The pattern of writing a data book with Shane Gibson and Ramona C Truta

AgileData Podcast #64

Join Ramona C Truta as she interviews Shane Gibson about the patterns of writing a data book (and they discuss a whole lot of other data things)

Listen

Listen on all good podcast hosts or over at:

https://podcast.agiledata.io/e/the-pattern-of-writing-a-data-book-with-shane-gibson-and-ramona-c-truta-episode-64/

Read

Read or download the podcast transcript at:

https://agiledata.io/podcast/agiledata-podcast/the-pattern-of-writing-a-data-book-with-shane-gibson-and-ramona-c-truta/#read

Google NoteBookLM Briefing

Briefing Document: "Scratching the Book Itch: A Patterned Journey" - AgileData Podcast

Source: Excerpts from "Scratching the Book Itch: A Patterned Journey" - AgileData Podcast featuring Shane Gibson and Ramona C Truta.

Subject: Discussion on writing a book, the concept of "patterns," the role of data professionals, and the future of learning and data in the age of AI.

Key Participants:

• Shane Gibson: Podcast Host (acting as guest in this episode), Author of "An Agile Data Guide to Information Product Canvas." Agile Data Coach.

• Ramona C Truta: Podcast Co-Host (acting as host in this episode). Data professional with a strong background in mathematics, computer science, databases, academia, and research. Describes herself as a "facilitator of knowledge."

Executive Summary:

This podcast episode features a conversation between Shane Gibson and Ramona C Truta, where Shane discusses his journey writing his book on data patterns. The discussion delves into the concept of "patterns" as common solutions to common problems, drawing parallels to architectural design. Shane shares the challenges and lessons learned during his writing process, including overcoming anti-patterns like rigid outlines and lack of consistency. A central theme is the evolving role of data professionals as "translators" and "storytellers" in the age of data and AI, moving beyond purely technical expertise. The conversation also explores the unexpected pattern of data teams replacing manual Excel processes and questions the traditional justification for data team value. Finally, the hosts ponder the implications of AI on learning, critical thinking, and the future of education, advocating for a focus on skills and storytelling over rigid roles and knowledge acquisition.

Main Themes and Important Ideas/Facts:

1. The Concept of Patterns:

• Drawing inspiration from Christopher Alexander's "A Pattern Language" (about building houses), a pattern is defined as a "common problem" with a "common solution that fixes that problem most of the time."

• Patterns provide "frameworks and patterns that I can anchor to" to explain complex concepts simply.

• The idea of "antipas" is introduced – solutions that create more waste than they solve.

• Ramona initially found Shane's focus on patterns perplexing but came to understand them as a way of structuring knowledge.

• The authors believe that experience (accumulating "massive data sets" over "several decades") allows for the building of "knowledge graphs" and applying "system thinking" to identify and apply patterns.

1. The Challenges and Process of Writing a Book:

• Shane describes writing a book as "scratching a itch," something he felt compelled to do.

• It is "incredibly hard" and "lots of people start and never finish."

• Shane learned that "you don't make money out of a book."

• Initial attempts failed due to anti-patterns:

• Rigid outlines/table of contents: Shane kept "rewriting the table of contents" instead of writing content.

• Inconsistent writing: Writing only "when I felt like it" was not effective.

• Shane developed a successful process involving "forcing functions," specifically making his word count public on LinkedIn and aiming for "60,000 words in six months."

• This process forced him to "research" and led to better content by "enhancing your understanding of what you know."

• He found that "collaborating on a book is actually harder than writing it by yourself," due to differing styles and visions.

• Shane realised the importance of "what am I not gonna write," applying a "constraints based model" to keep the book focused.

• Running a related course helped refine content and identify areas needing clarification based on student questions. Shane's future pattern for writing is to "write the course first. Then write the book."

• Writing the book also revealed "new problems to solve," such as learning about publishing details (e.g., "Adobe InDesign bleeds and Amazon Kindle Direct Publishing bleed formats").

1. The Evolving Role of Data Professionals:

• Ramona describes her role as a "facilitator of knowledge," translating "the tech talk... into business talk and vice versa." This involves removing "the fluff... and the foam, and I speak caffeine."

• Shane agrees that being a "translator" is a valuable role, especially with the advent of AI.

• Both hosts highlight the need for "system thinking" and applying accumulated knowledge and frameworks.

• The ability to "take these technical things we do and explain them by just telling stories" is crucial. Shane references the analogy of using a decision tree to explain a complex neural net model.

• The authors reflect on how people often use jargon to describe the same concept, hindering understanding.

• The hosts believe that "curators of that knowledge, humans that actually tell a story. Based on that knowledge in a different way will actually become the more successful people." This returns to the ancient pattern of "storytellers being the most valuable part of the tribe."

1. Unexpected Patterns in Data Value and Teams:

• An unexpected pattern emerged from the collaborators on Shane's book: the "unlike" scenario (what would happen if the information product wasn't built) consistently resulted in "manual process using Excel."

• This led Shane to question if data teams are "overthinking it" in justifying their value, as they are often simply replacing inefficient manual processes.

