Question-driven Data Projects

A business book from Zacharias Voulgaris, PhD, via Technics Publications

Overview

Become adept at asking good questions about the data and obtaining answers to add significant business value to analytics and artificial intelligence (AI), without any hype or jargon.We start by exploring fundamental topics, such as how data relates to your pain points, where to find the data, the skillsets involved in data work, and the two main data methodologies.Next, we cover high-level matters regarding data, expanding the scope of data initiatives and elaborating on the bigger picture of data work. Learn how to enrich your data assets, what professionals and technologies you’ll need to leverage, how data needs to evolve over time, the cost of data projects, and a process for evaluating when a data initiative is worth the investment.We conclude with a series of AI topics, from when AI is relevant in a data project to where you can find worthy AI professionals.

Main sections:
* What if we have been going about data the wrong way? (exploring the data at hand starting from where we need to go through data work)
* What about the bigger picture? (the most strategic aspects of data work and how they relate to the business)
* What about AI? (what it is and how it can be useful, beyond the hype around it)

Author

Dr. Zacharias Voulgaris is a data scientist and analytics consultant with a background in engineering and management. After his PhD in Machine Learning, he worked as a researcher in one of the top technical universities, Georgia Tech. Later, he shifted to the private sector, eventually getting recruited by Microsoft as a program manager.Throughout his career he has worked in various data-driven startups, developing multiple kinds of data products. Also, he has authored various books on data science and A.I. and coded a series of programs in these fields, and on cryptography. He has a solid understanding of machine learning, data engineering, and recommender systems, among other topics in the data science domain.A self-sufficient professional, he is also a good team player, particularly keen on mentoring, and enthusiastic about expanding his knowledge and know-how. These days, he is a mentor at GrowthMentor & MentorCruise and a consultant at GLG.

Zacharias has authored eight (8) non-fiction books, mostly technical, on various data-related topics.

Sample

Questions & Answers on the subject

FAQ
1. Why are questions important for data projects?
Because they enable a deeper understanding of data work and foster a better relationship with the corresponding processes. You can learn more about this topic at the introduction section of the book.
2. Who can benefit from question-driven data projects?
Anyone involved in data work, particularly managers of data projects and stakeholders of data initiatives. This topic is explored in various sections of the book.
3. What does a successful data project entail?
Good planning, robust team, ample data, and the use of the right tools, for starters. You can learn more about this topic at section 1.5 of the book, as well as a few other parts.
4. What's the process of going ino depth on a data-related topic?
Good questions, some research, various iterations, etc. Check out appendix B of the book for more information on this topic.
5. How does AI come into play in this sort of projects?
In various ways, from predictive analytics, to various generative AI-related processes, to chatbots. Chapter 11 of the book explores this topic in more depth.
6. What kind of people are involved in a data project?
PM person, Data Analyst / Business Intelligence pro, Data Scientist, Data Engineer, AI expert, Data Visualization pro, etc. More details about these roles and how they relate to each other are available in Chapter 3.
7. Where can I start for driving value in my organization through data projects?
Proof-of-concept projects tackling the most effective (e.g., in terms of ROI) business problems or objectives. You can learn more about all this in Chapter 4 of the book.
8. Who is this book for?
Anyone involved in data work, particularly managers of data projects and stakeholders of data initiatives. The introduction chapter explore this matter in more detail.
9. What constitutes a value-add in a data project?
Various things, such as tackling a particular business objective, for example. Sections 15.1 and 6.4 of the book are the best place to zero in on this topic.
10. Where (which industries) are data projects most applicable?
Various. Anywhere where data is available and there are data-driven initiatives in place. Check out Chapter 5 and 2 for more details on this topic.
11. How can data help achieve our key business objectives?
Through cost-effective projects related to analytics, AI systems, etc. This topic is covered in more detail in various parts of the book.
12. What additional types of data could enhance our insights stemming from data work?
Data streams related to the market, your competitors, the general sentiment around your products, etc. Chapter 7 is a great resource for this topic.
13. How do we ensure the quality and integrity of our data?
Through careful curating of the datasets and validation, for starters. Chapter 2 is a good place to start for learning more about this topic.
14. Who are the stakeholders involved in our data initiatives? How can we facilitate collaboration among them?
The various kinds of data workers, the investors, business partners, etc. Through better data literacy across the org. You can learn more about this topic in Chapters 6 and 5 of the book.
15. What tools and technologies do we need to analyze our data effectively?
Various, depending on the projects. A good stack comprising of efficient programming languages, database management tools, and specialized software for AI-related processes, are a good starting point. Chapter 8 is a great resource for going into more depth on this topic.
16. How do we measure the success of our data initiatives?
ROI, customer satisfaction, and cost savings, among others. Chapters 12 and 4 of the book delve more into this topic.
17. What ethical considerations must we take into account when handling customer data?
Keeping the data anonymized and secure are a good start. In Chapter 6 of the book we go more in depth on this topic.
18. How can we leverage predictive analytics to anticipate future trends?
Through the use of historical data for time-series analysis, various customer data for classification & regression, etc. You can zero in more about this topic in Chapter 5 of the book.
19. In what ways can we visualize our findings for better stakeholder engagement?
Having a data visualization expert in the team is a good starting point. You can find out more about this topic in Chapters 4 and 3 of the book.
20. How often should we revisit our data strategy to adapt to changing market conditions?
It depends on the projects at hand. Keeping in mind evolving technologies is a good aid in this. You can learn more about this topic in Chapters 8 and 6 of the book.
21. What kind of questions are good to have as a reference, when embarking on a data initiative?
Things like "What are the potential biases in the data?", "What are the low-hanging fruits when it comes to data projects?", and "What are the ethical considerations?" among many others. Appendix A has a whole list of such questions, in an easy-to-read format.
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