Unlocking Digital Transformation in Engineering: Insights from Arup’s Chief Data Officer

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Hello, and welcome to the Engineering Influence podcast from ECEC.
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My name is Thomas Grogan, Senior Director, Economics and Market Intelligence,
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and I'm really excited today to have Tembi Holmes, Global Chief Data Officer
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from Arup on our podcast today.
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Tembi, thanks so much for joining us. Thomas, thank you for having me.
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I will. Well, it is our pleasure, and I would love to just dive right in and
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ask you, what does digital transformation mean to you?
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Digital transformation for me is not always about the technology.
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I know the word digital is a key part of transformation,
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but for me, it's really about optimizing your processes, your guardrails to
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achieve your intended business outcomes and strategy.
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That's what it means for me. And it means in some cases, modernizing some of
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your processes using, you know, next gen technology.
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In some cases, it might just mean clarity on how you leverage existing technology,
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not necessarily leveraging new technology.
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So, it's really about how you optimize your processes so that you can achieve
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your business strategy. That's what it means to me.
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Yeah, no, that makes sense. And so, you know, in your day-to-day as global chief
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data officer, how do you work on optimizing some of these processes or putting guardrails in place?
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So I like to say our industry, in as much as we may not realize it,
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is actually a really heavy data-driven industry.
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If you think about designing infrastructure, the amount of data that you need
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to acquire, analyze, so you can determine the best solutions,
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regardless of the discipline, it's quite astronomical.
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We're talking a large amount of data. So in my day-to-day, part of it is thinking
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through how we enable quality of design and engineering,
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how we enable excellence in our processes, how we enable speed and velocity
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in how we deliver for clients,
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and how we also accommodate changing business environment,
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how we accommodate changing climates in our designs and in our solutions,
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and how we do that while leveraging either the technology we currently have
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and making that very efficient, or how we continue to keep an eye on new technology
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that's coming so that we can achieve everything that I just talked about.
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So that's what I think about day to day when it comes to my role as chief data officer.
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Yeah. And so, you know, you mentioned our industry, the AEC engineering industry
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is pretty data heavy, data intensive.
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You have strong background working in technology in other industries outside ours.
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What are some of the lessons learned or, you know, things that you might borrow
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or steal from another industry that you would bring in to an AEC firm?
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One of the really fundamental lessons learned in a previous role was how technology
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speaks the same language,
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how we can standardize and bring consistency to the data as it flows through the technology.
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So today, if I look at Arup and our peers and we take a piece of concrete,
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do we refer to that piece of concrete the same way across our industry?
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I don't think so. Do we refer to the components that have been put into a beam
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in a building in the same language?
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In some cases, yes. In some cases, no.
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What that means for digitization is that we become a little slower because we're
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having to translate the language that we speak in our industry.
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So getting to a common language on how we refer to really key components of
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our design and architecture and construction workflow makes it so much easier as a consortium,
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because as you know, we tend to deliver as consortiums, as partners.
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It's not one firm that's usually involved in a large infrastructure project.
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That communication, making that standardized and consistent,
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both from a technology standpoint, but also from a data language standpoint,
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I think would really accelerate some of the digitization in our industry.
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Yeah, great point. And, you know, the piece around interoperability and speaking
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the same language, you know, I think is relevant to a lot of parts of business.
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And, you know, in your role,
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how do you think about, say, and this is kind of for advice to other firms,
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working with the C-suite and coming up with the technology strategy and how
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you translate maybe business objectives to these technology or data objectives?
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What sort of guidance would you have for, you know, a firm who's going to be
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undertaking a digital transformation?
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I would say when it comes to interoperability is to really think through what
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decisions you're making from a technology standpoint,
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not necessarily in a silo, but to think across your whole organization and to
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proactively determine how your technology is going to connect,
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how it is going to speak to each other.
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There's so many stories of companies purchasing technology because it promised
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them the dream and then completely failing to integrate that technology.
