Categories:Innovation

Under the Hood: Data & Design

PostedWillow Team

In this episode of Under the Hood, our series on the technical aspects of WillowTwin™, team members Kayla Medica, Regina Campbell, Danny Henley-Martin, and Dave Rennick got together to talk about data and design.

Data is highly complex, and gathering it is just the first step. WillowTwin™ visualises vast amounts of data from multiple sources in simple, easy to read ways. Danny, Regina, and Dave work in our Product and Design teams, and are responsible for ensuring WillowTwin™ users can more easily search, navigate, and interpret their data.

Transcript

Kayla: Today we’re going to be talking to a few Willowers about data, user friendliness, and how our platform is laid out.

Regina: I’m Regina Campbell, I’m the Director of Product and Design at Willow. I oversee our Platform and Data team, as well as our design function at Willow.

Dave: Hi I’m Dave Rennick, I’m Head of Design, I lead the Product Design team, and undertake design processes, discovery, through to UI design and discovery.

Danny: Hi I’m Danny Henley-Martin, I’m a Product Manager here at Willow for our Real Estate and Enterprise product offering, and our IoT services solution.

Kayla: Great to be here with all of you, I ask you first, Danny. Willow works in a lot of industries around the built environment, so it could be infrastructure or commercial real estate, how does data play a role in these industries and what is the importance of it?

Danny: Well data in these industries, on its own isn’t really much. Today we know they can reside in a lot of silos, isolated systems or platforms. And data is not enough, you need information. And so, with the WillowTwin™ you can really bring these different data sets together to draw out information and insights.

Kayla: Typically, not all of our customers are data scientists and can read raw data, so how does design play a role in helping our customers understand data?

Dave: I totally agree with the premise of your question, our customers are not data scientists. So it’s one thing to get all this data, piping in from these assets, these buildings or airports, railway systems or anything else. That’s all well and good but figuring out how a user is going to comprehend that data is where we step into what design is here to do.

I guess it’s quite easy to get carried away with the possibilities of data, because when you have lots of data you can do an infinite amount of things. You can aggregate this, or provide a summary of this, or provide an analytics of this, but to try and cut through that noise, I think for design it’s really important just to understand the user first, to go to the user first and understand what their current world looks like, what their current way of doing things is, and where those pain points actually lie.

To pick up on something Danny mentioned before, it’s not even about asking the customer what they want, because often they don’t know what they want. They don’t really know what they want in terms of a feature or a product. It’s more just about asking them where those pain points lie in their current day to day. From there, as designers, we then take away that information and try and synthesise that into a solution, a feature, a product.

Regina: To tie onto that, it’s really important to understand what is it that people are doing every day in their jobs, so that then we can come back and create a solution to help them with that. Because we can come at it with the lens of ‘this is what data we have available, this is what they’re trying to achieve, and where Willow’s experience can help them get to’.

Kayla: Regina, can you tell us more about the kind of work that you do and how that shapes our platform?

Regina: It’s a lot of understanding who the different types of user are, and what is it that they’re trying to achieve, and bringing that all together. I look after the platform and that goes across different verticals, so even though it might be somebody looking at a report from real estate, there are similarities that can be seen across rail, or mining, or other verticals as well. And so it is a lot about deeply understanding who these people are that are using the platform, what is it they’re trying to achieve, and where is it that we could help them achieve that and bring synergy across that.

I’ve worked across a lot of big data companies before and bringing lots of disparate data sources together so that we can gain insights, and the question isn’t always ‘what question do you as a customer want us to answer’, it’s ‘here’s new, interesting things we found with the data’, because we do have a team of data scientists who can interrogate that data and present interesting findings to them. But there’s still always questions that follow on after that.

Once they’re presented with one set of information, it’s always ‘that’s really interesting, what about this?’ or ‘what can I do now to help us achieve this next milestone?’ and being able to pull that all together. We definitely see a lot in moving towards making buildings more efficient, getting people access to all the information that they need to make decisions, how are the assets in a building operating, where pinch points? Say a piece of equipment might fail quite often, what’s causing that failure, what other things could we be looking at in terms of bringing up maintenance cycles, running possibly simulations on that data to say ‘if we did this in a different order, based off of the information we have, what might we achieve with that?’

