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This is a short solo episode to set the context for this project, my motivations and what I hope to achieve. The primary goal of this podcast is to invite experts in each business function and discover how they feel in their role and what they wish they could change. The core assumption of this experiment is that the more we are aware of how it feels to be in the other person shoes, the quicker we can build a shared context and work more effectively. This show would not be possible without your support, so I'd like to thank you for listening. If you find this useful let me know by adding this podcast to your feed.
I've been in academia for a long time, working with information and data, first as a data scientist then as a data engineer, and finally, analytics engineer in a couple of startups in Sydney.Along the way, I learned a lot about business, technology and people. And gradually I started to realize the importance of a solid data architecture. The primary force in this journey has probably been the full-on frustration and desire to improve things and spare my future self the pain I felt dealing with unreliable data and unrealistic expectations.
I gradually moved from writing lots of code, maintaining servers and altering machine learning pipelines, to thinking more about data architecture, data management and data ops. Eventually this led me to start my first company in Australia, Data Foundations, which I created to help organizations struggling with fragmented, unreliable and poorly governed data.
My biggest lesson so far has been that in the business context, engineering and science without design are really useless. Since data is the common denominator of every business function, designing for analytics really means to build systems that add value to a range of stakeholders with different skillsets, languages, and ways of thinking.
The primary goal of The Data Project is to invite experts in each business function and discover how they feel in their role and what they wish they could change. The core assumption of this experiment, if you want to call it, is that the more we are aware of how it feels to be in the other person's shoes, the quicker we can build a shared context and work more productively.
This show would not be possible without your support. So, I'd like to thank you for listening. If you find this useful, let me know by subscribing to the feed, which is really a way to say, “Goodidea. Keep doing it.” I hope you enjoy the first episode. Let's dive in.
I'm from Rome, Italy. I lived there for 25years. Actually, there is a bit of a debate with people from Rome; they say,“I'm not actually from Rome, I'm from Tivoli.” And Tivoli is fairly well-known in the world. It's 45 minutes from the center when you're lucky, it’s two and a half hours, when you're not. Rome is chaotic, beautiful, gorgeous.
I am in Sydney now. I'll tell you in a little bit about the story later, but before we start, there's a few facts that you should know about me. First of all, I swear I had amazing hair and I'm not even kidding. There are some pictures. Maybe not, maybe I should post a few pictures, but you wouldn't believe me so I can try to describe the situation to you.
So, use your imagination. I was literally four or five years old. I had basically almost white hair. It wasn’t blonde, but of a shade so clear and bright that people called me a light bulb. I'm not making this up. It's true. I had amazing hair for a while and now I don't anymore. And that's just how life goes. Since this show is about data and not about my hair, let's move on.
Second fun fact about me is that I have always, always loved eggplants. It's unreal how much I like them. I think it's just part of my bloodstream. I don’t know. Fact number three is that I love to take things apart. I think I've always been a bit of geek, a bit of an engineer. You give me a screwdriver and a box that has parts that move and I can just forget about time for a week.
I also get passionate very easily. I found that everything that is around me that I don't know is just waiting for me to figure out how it works. So, I am one of those people that find it extremely challenging to stay still. I'll tell you a story about how I challenged that.
A year ago, I decided to go on a silent retreat and went for 10 days in the mountains, eating twice a day, waking up a 4:30 AM and meditating without talking to anybody until 9:30 PM. It was crazy.That was one of the best things I've ever done in my life. It was just a personal challenge, but I'm really glad I did it because it taught me things that I could have never imagined.
I studied engineering in Rome. I wanted to do aeronautics, but I ended up in telecommunications, which then later got rebranded to information engineering.I've studied this for five years and then I moved to Germany. And that was whatI call my 2011 reality check. I was fresh from uni.
I kind of was fed up with Europe as a whole and wanted to explore something else and decided to move from Europe. And sometimes people ask me, “how did you end up in Sydney?” I didn't have anybody, any relative, any friend in Australia. What I did know though, is that my girlfriend at the time, now wife, said a couple of things to me. Like she said, "we can go anywhere, but you need to make sure there's good weather, good food, and it's an English-speaking country.” That was my query. I just had to select all where the sun shines, where food is tasty and where English is their first language. And there's not many places. I mean, US could have been an option but Australia won and here we are in Sydney.