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And so not getting the return on investment that was expected or the efficiencies that were expected.
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So planning your integration strategy and thinking through how any new technology
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you're bringing in is going to fit in and how it's going to operate with everything
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you have, I think is number one.
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Number two is don't forget the data. I like to say technology are the pipes
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and data is the water that flows through it.
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The way you connect your pipes is going to be so helpful in terms of whether
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you get fast moving water or it's going to get stuck somewhere.
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And if you decide to put in pipes that are not made of the best materials,
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you will get contaminated water.
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So you lose that integrity in your data. And then you spend a lot more time
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checking and validating and asking questions whether this is the right data set.
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So it is really critical to think of the data as part of any technology transformation.
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Yeah, and I think that's a great analogy, thinking of water flowing through the pipes,
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What are some, you know, going back to guardrails that you mentioned in figuring
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out what digital transformation might mean,
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what sort of guardrails or processes do you think about putting in place to
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maintain that data integrity that's, you know, the life water flowing through an organization?
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Number one is security. Okay. You absolutely need to make sure that you've got
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security and good governance around who can access your information,
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how they can access it, how often, how you deprecate access.
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Because sometimes we think about giving access. We don't necessarily think about
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how we monitor whether the right people still have the right level of access.
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Over time, we tend to forget about it or think about it when people are exiting, that is critical.
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It is also important to stress test your own systems.
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Be your own hackers to see whether you're really sound. And anybody who's a
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CISO, Chief Information Security Officer,
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or is responsible for that side of things, will tell you that is something that
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they absolutely think about to do their own penetration testing and to see how
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robust the guardrails they're put in are.
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In addition, a lot of training and awareness to your own employees is critical
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because you're only as strong as your weakest link.
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If you think about major phishing or fraud that has happened to companies,
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it's usually via your weakest link.
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Somebody wasn't thinking, they were busy, something came through,
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they clicked on it and it gave access to systems.
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And so you almost need that waterfall of guardrails to say, yes,
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how do you provide security to make sure, yes, somebody but has clicked on a link.
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But how do you make sure it's not pervasive across your system?
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So security is number one, as you think about digital transformation.
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Yeah. And, you know, you bring up a good point around the training and people.
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How does your organization kind of...
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Train upskill make sure that you
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know as you implement a new technology your staff
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are able to use it as efficiently as
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possible or is there also other training that
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isn't let's say software specific but like
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you mentioned the learning about phishing or scams or
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things like that how do you work with you
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know your hr group or your talent
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development group to kind of keep the the
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skills of your staff modernized you're actually
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tapping touching on a topic that's really top of mind at the moment we are in
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the middle of our own transformation around data and when you think about like
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i mentioned transforming processes bringing in new technology or leveraging
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your existing technology in a different way the only way you're going to
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get success in terms of wide-scale adoption and scalable leveraging of that
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infrastructure and new processes is your employees knowing about it,
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your employees knowing how to navigate what you've brought in and what you've put in place.
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So that is so topical for me. And part of it is working really closely with
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our learning and development function.
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So every organization, I'm hoping, has a dedicated learning and development function.
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And then secondly, there are partners out there that really help organizations
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with going through that journey and can really determine what the measurable
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impact of those learning and development initiatives are and can really also
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articulate what success or what aspirational success looks like.
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Like, so it's acknowledging that not everybody is an expert at change management
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or rolling out training.
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And so partnering with the right folks to help you along, but also making sure
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that as an organization, we're making the right investments in learning and
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development and not just rolling out technology.
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And that's a common area of where people sometimes fail in terms of rolling
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out digital transformation, but forgetting about the change management and the
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learning that's involved.
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Right. And so, you know, let's say an organization has started to do some of
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these implementations and the change management piece seems to be going well. What are some...
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Next step, positive outcomes, measures, metrics they should be thinking about
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to know they are on the right track or on the right path of doing this digital transformation.
