizations use to determine their information needs? List several characteristics of each approach.
- What are critical success factors and what role can they play when determining the information need?
- What’s the difference between the critical success factors from the strategy map and the organization’s strategies?
- Come up with at least two extra possibilities presented by a strategy map.
- Describe the difference between a PI and a KPI.
- Why are KPIs (Key Performance Indicators) never purely financial?
- Describe at least three rules of thumb for determining valuable insights that can reveal great improvement potential.
- Besides defining indicators, what should an organization do to create a complete management model?
- On which points should information be cohesive and why is this important?
- Why do some people think that creating one version of the truth is impossible?
- What should intelligent organizations keep in mind when setting targets for indicators?
- Why is it important to establish the relationships between indicators?
- Describe three characteristics of data-driven working and describe the advantages in your own words.
- Name the three types of machine learning models and indicate the most important difference.
- Define the four Vs of Big Data and provide an example of a Big Data application.
- What does it mean for an organization to pursue an AI-first strategy?
- What is an important (first) step towards implementing an AI-first strategy?
Chapter 4 questions
- 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).
- What can be seen as the most important goal of the first five steps of the small BI cycle?
- Describe the ‘interpret” step of the small BI cycle.
- Provide some examples of mistakes, biases, and preconceptions that can factor into the internalization process.
- Describe the processors of the intelligent, data-driven organization and their typical information use.
- Why is it important that the processors use the same system?
- What rule of thumb can be applied to optimizing the refresh rate of reports?
- Name the two most important characteristics of a Business Intelligence Framework.
- 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.
- Describe at least 3 tips for improving decision management in your organization.
- Provide a clear definition of Artificial Intelligence (AI).
- Why does AI have a large (disruptive) impact on every industry?
- In your own words, describe the basic principle of an algorithm.
- Describe the most important difference between supervised and unsupervised learning.
- Describe the difference between probability and accuracy of a machine learning model.
- Provide at least three examples of different algorithms and describe, broadly, how each type of algorithm works.
- Is clustering based on proximity a form of supervised or unsupervised learning?
- Describe the 15 steps of the AI process and indicate where it deviates from the regular BI process.
Chapter 5 questions
- When and why should organizations strive for greater agility?
- The principle of loose coupling, high cohesion concerns the dependency between effectors. What can the organization achieve using this principle?
- Who and what are the starting point of agility?
- Based on the Netflix case study, describe how Big Data and business model innovation are inextricably connected.
- What is the generic function of an organization?
- The use of AI can lead to greater knowledge, but the AI can also keep this knowledge to itself. Explain how this principle works.
- 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.
- Which technologies support the process of pivoting the organization?
- Who and what ensure that organizations need to pivot?
- What are the most important characteristics of the social infrastructure of the intelligent, data-driven organization?
- What is data literacy? What factors can help you determine whether or not someone is data literate?
- Why is it important to focus on aspects of behavior and culture in data science projects?
- How can artificial intelligence help solve the delegation dilemma?
- What is the importance of feedback to an intelligent, data-driven organization?
- Name some factors that can impede change processes and what data science can do to cancel them out.
- Describe the scrum process and the most important differences compared to the traditional approach.
- Every employee in every organization should understand what machine learning and AI are. Why is this?
Chapter 6 questions
- Why is working with an architecture a necessity when creating an intelligent, data-driven organization?
- Name the six main components of the ideal architecture of the intelligent organization.
- What is an enterprise data hub?
- Describe at least five principles that can dictate the architecture.
- What is the most important advantage of following the principles?
Data warehouse and ETL
- Why is it okay that some data in the data warehouse is stored multiple times?
- How can a data warehouse clear the way for pivoting the organization?
- Name three alternatives to a data warehouse.
- What is Changed Data Capture (CDC) and what is the advantage of this?
- What do the letters ETL stand for?
- Describe some benefits of a star schema.
- Which aspect of the large BI cycle when checking the data quality in the data warehouse?
- What is the advantage of a data warehouse bus?
- When does the organization also need data marts and cubes in addition to a data warehouse?
- Why should both the Central Data Warehouse (CDW) and the data marts use the same dimensions?
- Describe the difference between anonymizing and pseudonymizing data.
- Why is a Data Vault (without a Central Data Warehouse) hard to couple with one version of the truth?
Portals & Mobile BI
- Describe the two main components of a portal.
- A portal supports every process of the BI cycle. What technologies play an important role in this?
- For what type of user is mobile BI ideal?
- Metadata is information about data. What role does it play in the development of a data warehouse?
- What are the most important functions of a metadata repository?
- Name the five ambition levels of metadata and indicate how they relate to the large BI cycle.
- What data does an intelligent organization store in a data lake and why?
- Describe the most important disadvantages of a data lake.
Chapter 7 questions
- 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?
- What instrument is best suited to discovering the cause of an issue?
- What programming languages are often found under the hood of data science instruments (reports, dashboards, algorithms)?
- What is the first step that should be taken when the organization starts using machine learning?
- What is the purpose of visualizations like graphs or metaphors? And what is the danger of using them?
- What is data storytelling and how could this principle contribute to better decisions?
- Why should the data-pixel ratio of data visualizations be kept as low as possible?
- 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.
- Come up with an example of a big data analytics application in your organization and describe the three most important challenges.
- Why can performance management never work efficiently without data science? Name at least two reasons.
- What is the most important goal of Competitive Intelligence (CI) and why is it so important?
- Name at least three benefits of Customer Analytics applications.
- When do you need Business Activity Monitoring applications? And which components within the data warehouse architecture should the organization employ?
- What is the goal of process mining, and which three basic forms are there?
- Describe the most important benefit of data science for supply chain applications.
Chapter 8 questions
- List at least four points that distinguish data science projects from traditional system development projects.
- What is the specific danger of data science projects that don’t start from the business?
- Which activities are performed during the blueprint phase?
- 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?
- How large, as a percentage, should the initial scope of the first data science project be in order to successfully complete it? And why?
- Why is it important to perform a data quality audit?
- Name at least four aspects that are important to research when evaluating data science software.
- Testing a data science system can be a lengthy and exciting process. Why is that?
- 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?
- Name at least four obstacles and risks of data science projects divided across the three categories.
- The critical success factors of data science are mainly to do with (choose one answer):
B. Behavior and culture
C. Correctness of information
D. Having good KPIs
- Why is the project manager considered a bridge builder?
- Why does the project need a sponsor on the executive level?
- Who typically makes the (more complex) reports and analyses?
- List the three most important competencies of a data scientist.
- What role does the privacy officer play in a data science project?
Chapter 9 questions
- What do we mean by map and compass and how do they broadly relate to each other?
- Provide at least one example that proves that the various dimensions of the maturity levels can’t be too far out of lockstep.
- How can the organization’s environment be characterized for the maturity level “innovating”?
- Why is mobile BI on the map on the level of “improving”?
- Name the two primary focus areas of data science governance.
- Why does data science governance become more important as the organization climbs the maturity ladder?
- Provide an example of an important effect of “responding” to the “registering” step of the large BI cycle.
- Name at least four tasks and responsibilities of the BI Competency Center.
- Why is the position of the BI Competency Center in the organization so important?
- Name and describe, in your own words, the six steps to take to arrive at a comprehensive data science roadmap.
- Name at least 12 products and services (of the 32) that a data science team can deliver.
- Describe two ethical principles that should be applied when it comes to algorithms.
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