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SDO 004 - Designing Your Data Experience - Kai Hess
What are your thoughts on design?
I was first formally introduced to design during my time at Stanford, with a huge emphasis on “design thinking” throughout the university. Every student I talked to said I had to get into one of the “design thinking for X” courses, especially if I’m considering entrepreneurship— you even had to apply to get into the classes because they were so popular.
Unfortunately, my grad school schedule didn’t allow me to take a course, but I did take multiple workshops to learn the design thinking process at a high level. In summary, the design thinking process consists of 5 steps:
This process is actually how I approach many technical problems, with a major focus on empathizing. Even more interesting is how this theme of empathizing and defining problems is throughout my interview with Kai Hess below!
Hear from Kai Hess, Founding Product Designer at Mage:
Hear from "XYZ" highlights real-world use cases for all of us to learn from. Data collection sets the foundation for what's possible with our data workflows… yet we don't talk enough about it. A clear example of poor design hampering data processes is electronic health records. Doctors, the ones entering some of the most valuable data in the world, despise this product resulting in the messiest data one can work with. In contrast, it became clear to me how integral design is in capturing quality data when I had a chance to work with a designer on a data product feature. I'm excited to dive deeper into understanding the intersection of data and design with Kai Hess.
What is the role of design when creating data infrastructure products?
Kai: “The role of design is the same for basically any product that's created, and it is to provide utility and delight and function to the people that use it. Data scientists are a core part of the modern tech stack and modern business.
I mean, data is driving every single thing in Silicon Valley these days, and I want them to have a tool that is curated and designed specifically for them. And so anytime we're creating a product, whether it's internal or external facing, I'm trying to think about how do I make a intuitive experience that actually makes someone's day to day better?
And as this is a tool, that's what you're really focused on here. It's a data scientist that's going to be in something for hours a day, every day of the week, thinking hard about data, extracting data, working with charts. And so we need to make it so that it's the best possible and most intuitive experience.
And so the function of a designer more than anything else is to listen, and interpret, and understand the complaints and desires of the people that are using the actual tool. And so for a data focus tool, I'm talking with a lot of data scientists saying, "Hey, what are your pain points? What drives you crazy? What are you spending too much time on?" if you're spending time rewriting the same code every single day, there's a problem there or there's an opportunity there.
So design is really like being like a detective investigating where there are opportunities and problems in somebody's workflow. So for the tool that I'm working on right now, it's focused specifically on creating data pipelines for data engineers and data scientists.
We're figuring out how do we make that workflow better for them, better than it's ever been before, and design just gets to be a big push for making it right for those people.”
On your LinkedIn, you described one of your roles as “I made data sexy.” What's your process for making people pay more attention to data?
Kai: "So I am incredibly fortunate. That the people that I'm working with and the industry that I'm in, data is already paid way too much attention. I am working with data scientists and I'm working with tech people and BI people, and their whole world is data, so it's not really a matter of making that interesting to them or enticing to them cuz it's already those things.
The the role that I would have and the desire that I would have would be to make it effective. I think this is a problem with all data these days is that we are collecting so much stuff and really high quality stuff, but that's also disorganized. It's also got a lot of bad quality stuff hidden inside of it.
And you know, if you're making a snap decision, that's not a huge deal. But if you're driving business decisions, if you are training an AI model that's going to react and think about something. Your data has to be incredibly, incredibly high quality. And so a designer, their role would be to make sure that not only is data being moved effectively to the people that use it, but that they understand the efficacy of it, that they understand the transparency and the things that have happened to it before it gets to them. Because, that oversight, that understanding of everything that's happening to the data is the most important thing when it comes to having a quality assumptions based on something.”
How can data professionals best collaborate with designers to obtain high quality data within products?
Kai: “That's a good question. The better framing for that maybe would be, what can designers learn from data scientists and data engineers? I mentioned briefly earlier that designers are the conduit to intuitive experiences, delightful experiences, and effective experiences, but we are really just listeners.
We pay attention to what the people who are building the core functions and using the core features you're talking about. So data scientists, they should be giving the feedback on what they would like to see from design. We wish that we could effectively convey charts more. We wish that we could handle X, Y, Z type of scenarios that they're running into every day.
And a designer is kind of a liaison between all these groups where they get to look at both visual designs, interaction designs, even stuff like workflow designs and say, "Hey, let's see if we can make it better." So I think of myself as someone that is a facilitator just for progress within an organization.
And sometimes that just means sitting with a data scientist and seeing how they do things. I won't know necessarily what's going to be an opportunity or improvement, but just spending some time with them, getting to know kind of what their workflow is like is a really, really effective way to figure out how to design something progressive.
And I think one of the realities of work is that so often we're separated into silos. You know, product engineers hand off to product managers, data scientists hand off to somebody else. Research goes to these UX people and it trickles up, and everyone's kind of like talking, but you're in your own little unit. And so, for designers, it's really about breaking down those walls and seeing as a whole workflow, how do we create something that's great for the end user?
And that's what it's all about. Like we're not all engineering great things just for ourselves in private. We're making this for real people that are gonna use it. And so I don't always know because I'm a designer, I don't really know the depth of the problems that data scientists are dealing with. They are on a different level than me entirely when it comes to engineering and data innateness and their statistical ability.
It just blows me away. I kind of come in as a dumb little designer and I say, All right, what kind of stuff are you running into? Let me see if I can think of it with a totally different perspective. And so I think just creating that, that relationship between people is the biggest thing. Design's not gonna come in and push their agenda when it comes to design.
They're gonna listen and figure out what somebody needs and then go from there.”
Kai Hess is the Founding Product Designer at Mage. I would highly encourage checking out his design work at https://www.mage.ai to see how he is helping data professionals (it’s stunning).
What are others saying in the DataOps space?
What: A data executive describes five best practices in combining design thinking and data engineering.
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Who: You are a data engineer looking to implement more human-centric workflows with your data.
What: A thorough article on how to design for data quality in how products capture data.
Why: Data capture is one of the most important steps in the data lifecycle and sets the foundation for what is possible with the data.
Who: You are building a new product feature and want to ensure the data team is happy downstream.
What: A repository of design thinking resources from IDEO.
Why: IDEO has been one of the leading firms in the world for design since the 1990s.
Who: You are brand new to design thinking and want to learn from some of the best.