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Some people describe it as you're going to see a little bit of a decline in
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productivity before the curve swoops you back up and does a moonshot.
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What would that later stage in the life cycle look like at an organization?
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I always say it has to boil down to your bottom line.
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Okay. Are you seeing innovation that's helping you become more competitive,
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and therefore you're winning more bids, and therefore your revenue is increasing?
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Are you seeing efficiencies or productivity gains that's making sure that your overhead is shrinking?
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It's making sure that you're focusing on shrinking your overhead,
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not necessarily shrinking the size of the organization.
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You can still have the same level of employees, the same number of employees,
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but you want to make sure that you're seeing productivity gains and efficiency
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gains. It's reflected in your numbers.
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And it's really hard and challenging to really point, especially if you're a
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data leader, exactly where you're making an impact.
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And I think I speak for a lot of chief data officers here, even for some chief technology officers.
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It can be challenging to really point exactly where the efficiency is,
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but you have to spend the time to be able to make that link to what you're doing,
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the productivity gains you're seeing, and how that impacts your numbers. Mm-hmm.
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No, no, that is sometimes the key, right, as data people is we want to quantify everything,
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and yet the technological productivity gains can sometimes be very hard to quantify,
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especially in a professional services setting.
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So we've talked a bit about some of the data and metrics on the operational side.
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Are there any examples of using data outside the operations of the firm and working on projects?
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So using data to deliver a project more efficiently or design it in maybe a
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more cost-effective, sustainable way,
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does that also touch on your role as being a chief data officer?
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It absolutely does. some of the work and some of the prototypes and pilots that
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we've actually done with GenAI.
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And we've been leveraging Copilot, Microsoft Copilot, have indicated that you
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can get up to at least 30 minutes savings per day from an engineer who's leveraging Copilot on their work.
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Now, as we said earlier, what does the engineer then do with that time?
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Does it become a wash because then they went and did something else that's not
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necessarily suddenly adding value?
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And how do you track an 8,000-person organization to see what the aggregation
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of those productivity gains looks like?
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But yes, we've proved in a small cohort that you do get those productivity gains.
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And how do you then trace them back to innovation?
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Because is the engineer or architect then spending time on innovation because
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they were able to get some efficiencies?
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And how do you track that back to a new bid in terms of winning that work? work.
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So we're seeing it at a cohort level, but then translating it to a firm level
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is something we're still working on.
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What sort of challenges or, I don't know, comments are you hearing from your clients around data?
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Are they asking for more data to be shared with them when they are,
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you know, collaborating with you on a project?
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Do they not care? Like, has it changed?
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And how is that relationship impacted and what sort of data and metrics are
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out there that you're kind of working together with your client to deliver?
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What clients are asking for before, they would say, you have the model of the
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item thing, infrastructure that you have designed.
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I'm comfortable with it. Let's talk with a construction company.
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Construction company now our clients are saying well actually i want to be more sustainable.
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So can you give me enough information that can
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help me if i'm in a hazardous location for
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example let's say they flash the earthquakes can you give
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me more information about what decisions or options i have will be both of the
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construction so now your model has to be dynamic it cannot be anymore because
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at each given point the client wants to make a different decision So they need
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the data to be able to make that assessment.
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So we find clients are asking for more and more information so that they can
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make better decisions, especially around sustainability, around how they manage
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their carbon footprint, around maintenance.
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In the past, you could say, look, we'll just schedule a two-year maintenance for the Sprint.
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But we want to be able to leverage, you know, new technology that allows you
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to now have a mix of IoT to then say, well, let's actually make this a data-driven assessment,
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which measures parameters or the number of vehicles on the bridge or the weather
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that's impacting that bridge and
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then allows us to make a decision of when maintenance can actually happen.
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So we're seeing a more request for data so that our clients can make more informed
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decisions, not just Gatfield. In previous times, you know, everybody is an expert
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and it's in your head and you can suggest or recommend a decision.
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But now clients are asking for data-driven decisions and options.
