Here are some excerpts:
Dermetzis: We have a unique opportunity to stand back and see what history and evolution provided over the past 20 years and say, "Okay, how can we provide one technology stack that starts addressing all those individual problems that started appearing over time?"
If you think of the majority of the systems out there, the way we describe them is that they were built from the ground up as islands. It was really very data-centric. The whole idea was that the enterprise resource planning (ERP) system gave all the solutions, which in reality isn't true.
What we tried to do at Workday was start from a completely white sheet of paper. The reality around ERP systems is actually making all this work together. You want your transactions, you want your validations, you want to secure your data, and at the same time you want access to that data and to be able to analyze it. So, that's the problem we set out to do.
What drove our technology architecture was first, we have a very simple mentality. You have a central system that stores transactions, and you make sure that it's safe, secure, encrypted, and all these great words. At the same time, we appreciate that systems, as well as humans, interact with this central transactional system. So we treat them not as an afterthought, but as equal citizens.
If you go back in time to when mainframes started appearing, it was about transactions, capturing transactions, and safeguarding those transactions. IT was the center of the universe and they called the shots. As it evolved over time, IT began to realize that departments wanted their own solutions. They try to extract the data and take them into areas, such as spreadsheets and what have you, for further analysis.
ERP solutions evolved over time and started adding technology solutions as problems occurred. They started with a need to report data and very quickly realized it was like climbing a ladder of hierarchic needs. When you get your basic reporting right, you need to start analyzing data.
The technologies at the time, around the relational models, don't actually address that very well. Then, you find other industries, like business intelligence (BI) vendors, appeared who tried to solve those problems.
The way things evolved, you started with an application, and integrations were an afterthought; they got bolted on. ... They kept on adding more and more and more layers of vendors, and the more the poor enterprise IT customers are trying to peel it, the more they start crying -- crying in terms of maintenance and maintenance dollars.
Old approach won't scale
Right now, the state of the art is hard-wiring most of these central solutions to these third-party solutions, and that basically doesn't scale. That's where technology kicks in and you have to adopt new open standard and web services standards.
What we try to do at Workday is understand holistically what the current problems are today, and say, "This is a golden opportunity." This is opposed to finding all existing technologies, cobbling them all together, and trying to solve the problems exactly the same way.
If you're managing any system with HRM systems, you need to communicate with other systems, be it for background checks, for providing information to benefit providers, connecting to third-party payrolls, or what have you.
Obviously, [traditional ERP vendors] were solving the problem incrementally, as they were going along. What we tried to do was address it all in the same place. Where we are right now is what I would describe as very business transaction-centric in what I define as legacy applications. Then, we want to take it more to an area which is business interactions, and interactions can happen from humans or machines.
We're creating a revolution in the ERP industry. As always, you have early adopters. At the other end of the bell-shaped curve, you've got the laggards. When you're talking to forward thinking, modern thinking, profit-oriented, innovative companies, they very quickly appreciate that the way to go is SaaS.
Now, they've got a bunch of questions, and most of the questions are around security -- "Is my data safe?" We have a huge variety of ways of assuring our customers that these are actually probably safer in our environment than on-premise.
Some customers wait, and some will just jump in the pool with everyone else. We are in our fifth year of existence, and it's very interesting to see how our customers are scaling from the small, lower end, to huge companies and corporations that are running on Workday.
A blast from the past
Applications are built on top of relational databases today, and then they are being designed thinking about the end-user, sitting in front of a browser, interacting with the system. But, really they were designed around capturing the transaction and being able to report straight-off that transaction.
The idea of integrating with third parties was an afterthought. Being an afterthought, what happened was that you find this new industry emerging, which is around extract, transform and load (ETL) tools and integration tools. It was a realization that we have to coexist within the many systems.
What happened was that they bolted on these integration third-party systems straight onto the database. That sounds very good. However, all the business logic, all the security, and the whole data structure that hangs together is known by the application -- and not by the database. When you bolt-on an integration technology on the side, you lose all that. You have to recreate it in the third-party technology.
