Hey Hackers! I’m Ryan Dawson, and I’m Principal Data Consultant at Thoughtworks.
First of all, a huge thank you to the HackerNoon community and staff for nominating in the 2021 Noonies awards! I’ve been nominated in the following categories - please do check out these award pages and vote:
As someone that works a lot with data, I’m currently excited about Data Mesh. It’s an approach to structuring and using data within an enterprise that contrasts with the Data Warehouse or Data Lake model. The Lake and Warehouse centralized models with a single team of gatekeepers is creating bottlenecks when many organizations want to be moving quickly to get value from the data. Data Mesh is a bit like microservices for data. Learn more about my thoughts and opinions on getting value from data and my journey in the tech industry via the interview below.
My role at ThoughtWorks is Principal Data Consultant, which is pretty broad. I work with clients to help them get the most value out of data. This can mean organizing the way data is captured and shared, selecting the right data technologies for working with data, and advising on optimal team structures. It also includes some AI work as AI is key to the value that many organizations are looking to get from data. Taking machine learning to production is one of my favorite topics.
To me, the big challenge in tech right now is for organizations to become more data-driven. Certain very famous companies have shown how impressive software products can be when they leverage data in intelligent ways. The likes of Amazon, Google, Netflix, and Spotify and their search and recommendation features especially have had a big impact on people’s lives. Lots of other companies see that data has a lot of potential but don’t yet see how to make full use of it.
My day-to-day is working with customers on whatever their problems might be. That might be about putting together a strategy document or refining a plan, or advising on a choice of technology or some team structures. Or sometimes, it can mean getting into the details of system code or system architecture. I also write about the challenges that I encounter, and I publish some of that on hackernoon.
I started as a software consultant, building software projects for big banks. Those were mostly Waterfall projects, and I learned about the shortcomings of the Waterfall delivery model first-hand. It got me very interested in Agile and in the big picture of how we approach software delivery.
Then I moved into open-source software at Alfresco. It was interesting to work at a company selling software products. You tend to get a different culture when the software is what makes the company money when compared with software developed for in-house use. Software product companies are more conscious that they have to nurture the software over time.
My next job was at another open-source software product company, Seldon. That was working on tools to get value out of machine learning - MLOps tools. MLOps is a new field - it's all about making the machine learning build-deploy-monitor lifecycle as smooth as possible.
This is surprisingly challenging, as machine learning uses data in very different ways from most software. You extract patterns from data and reapply those patterns to make predictions.
So, if the new data you're applying the patterns to is different, or it changes, then you can get really bad predictions.
I’m still involved in MLOps in my current role at Thoughtworks. Now I work on the more general problem of how to get value from data. One aspect of this is enabling AI, but there are also lots of challenges about how to structure and store data, how to ensure quality and keep the data fresh, and how to enable the various use cases that arise for the data.
I’m very excited about Data Mesh. It’s an approach to structuring and using data within an enterprise that contrasts with the Data Warehouse or Data Lake model. The Lake and Warehouse centralized models with a single team of gatekeepers are creating bottlenecks when many organizations want to be moving quickly to get value from the data. Data Mesh is a bit like microservices for data. There’s a lot of enthusiasm about Data Mesh right now, and Thoughtworks are leaders in developing the concept and bringing it to life. It’s an exciting thing to be part of.
I’m most worried about the Attention Economy and how much business relies on getting people to scroll or click on a screen. I love software, but some software design practices strike me as manipulative, and I am worried about a tendency to play to the subconscious. These days everyone seems to feel that they spend more time with their screens than they want to, and I suspect certain design practices are a big part of why that’s happening.
Emitwise, if they wanted the money. I'm an organizer of Tech Ethics London, and Mauro Cozzi told us all about Emitwise. They help companies better track and improve their overall carbon footprint. I’ve previously talked about how emphasizing ethics is a sound business investment, but I'm also pleased to see that there's more legislation coming to force companies to do this kind of reporting (at least in the UK).
I'm looking into the various data platforms out there and trying to work out how best to use them for Data Mesh. Most recently, I did the Azure Data Engineer Certification, and I'm looking to get deeper into more of the big data platforms.
Years ago I was doing some teaching and giving students feedback on their essays. There was one essay for which I told the student that it was a very good piece that they should be proud of but that to do best on the exam, they should be less interesting. Exams have a mark scheme, and there's a game you have to play to tick all the right boxes. I was pretty pleased to be able to give that advice as it was something I had to learn for myself when I was a student.
It was basically to focus on something and do it really well. At the time, I was writing a dissertation, and I was trying to do too many things. Narrowing the scope really helped me a lot. It became something much better after it became more focused, and I realized that I could always return to the other questions that I'd set aside later. It was specific advice at the time, but I think it generalizes. It's tempting to think that you have to do something big in order for it to be useful to others but often, what matters to others is actually the quality. Also, if you do something really well, then you gain the skills to do that thing well, and in the long run, those skills may be more valuable than whatever you're working on.
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