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Yeah. And, you know, you bring up data to use for, say, sustainability and planning.
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What are, you know, maybe how mature is that environment right now?
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Now, does the data exist?
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Is it being tracked uniformly?
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You know, to what you were saying earlier, there's no standardized or oftentimes
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there aren't standardized measures or language.
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What are some of the maybe limitations right now with data and sustainability
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and how can it be improved?
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So it goes back to how you assess carbon factors, for example.
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Okay. and how you determine the naming conventions of some of the pieces that
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are involved in infrastructure. I talked about a piece of concrete.
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If you want to assess the carbon footprint, both the embodied carbon,
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the operational carbon of that
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piece of concrete, you would need to know what went into it to make it.
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That is not consistent across the whole industry. So within our own firm,
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we've got our own standards, our own language that we've created that allows
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us to be standardized within our own firm.
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But the moment we're now a consortium, that language is not there.
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The carbon factors themselves will tell you if you put XYZ in this piece of
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concrete, therefore this is your carbon factor that you have to apply.
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But then if it's firm A and firm B, and we don't necessarily agree on what we
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put in that piece of concrete in terms of the naming convention,
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then we're going to have to do this in a manual fashion.
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Whereas if we agreed on the naming convention, the language we used,
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then you can automate that process.
00:19:01.165 --> 00:19:05.425
This technology is as good as the standardized data that flows through it.
00:19:06.025 --> 00:19:10.285
So if you don't have the standardized data, it means a lot of these processes
00:19:10.285 --> 00:19:15.565
to really assess the carbon footprint or the operational carbon of assets is
00:19:15.565 --> 00:19:17.865
pretty manual at this point in time.
00:19:18.105 --> 00:19:21.745
And then if you continue with that lifecycle, when you come to your ESG reporting
00:19:21.745 --> 00:19:26.845
as well, companies are struggling with that because each company is still doing
00:19:26.845 --> 00:19:30.525
it differently, using different factors, using different language.
00:19:30.725 --> 00:19:33.605
So we do need to get to a point where that's all standardized.
00:19:33.605 --> 00:19:35.565
And I know there's a couple of startups.
00:19:35.985 --> 00:19:39.845
I know AWS has a few things going on with their climate pledge.
00:19:39.985 --> 00:19:43.825
And they've got, I think, about 400 signatories at the time who had pledged
00:19:43.825 --> 00:19:45.305
to start thinking about this data problem.
00:19:45.585 --> 00:19:50.765
So there's definitely activity and action around it. I think we haven't landed
00:19:50.765 --> 00:19:53.085
yet. It's not yet a mature area. Yeah.
00:19:53.565 --> 00:20:01.765
And so to, you know, take a little bit of a step back for the type of, say, data maturity,
00:20:02.045 --> 00:20:11.085
technology maturity, a firm like Arup has, ACEC has many member firms who are 50 employees or less.
00:20:11.245 --> 00:20:14.445
Some are just, you know, one or two mechanical engineers.
00:20:14.445 --> 00:20:22.085
Engineers what advice would you give to a smaller firm who you know doesn't
00:20:22.085 --> 00:20:28.465
have all the resources or staff available if they were trying to start a digital transformation.
00:20:29.165 --> 00:20:34.465
I would say start with your biggest point point and start with table stakes.
00:20:35.843 --> 00:20:43.323
What I mean by that is sometimes we all focus on the topical technology of the moment.
00:20:43.923 --> 00:20:46.623
Right now it's Gen AI. A few years ago it was blockchain.
00:20:47.343 --> 00:20:49.923
There's always some, before that it was RPA.
00:20:50.703 --> 00:20:54.723
There's always some exciting technology coming through. If you're a smaller
00:20:54.723 --> 00:20:56.363
firm, that's not where you want to put your money.
00:20:56.423 --> 00:21:00.763
Let the big firms do the exploration, the prototyping, come up with the use cases.
