Intro: Can we focus on the data at hand for a moment?

Part I: What if we have been going about data the wrong way?

Chap 1: What do you need from the data?

1.1 What does this mean, exactly?

1.2 What are your main pain points?

1.3 What are your objectives?

1.4 How does data fit into your overall strategy?

1.5 What does a successful data product look like to you?

1.6 Would any of this be possible without data analytics?

1.7 Key takeaways

Chap 2: What data do you have?

2.1 What does data entail, exactly?

2.2 What are the different kinds of data?

2.3 Where can you look for the data you have?

2.4 How much can be harnessed in a data project?

2.5 How does time come into play?

2.6 What potential value exists in your data?

2.7 Key takeaways

Chap 3: What kind of skillsets are available?

3.1 What does this question mean, exactly?

3.2 What are the skillsets that come into play?

3.3 What kind of people have such skillsets?

3.4 Can you train or educate your existing team members?

3.5 How does mastery of certain skills come into play?

3.6 What is the value of the right skillsets?

3.7 Key takeaways

Chap 4: What can you learn about what was or what is right now?

4.1 What picture can data paint for you?

4.2 How and when is descriptive analytics useful?

4.3 What specific methods and processes can you leverage?

4.4 What are the methods and processes limitations?

4.5 What are some potential pitfalls in this methodology?

4.6 Who can you leverage for this kind of work?

4.7 Key takeaways

Chap 5: What can you learn about what is bound to be?

5.1 Is it possible to predict the future using data?

5.2 How and when is predictive analytics useful?

5.3 What specific methods and processes can you leverage?

5.4 What are the methods and processes limitations?

5.5 What risks are there in predictive analytics?

5.6 Who can you leverage for this kind of work?

5.7 Key takeaways

Part II: What about the bigger picture?

Chap 6: What will you need from data in the foreseeable future?

6.1 Are you asking the right questions about your projects?

6.2 What are the questions to ask beyond the data at hand?

6.3 What about automation?

6.4 What is the value of developing a data culture?

6.5 What are the different levels of data literacy?

6.6 Can you be data-driven in your decisions?

6.7 Key takeaways

Chap 7: What other data can you acquire?

7.1 Why do you need to get additional data?

7.2 Who could you ask about this?

7.3 What are the limitations of the data at hand?

7.4 What data streams should you prioritize?

7.5 Can you really trust data that you don’t own?

7.6 What about privacy considerations?

7.7 Key takeaways

Chap 8: What technologies and professionals will you need?

8.1 Will you be able to handle the data you need?

8.2 What technologies are available?

8.3 What about the people operating these technologies?

8.4 What about the rapid rate of change in tech?

8.5 Can AI help in all this?

8.6 Can you make your data strategy future-proof?

8.7 Key takeaways

Chap 9: What will this cost?

9.1 What are the financial aspects of governing data?

9.2 What are the costs of new data streams?

9.3 Is freely available data a real thing?

9.4 What are the kinds of costs involved in a data project?

9.5 What about the human costs when things go awry?

9.6 Are there any hidden costs you need to be aware of?

9.7 Key takeaways

Chap 10: What are the risks and opportunities at play?

10.1 What are the most common risks and opportunities?

10.2 How can you model all this in practice?

10.3 What is the biggest risk in data work?

10.4 What is the biggest opportunity in data projects?

10.5 How can you decide all this without regrets?

10.6 How can you SWOT-analyze all this?

10.7 Key takeaways

Part III: What about AI?

Chap 11: What does AI have to do with all this? (And what it doesn’t.)

11.1 What is AI anyway?

11.2 Why is AI often relevant in a data project?

11.3 What are the main pitfalls and limitations of AI?

11.4 How do GenAI, Automation, and DL factor in?

11.5 To what capacity can AI be leveraged?

11.6 What about Artificial General Intelligence (AGI)?

11.7 Key takeaways

Chap 12: When would you want to get AI involved in a data project?

12.1 Isn’t it a no-brainer?

12.2 What kind of AI suits your project best?

12.3 How would you start?

12.4 What KPIs would you use?

12.5 What steps can you take to make AI a better value-add?

12.6 When should you give up on an AI project?

12.7 Key takeaways

Chap 13: Where can you find AI pros who add the most value?

13.1 What qualifications does an AI professional need?

13.2 How can you discern between AI pros and wannabes?

13.3 At what point do programming skills come into play?

13.4 What’s the difference between AI users and AI experts?

13.5 How can you see beyond the hype of AI?

13.6 Can you leverage AI professionals for other data work?

13.7 Key takeaways

Chap 14: What kind of information can AI handle for you?

14.1 What are the kinds of information in a data project?

14.2 What’s the deal with prompts and prompt engineering?

14.3 What are the AI limitations in handling data, information, and knowledge?

14.4 Can you overcome AI’s limitations? If so, how?

14.5 Diamonds are forever, but can we say the same for an AI system?

14.6 How can you customize an AI system for your knowledge base?

14.7 Key takeaways

Chap 15: What kind of investment does AI entail? Is it all worth it?

15.1 How does AI add value to you?

15.2 What kind of AI will you use?

15.3 How would you go about it to minimize risk?

15.4 What kind of results are you expecting?

15.5 What are other considerations when investing in AI?

15.6 What’s the bottom line when it comes to AI?

15.7 Key takeaways

Epilogue: What now?

Appendix A: Classic questions to ask yourself regarding data

Appendix B: Questions flowchart