Interview with Clare Hickie: Cloud, Data Security, and AI

Clare Hickie, CTO (Chief Technology Officer) EMEA, joined Workday in June 2018. As well as being a passionate technology champion, she inspires tech leaders to take full advantage of the extensive Workday platform. An IT specialist, Clare Hickie began her career in the dotcom era and then moved into digital transformation. Before joining Workday, she worked for large blue chip companies such as GSK (GlaxoSmithKline) and General Electric. Originally from Ireland, she has lived in the UK for many years but is now based back on the east coast of Ireland. She holds a BSc (Hons) in Computer Science and Business Studies from Brunel University, London.

Clare Hickie, CTO EMEA, Workday

Workday Cloud is a public cloud and is cloud native. How would a connection from a Workday Cloud system to an SAP on-prem or a cloud system be established?

We would do that through our integration framework. So, we've got a strong integration framework on a native level, but also we deliver on some pre-built connectors, and we have many organizations. We have over 10,000 customers and so the most important thing is that we can integrate and connect because we have a full appreciation for the other best-of-breed applications within our customers' landscape. It's really important for us that we can ensure that and have the technology capability to ensure that they can find those integrations. We really built it out from an integration framework perspective.

Would you say you often need to integrate Workday programs?

Our customers are some of the biggest brands in the world, and then some of our customers are medium size. The most important thing is that we've got a full appreciation for the landscapes for which they have already embedded. For example, when Workday is equally being deployed, we can ensure that we can integrate with the rest of their landscape; this is what becomes very important to us. So we do ensure that we can provide that level of connectivity, and that we have a framework in place so that they can manage their integrations equally. I would say the majority of our customers might be surprised with anything else, especially knowing the portfolio of customers that we have. And there's always going to be an integration requirement at some scale or level for further applications in the landscape. So, it's very important to us that we can allow for that.

Cloud availability is always dependent on IT infrastructure, is this a problem for your applications?

First of all, we’re running in our own data centers. The data center that we have is here in Dublin and is run by some of our partners, but it's our own Workday people that are managing the infrastructure and the monitoring activity for those data centers. We also have some customers in AWS. From a cloud perspective, the most important thing that is needed to be known here is that we consider ourselves a true cloud. This means performance and availability, as well as performance and scalability, is built into our recipes for our customers. In November, we announced that our availability now stands at 99.7 percent. So we're standing at 99.7 percent. And through the availability that we've had in that year, we've now got the confidence to take that one step farther. So we're now delivering on a 99.7 percent. From the availability perspective, we've got full appreciation for the global extent of our customers, we talked about that not only from an engineering perspective, but we also have a full, round the clock wave mechanism of being able to support our customers. It's really important to ensure that our customers have got the highest level of availability, from a Workday perspective, so that they see they can continue on operations.

From Workday’s perspective, what is the advantage of HCM in the cloud compared to on-prem installations?

Every week, we say, our cloud is a multi-tenant environment; every single one of our customers are on the exact same version. We deliver two big releases a year, as well as on a weekly and monthly basis. And so, for every single one of our customers, when we talk about our community of practice and about the energy that we feel from our customer perspective around Workday, we’re referring to the fact that they all speak the same language. This is because every single one of those customers have the exact same version. So, there's one differentiator and the differentiator is that we're built for agility and change.

Could you expand on that?

This organization was born in 2005, by two phenomenal co-founders David Duffield and Aneel Bhursi. And they really had a vision to ensure that we were a complete SAAS player, which was very fundamentally different from the traditional enterprise architectures of the past. They built out to ensure that we had a cloud that's built for agility. And what I mean by that is that we're built for change. And a great example of that is that when we make further developments on our innovation path—as we have for the past 15 plus years—we're able to do it seamlessly. Because our architecture—the underpinning architecture—is abstracted from our application, so one doesn't start to affect the other. This means our customers do not get affected. And we also are pretty much technology agnostic.

What about agility in the cloud?

When you talk about agility, it's the agility to be able to change and pivot, and to be able to upscale and plan according to these very large loads. And doing that simultaneously as we grow our customers as well. So when we talk about these very large customers—customers that have over a million workers—as well as some of our medium-sized customers—with 500 workers—they get the same level of service. And that can be built out because it's the change element for those unplanned loads that we can provide for as a consequence of the architecture that was built many years ago by being a pure SAAS cloud player.

How does security factor in?

Another key component of it is around our security. And so many of the security components of the past based on an enterprise structure of, for example, your server database divemasters. This is where we become fundamentally different because we've got the same security model, as a consequence of those key technology decisions that were taken from when Workday was first developed. But basically, as a consequence of what we're able to deliver through our architecture, we can allow for the single level of authentication that we can provide, both from a transactional perspective to a reporting perspective and an integration perspective. So that becomes fundamentally different levels.

What advantages are there for data in the cloud?