00:21:00.763 --> 00:21:03.583
The simple things around, I mentioned earlier,
00:21:03.883 --> 00:21:08.783
how your systems speak to each other, how your folks in the field,
00:21:08.803 --> 00:21:14.083
your employees in the field can communicate with each other on a project efficiently,
00:21:14.223 --> 00:21:17.363
can make changes, can validate some of the designs,
00:21:17.643 --> 00:21:19.763
can check each other's work.
00:21:20.103 --> 00:21:24.463
Simple things that are just table stakes, part of the workflow.
00:21:25.063 --> 00:21:30.563
We make those more efficiencies. You will then have enough time and investment left over.
00:21:30.763 --> 00:21:33.783
To then invest in the new topical exciting technologies.
00:21:34.783 --> 00:21:39.443
But sometimes just the normal business life cycle of delivering for our clients
00:21:39.443 --> 00:21:41.783
is very painful and it's not efficient.
00:21:42.963 --> 00:21:47.043
And sometimes we don't see the reason for investing in that because it's not the most exciting.
00:21:48.023 --> 00:21:54.423
Just having a really good ERP that's integrated into your resource planning application,
00:21:54.743 --> 00:21:59.603
that's integrated into your billability, if it's a different billability tracking
00:21:59.603 --> 00:22:02.583
application, if it's a different one, your travel dashboards,
00:22:02.683 --> 00:22:07.683
for example, and how you communicate and share data across your own team and externally.
00:22:07.843 --> 00:22:13.483
It's simple things that sometimes are not set up the right way because they're
00:22:13.483 --> 00:22:15.923
table sticks and they're not that exciting. Yeah.
00:22:16.243 --> 00:22:20.863
So I would say get your table stakes processes and workflows and applications
00:22:20.863 --> 00:22:22.543
working as best as you can.
00:22:23.474 --> 00:22:27.754
That sets you up as well for success as you try to leverage new technology.
00:22:28.374 --> 00:22:32.434
Right. So essentially having... Focus on your data as well. Make sure you have
00:22:32.434 --> 00:22:34.334
a good data management strategy.
00:22:35.594 --> 00:22:38.494
Because that allows you to be able to move faster if you need to.
00:22:39.734 --> 00:22:46.214
So it's really thinking about the building blocks and foundational tools or components.
00:22:46.214 --> 00:22:50.814
So there's the tools, the technology, but then other stuff like data,
00:22:50.954 --> 00:22:53.994
communication skills, things like that are
00:22:53.994 --> 00:22:57.274
always you know going to be in demand and
00:22:57.274 --> 00:23:00.194
important demand and important
00:23:00.194 --> 00:23:03.754
if you think about financial services industry right my
00:23:03.754 --> 00:23:08.354
take on why they're always really leading edge is because they're looking after
00:23:08.354 --> 00:23:13.194
our money and they've had to have really robust processes right so when a new
00:23:13.194 --> 00:23:18.034
technology comes in their foundational infrastructure is ready because they've
00:23:18.034 --> 00:23:21.354
been forced to be that way because of the industry. Right.
00:23:21.894 --> 00:23:25.174
Healthcare is fast coming up as also leading edge.
00:23:25.294 --> 00:23:29.394
Part of it is because of all the HIPAA laws that are in place that forces them
00:23:29.394 --> 00:23:31.094
to be really careful about that information.
00:23:31.854 --> 00:23:36.534
And so that forces them to have really optimized infrastructure so they can
00:23:36.534 --> 00:23:37.334
respond a little faster.
00:23:38.094 --> 00:23:44.114
Yeah, that's a very interesting point. They are, I suppose, from a cybersecurity
00:23:44.114 --> 00:23:52.274
and regulatory framework, they've had to, they have to, there's no room for error, right?
00:23:52.454 --> 00:23:55.374
There's no room for error. It is a very punitive industry.