After the summer, I found a gig in machine learning at the University of Sydney. I was in the research to apply reinforcement learning, which is a particular type of machine learning algorithm to really hard problems that cannot be solved by brute force. I published a couple of papers. That's also where I met my then advisor, the person that was my PhD advisor for four years.
Still at Sydney uni, but I moved from engineering to physics. I looked at quantum physics, so I was playing with lasers and extremely thin materials. We looked at quantum computing and we asked the question, “can we generate the inputs for this quantum computer in a way that is easy to integrate in a microchip?” So, it was microscopes, it was free space, lots of mirrors and powerful lasers, super cool stuff. Then I dived into data science and I worked as a data scientist, as a data engineer, as data architect – a bit of everything.
But after that experience, I started wondering what was wrong with data science. Seriously? Like, I joined as a data scientist and 90% of the job that I was doing had nothing to do with modelling or statistics. The field is moving really quickly. And so, it's only natural that it takes time for that information to diffuse and propagate. And so, everybody can reach the same understanding of the requirements and how you should think about data products.
I started thinking as an engineer and then I realized that architecture is really what it is important. It was a fantastic role because the company had been already doing data for three years. They had an existing all-in-one kind of a platform to do everything from reporting and transformations, but there was just an intrinsic limitation. Like how big a query can be or how many, how fresh the data was. I was hired to redesign the thing from scratch. I was placed as part of the engineering team, which is what makes this whole thing very interesting. I started thinking as an engineer, as I am an engineer after all, and while I was doing my job, I realized there were so many data users that are not engineers, people that have no clue what the difference is between say, structured and unstructured data, and they possibly don't care. They just want the answer.
And oftentimes that answer leads to more questions that need more answers at a rate and a speed that it can be overwhelming. So, when I think one of the key lessons, again we'll have dedicated episodes about this, that I learned is that design matters and the best designs are those that involve multiple people with different backgrounds that all collaborate towards the same goal.
But bringing people together either virtually or physically is not the only answer. We also need to make sure that there is a shared understanding and a shared language so that everyone can, up to a point, anticipate the challenges of the other people in the team and take those into account when they form the expectations around how long things take, what's possible, what isn't.
This project starts from the assumption that if we are more aware of what it feels like to be in the shoes of the other person, we can do a faster and better design, and ultimately generate insights and data products that are actually useful. And look, my journey is in no way exceptional. I just happened to have started from data science and walked backwards to focus more on analytics, going through an engineering role and an architectural role.
If there's one thing I'm sure of today is that my own journey has been sometimes painful. I wish I knew these things back then. And so, in conclusion I see this project literally as a gift. And what I'm hoping to do is bring all of those perspectives together, and help all of us achieve a higher state of productivity and get less frustrated and actually add value to the businesses we work for. So, I will do my best as a host to invite people that have relevant experience and are willing to share what they actually learned, not just the technical details, but how it felt to do what they did.
But really this is an experiment and the knobs can be turned left to right depending on whether you find this useful or not. If you are listening to this episode right now, I want to thank you for first of all, for giving a shot to this podcast. There are millions of podcasts that you could follow right now yet you decided to listen to this. So, thank you for that.
And the second is be aware that you, as a listener, have an immense power on projects like this. This is a side project for me. I'm running completely out of my own free time. There are no ads on the show yet. And I hope to keep it that way. The only thing that counts at the moment, the only metric for me to gauge whether this project is worthwhile or not, is whether you follow it.
So, if the stuff I talked about resonates with you even slightly, do consider to take the time and press the button to subscribe. That's good feedback that goes straight in into my own analytics dashboard, and it will give me a signal that it's worthwhile having this conversation and that I should continue doing it.
The next episode, the really first interview is going to be with Stephen Pollock. Steven is a data scientist and we'll talk about his perspective on what the data science process is and what are we doing right as well as what we are doing wrong. So, I'm really looking forward to that.
In the meantime, stay safe and subscribe if you think it’s worth it.