We adopt 65 million workers, all of those workers on this exact same version; we deliver those innovations to all of our customers. At the same time, it's down to the customers what they think, what they use. And from a data perspective, what this allows for is that, because we use a unified data model, with all of our workers on the exact same version, we can deliver. This is just a great example of what we can deliver with AI, because of those decisions that actually got taken.

We can stand up very firmly and say that machine learning requires very large volumes of data, it absolutely does. And we can not only have that as a consequence of the amount of workers in our towns, it's also about the quality of the data. We can deliver machine learning rapidly right throughout our platform because of this uniform dataset, that inequality data set that we're able to provide as a result of being this pure SAAS player in the cloud that was developed over 50 years ago.

Could you talk about this more from an intelligence perspective?

We also have the ability to be able to store the data. And we can ingest that and combine it with the internal data. For example, you're pulling in money data, which we hold for our customers. We can do that very seamlessly also to be able to provide that ingestion. And what that allows for is a higher depth of insights and reporting and decision making that we can provide. Everything's built in memory on an object data model. So, all of these components are pure differentiators. From the moment that we were set up, and it allows us to have this level of intelligence in an architecture, that agility that we need.

We spoke earlier about being able to integrate all the best of breeds. And that's because we have got this huge appreciation for the large landscapes that some of our customers manage within their organizations. And one final component of that is extensibility; this becomes important too. So, we also provide that ability—extendibility—so our customers can actually look at their landscapes, and they can also start building some of their applications within Workday. So, it's about agility; it's about that intelligence that we have in our architecture, and it's also about being able to integrate and be connected. And that's where some of those differentiators really start to pay.

How does Workday handle individual customer requests and modifications?

We can’t do what we do without understanding the requirements of our customers. And so we provide for our customers in many ways. When we start to innovate, we bring our customers into an early adoption program and they provide us with feedback in terms of what we're innovating far, and what's most important for them. What's really important is to understand the problems. We face the biggest problems that our customers are trying to solve.

What we do is we end up collaborating really closely with our customers, in terms of what their strategic priorities are, what's important to them to be able to deliver on. And we've got a framework in place to be able to take in their requests. And then there's an assessment done of the amount of requests that are received in those particular areas of the product. And then this assessment will go through the viability into the design phases, etc.

Could you expand on that?

Taking that feedback on board from the customers is an incredible differentiator—we believe—from many of our competitors. But it's what makes that collaboration truly special for us. Because success comes from a customer's perspective, and we can’t deliver on that success without knowing what they want. It's essential to us that we listen to their needs. We've got a very strong customer service function in place to support our customers, and we've got managing partners and CSMs and everything. It's through multiple modalities to some degree that we receive the feedback. But everything then is built out in terms of where are the big requirements that we're starting to see in the interest of many things, innovation. Generative AI is a great example at the moment. We're already starting to go into one of the early adopter programs for some of those generative AI features that our customers have been previewed with. So then they go into this partnership mode with us before we go into what we call GA, so for deployment mode for all of our customers.

There's a very large framework, a very narrow network, in place to support our customers and what's really important is that we can ensure that we can meet the biggest problems that are customers are trying to solve. And we do! We can do that through some of those advantages that I just described.

How and with what data does the system learn? A statement from Workday said that the only data you use for your AI is data customers have given their consent to use. Is there any case where that does not apply?

No. That's a really easy answer. And that statement is absolutely correct. In terms of all of our customers it's an ISA; it’s what we call an Innovation Services Addendum. Our customers decide what data contributes to everything that we're doing around AI. And they equally can pull back from that at any point in time also, so we have the absolute consent of all of customers that are going into collaboration with us in terms of providing their data for our monitoring purposes.

Would you say that Workday's AI functions are a key selling point?

We've been doing AI for the best part of a decade; the only reason this question is coming up now is obviously because of a lot of the hype that has been driven in the past 12 months. We've been already delivering AI. The features that we've already gotten placed, they have already been delivering high value to our customers. That curiosity remains as obviously we're equally building out from a generative AI perspective. What's important to us is that we're all about augmenting human potential. And we're about ensuring that we can have this high size and impact. But most importantly, we do everything that we do in a very responsible AI way. It's about the value that we're able to provide to our customers, the biggest investment we can give them to be the most efficient businesses that they can be. But bearing in mind, we've been doing this a long time.

But I can share some use cases with you. We're delivering solutions for generating job descriptions, for managing talent, to manage growth plans. So many of these use cases that we're building out, we know that they're going to provide that level of efficiency that our customers are looking for, so that they can be more productive in the areas that they need to be in their business to deliver on their goals. So we've, as you can imagine, an extremely focused organization. And equally that comes from an AI perspective also. It's very important to us to be able to deliver those innovations, so we can ensure that we can share those successes with our customers as they start to come through.

Thank you very much for the interview!

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