Kayla: You mentioned access to data there, which is really interesting because the idea of democratising data is becoming more mainstream, I’m interested to hear from each of you what your perspective on that is.

Dave: Data on its own is kind of useless. If you’re looking at a single data point, let’s say I gave you the temperature of a sensor, without any other context or correlation around the temperature on that sensor. It would be meaningless; data is always about the correlation with another piece of data. And that other piece of data might be coming from a completely different physical place, it could be coming from all these different places and that comparative action that the user needs to be able to perform is really important. Just elaborating on the challenge you highlighted, Regina.

Regina: Yeah, and then there’s the other challenge of how to present it back to them. There’s so much research that goes into what are the right ways to visualise the data, what cognitive load goes into one being able to interpret the information and actually know what they’re at. I always love having a variety of charts that you can look at but you also end up with almost analysis paralysis. Somebody is in there looking at a variety of charts, what we don’t understand is how quickly they’re understanding that information and moving on from there. So there definitely is an element of making sure it’s as easy as possible to understand when they are reviewing this.

Danny: I think what Regina is highlight there is that there’s two ways of doing things. There’s the way of ‘okay, I’ve got a lot of data, I want to draw my own conclusions and kind of look at the data raw create information from that myself’ and that’s kind of like self-discovery. Then there are those common questions that you have across your entire portfolio of properties or different assets, and you’re like ‘well I just want to see the answer to this thing’ and then that thing can exist on a dashboard somewhere, okay, great.

So you’ve got your ‘here’s my reported view of common questions I always want to see the answer to’, and how should that be best displayed for me to understand and see the answer that I’m looking for, and then there’s the whole ‘I just want to explore everything I’ve got and see what else I can derive out of it that could be very important or we could get some value’.

Kayla: What kind of things have we done within Willow to make sure that our platform is user-friendly for anyone who uses it.

Regina: I think one thing that we have done is definitely a lot of time with our customers, when we’re making designs and decisions around what could we present back to them and investigating some of those spaces, it really has been around, let’s get this in front of the customer, let’s ask them questions. What do they think about this particular design, and get their feedback. And then incorporate that into what is it that we’re doing next. So we’re always keen to talk to the customer so that we can get that feedback from them and watch them using the experience.

Dave: And I’ll just add to that, not only get that feedback up front but continue to get that feedback as we develop the product and as we release features. While something we design might be the answer or the presumed answer to a particular problem, it’s actually a question to put in front of the user, to then develop upon or iterate upon. Adopting that mindset is generally something that we have done to answer the user needs.

Danny: Both Regina and Dave are talking about how we like to constantly get feedback. The product that we’ve developed, you know we also see the user analytics behind that as well so that gives us deeper insight, we can go back and verify things. At the beginning though, you just need to start with the common basics, like functional is a good place to start and you can always evolve from there.

I think, at Willow, we have done that on some of the capabilities that we have within the WillowTwin™ and how you can explore some of the data that we have been able to pull in. It’s been a fantastic journey actually, some of the design decisions that we’ve made along the way have been interesting but when you’re an agile company like Willow, we can change them when we get new feedback rapidly, and I think that’s what our customers and our users really love about the WillowTwin™, is that they’ve seen a way that they can do something, we’ve shown them, they use it, they identify an easier way they could achieve something, they share that feedback, and we incorporate that into our product and they see that there and it’s driven from their own feedback. Knowing they have that line of sight, I think makes them feel very much part of creating a world class product like the WillowTwin™.

Kayla: That’s all we have for today, thank you Regina, Dave, and Danny. I hope you learned something new today and feel more confident understand data and knowing that you can use WillowTwin™ for all your data needs without needing to be a data scientist.

 

Watch the previous episode of Under the Hood

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