• Shane provocatively asks if data teams are essentially a "cost center" and questions the traditional model of data teams as a centralised "shared service" that has to "go and justify our existence."

• He draws a parallel to finance teams, which are also a shared service but are mandated and don't need to justify their existence.

• He considers whether the future might involve decentralising data professionals, embedding them with operational teams.

1. AI, Learning, and the Future of Education:

• The advent of AI provides "access to a knowledge base. In a way we've never had before."

• Ramona is enthusiastic about "learning how to learn" with AI tools, using chatbots to learn and test her knowledge.

• Shane questions the assumption that AI requires more structured data, hypothesising that "we will structure our data less" and let LLMs find patterns in chaos, similar to k-means clustering versus complex SQL queries.

• They discuss the "non-deterministic nature" of AI and the paradox that "we don't trust it" yet trust humans who are arguably more non-deterministic.

• The hosts express concern about how future generations will develop critical thinking if they rely solely on AI for answers.

• They believe that traditional educational institutions, focused on "giving you some knowledge and testing whether you got the knowledge," are using "legacy education sessions."

• The future of education should focus on teaching "skills, not roles," allowing individuals to combine skills ("storytelling skills and some science system thinking skills") to fill various roles and solve complex problems.

• The discussion questions the need for traditional grading if the focus shifts to problem-solving and storytelling.

Key Quotes:

• Shane: "Today I'm gonna be the guest and you are gonna be the host."

• Ramona: "I have started in data since ever I joke that I came on this earth doing data..."

• Shane: "...that idea of being a translator, I think we see. People fall into that role over time... because we've spent years observing and learning and training our brains to recognize patterns..."

• Ramona: "...what I am now is a facilitator of knowledge... I take the tech talk... and translate it into business talk and vice versa."

• Shane: "...the foam on the top of the cappuccino... I'm the person that goes down to the caffeine."

• Shane: "...a patent really is there's a common problem. There's a common solution that fixes that problem most of the time..."

• Shane: "...I used to get really frustrated that whenever we went into a new customer, we seem to be reinventing the wheel."

• Shane: "...I decided that what I needed to do was a forcing function that would make me write consistently..."

• Shane: "...it forced me to research... I could see the patterns that were anchoring me..."

• Ramona: "...even in the first iteration... you are trying to figure out a pattern in the knowledge... It's meta the way I see it..."

• Shane: "...collaborating on a book is actually harder than writing it by yourself?"

• Shane: "...it's the book I wanted to write and like I said, I wrote it to scratch a itch."

• Ramona: "...that is what makes. Sense to me, but I understand, and I know there is order and structure in chaos. You just have to find it."

• Shane: "...because we know the machine is not deterministic... We don't trust it... However, when we ask a human... We trust that they know what to do... So there's this economy between not trusting a machine but trusting a human."

• Shane: "...the technical tools that a data scientist use is Excel and Tableau, like bollocks..."

• Ramona: "...I became so passionate... realizing how people just touch the surface of things and don't know what's underneath..."

• Shane: "...writing a book for people that were inquisitive... It tells you what you need to know, but actually you still have to go and do more work."

• Shane: "...what am I not gonna write? That was more important."

• Shane: "...the answer was manual process using Excel."

• Shane: "...maybe we're overthinking it because really the alternative is do you wanna run that process in Excel or do you not? Because that's actually the thing we're replacing nine times outta 10."

• Shane: "...data teams are like a finance team. Without the mandate..."

• Shane: "...the questions that I get asked is the content the book should focus on."

• Shane: "...maybe we're looking at it from different angles... Structuring or cleaning the data. Those are D different aspects of it."

• Shane: "...maybe the answer is actually we do get embedded with the operational teams. We no longer become a centralized shared service. We become decentralized."

• Shane: "I have a hypothesis... that actually will structure our data less..."

• Shane: "...curators of that knowledge, humans that actually tell a story. Based on that knowledge in a different way will actually become the more successful people..."

• Shane: "...we are trying to apply legacy patterns that haven't worked for us anyway into this new world."

• Shane: "...the education system has to teach skills, not roles."

• Shane: "If you're gonna go and write a book, do it for yourself."

Conclusion:

This podcast provides a rich and insightful discussion on the multifaceted journey of writing a book, particularly in the context of data and technology. It highlights the personal and professional growth that comes with the process, the importance of identifying and applying patterns (both positive and negative), and the evolving landscape of the data profession in the age of AI. The conversation provocatively questions established norms around data team value and the future of education, advocating for adaptability, storytelling, and a focus on core skills. The discussion is marked by the candidness of the hosts and their willingness to share both successes and failures.

«oo»

Stakeholder - “Thats not what I wanted!”
Data Team - “But thats what you asked for!”

Struggling to gather data requirements and constantly hearing the conversation above?

Want to learn how to capture data and information requirements in a repeatable way so stakeholders love them and data teams can build from them, by using the Information Product Canvas.

Have I got the book for you!

Start your journey to a new Agile Data Way of Working.

«oo»