Similarly, when it comes to reporting, relational technology does a phenomenal job with the use of SQL and producing reports, which I will define as two-dimensional reports, for producing lists, matrix reports, and summary reports. But, eventually, as business evolves, you need to analyze data and you have to create this idea of dimensionality. Well, yet another industry was created -- and it was bolted back onto the database level, which is the [BI] analytics, and this created cubes.
In fact, what they used were object-oriented technologies and in-memory solutions for reasons of performance to be able to analyze data. This is currently the state of the art.
The same treatment
Conversely, any request that comes into our system, be it from a UI or from a third-party system by integrations, we treat exactly the same way. They go through exactly the same functional application security. It knows exactly what the structure of your object model is. It gets evaluated exactly the same way and then it serves back the answer. So that fundamental principle solves most of our integration problems.
On the integration side, we just work off open standards. The only way that you can talk with a third-party system with Workday is through web services, and those services are contracts that we spec to the outside world. We may change things internally, but that's our problem.
That's the point where we have a technology around our enterprise service plus our integration server that actually talks the language that we do, standards web service based. At the same time, it's able to transform any bit of that information to whatever the receiving component wants, whether it's banking, the various formats, or whatever is out there.
We put the technology into the hands of our customers to be able to ratchet down the latest technology to whatever other files structures that they currently have. We provide that to our customers, so they can connect them to the card-scanning systems, security systems, badging systems, or even their own financial systems that they may have in house.
We're a SaaS vendor, and we do modify things and we add things, but those external contracts, which are the Web services talking to third-party systems, we respect and we don't change. So, in effect, we do not break the integrations.
Best way to access data
The next architectural benefit is about analyzing data. As I said, there are a lot of technologies out there that do a very good job at lists and matrix reporting. Eventually, most of these things end up in spreadsheets, where people do further analysis.
But the dream that we are aiming for continuously is: When you are looking at a screen, you see a number. That number could be an accumulation of counts that you'd be really interested in clicking on and finding out what those counts are -- name of applicants, name of positions, number of assets that you have. Or, it's an accumulation. You look at the balance sheet. You look at the big number. You want to click and figure out what comprises that number.
To do that, you have to have that analytical component and your transactional component all in the same place. You can't afford what I call I/Os. It's a huge penalty to go back and forth through a relational database on a disk. So, that forces you to bring everything into memory, because people expect to click something and within earth time get a response.
When you are traversing, you come to a number in a balance sheet, and as you're drilling around, what you are really doing in effect is traversing an object model underneath, and you should be able to get that for nothing.
The technology solutions that we opted for was this totally in-memory object model that allows us to do the basic embedded analytics, taking action on everything you see on the screen.
So the persistence layer is really forced by the analytical components. When you're analyzing information, it has to perform extremely fast. You only have one option, and that is memory. So, you have to bring everything up in-memory.
We do use a relational component, but not as a relational database. We use a relational database, which is what it's really good at securing your data, encrypting your data, backing up your data, restoring it, replicating it, and all these great utilities the database gives you, but we don't use a relational model. We use an object model, which is all in-memory.
But, you need to store things somewhere. In fact, we have a belief at Workday that the disk, which is more the relational component, is the future tape. What you used to use in legacy system was putting things on tape for safety and archiving reasons. We use disk, and we actually believe, if you look at the future, that nearly everything will be done exclusively in-memory.
Make way for metadata
And, there is another bit of technology that you add to that. We're a totally metadata-driven technology stack. Right now, we put out what we describe as updates three times a year. You put new applications, new features, and new innovations into the hands of your customers, and being in only one central place, we get immediate feedback on the usage, which we can enhance. And, we just keep on going on and keep on adding and adding more and more and more.
This is something that was an absolute luxury in your legacy stack, to take a complete release. You have to live through all the breakages that we mentioned before around integrations and the analytical component.
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