00:23:55.634 --> 00:24:00.914
The consequences are pretty dire. and I cut my teeth actually in data management
00:24:00.914 --> 00:24:04.534
on a financial services institution where an employer is helping their chief
00:24:04.534 --> 00:24:08.254
data officer with their data management strategy because they realized that
00:24:08.254 --> 00:24:11.854
was the most important thing they needed to do so that they could remain in
00:24:11.854 --> 00:24:14.274
compliance and also respond to new regulations.
00:24:14.654 --> 00:24:19.814
And I think our industry has regulations, I agree, but there's some that are
00:24:19.814 --> 00:24:21.914
forcing us to become that way.
00:24:21.994 --> 00:24:25.454
If I think about all the sustainability regulations that are coming up of the
00:24:25.454 --> 00:24:29.554
EU, The EU is drafting an AI policy at the moment.
00:24:29.794 --> 00:24:33.514
So there's a lot of regulations coming that are going to force us to really
00:24:33.514 --> 00:24:36.234
focus on the data management side of things. Yeah.
00:24:36.914 --> 00:24:44.074
So then, you know, for a firm who is going to start, undergo a digital transformation
00:24:44.074 --> 00:24:51.114
journey, what do you think are some of the biggest risks that they should be aware of?
00:24:51.114 --> 00:24:55.674
It seems like cybersecurity, security in general, is definitely one of them.
00:24:55.714 --> 00:25:00.314
What else should someone keep in the back of their mind as they're starting
00:25:00.314 --> 00:25:04.994
to do change management, evaluate new tools, things like that?
00:25:06.014 --> 00:25:12.114
This one is more, I would call it, we talked about it, interoperability.
00:25:13.059 --> 00:25:18.099
You do not want to bring in technology that cannot be integrated into your existing technology.
00:25:18.979 --> 00:25:22.819
You also run the risk of investing in technology that's a fad.
00:25:22.979 --> 00:25:28.539
We've seen that when it came to RPA. I think people were pretty disappointed with what RPA can offer.
00:25:29.479 --> 00:25:33.959
And so you need to be really careful about the use cases that you are selecting
00:25:33.959 --> 00:25:36.419
for the digitization and the digital transformation.
00:25:37.859 --> 00:25:41.379
Well, that is good information to have.
00:25:41.379 --> 00:25:47.299
Have and temby thank you so much for joining us i do want to end you know on a positive note,
00:25:47.979 --> 00:25:52.039
what do you think are the top opportunities for firms with respect to digital
00:25:52.039 --> 00:25:57.879
transformation and you know having a modern data approach to their organization
00:25:57.879 --> 00:26:03.499
what can they look forward to once they complete this journey innovation oh
00:26:03.499 --> 00:26:07.599
my goodness can you imagine if we could leverage,
00:26:08.459 --> 00:26:11.719
gen ai to provide us and solve
00:26:11.719 --> 00:26:14.559
to provide us with solutions and some and solve
00:26:14.559 --> 00:26:17.299
some of the most complex computations that we have to
00:26:17.299 --> 00:26:19.959
do as engineers can you imagine if we could
00:26:19.959 --> 00:26:22.739
leverage it at scale i i just think there's
00:26:22.739 --> 00:26:25.539
so much opportunity there we're not there we're
00:26:25.539 --> 00:26:28.379
still exploring those cases but i just get so
00:26:28.379 --> 00:26:31.479
excited when i think about the potential of combining
00:26:31.479 --> 00:26:34.359
gen ai and computational design which a lot of firms actually
00:26:34.359 --> 00:26:37.399
average um i just think we can solve some really
00:26:37.399 --> 00:26:44.259
really complex and gnarly problems absolutely well temby thank you so much for
00:26:44.259 --> 00:26:48.979
joining us today this is really informative really helpful and really learned
00:26:48.979 --> 00:26:54.219
a lot uh you know i look forward to connecting again and thank you so much of
00:26:54.219 --> 00:26:56.319
course you're welcome thank you for having me again.