A discussion on how the IT4IT Reference Architecture for IT management works in many ways for many types of organizations and the demonstrated business benefits that are being realized as a result.
Many of the latest technologies -- such as Internet of Things (IoT) platforms, big data analytics, and cloud computing -- are making data-driven and efficiency-focused digital transformation more powerful. But exploiting these advances to improve municipal services for cities and urban government agencies face unique obstacles. Challenges range from a lack of common data sharing frameworks, to immature governance over multi-agency projects, to the need to find investment funding amid tight public sector budgets.
The good news is that architectural framework methods, extended enterprise knowledge sharing, and common specifying and purchasing approaches have solved many similar issues in other domains.
BriefingsDirect recently sat down with a panel to explore how The Open Group is ambitiously seeking to improve the impact of smart cities initiatives by implementing what works organizationally among the most complex projects.
The panel consists of Dr. Chris Harding, Chief Executive Officer atLacibus; Dr. Pallab Saha, Chief Architect at The Open Group; Don Brancato, Chief Strategy Architect at Boeing; Don Sunderland, Deputy Commissioner, Data Management and Integration, New York City Department of IT and Telecommunications, and Dr. Anders Lisdorf, Enterprise Architect for Data Services for the City of New York. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Chris, why are urban and regional government projects different from other complex digital transformation initiatives?
Harding: Municipal projects have both differences and similarities compared with corporate enterprise projects. The most fundamental difference is in the motivation. If you are in a commercial enterprise, your bottom line motivation is money, to make a profit and a return on investment for the shareholders. If you are in a municipality, your chief driving force should be the good of the citizens -- and money is just a means to achieving that end.
This is bound to affect the ways one approaches problems and solves problems. A lot of the underlying issues are the same as corporate enterprises face.
Bottom-up blueprint approach
Brancato: Within big companies we expect that the chief executive officer (CEO) leads from the top of a hierarchy that looks like a triangle. This CEO can do a cause-and-effect analysis by looking at instrumentation, global markets, drivers, and so on to affect strategy. And what an organization will do is then top-down.
In a city, often it’s the voters, the masses of people, who empower the leaders. And the triangle goes upside down. The flat part of the triangle is now on the top. This is where the voters are. And so it’s not simply making the city a mirror of our big corporations. We have to deliver value differently.
There are three levels to that. One is instrumentation, so installing sensors and delivering data. Second is data crunching, the ability to turn the data into meaningful information. And lastly, urban informatics that tie back to the voters, who then keep the leaders in power. We have to observe these in order to understand the smart city.
Saha: Two things make smart city projects more complex. First, typically large countries have multilevel governments. One at the federal level, another at a provincial or state level, and then city-level government, too.
This creates complexity because cities have to align to the state they belong to, and also to the national level. Digital transformation initiatives and architecture-led initiatives need to help.
Secondly, in many countries around the world, cities are typically headed by mayors who have merely ceremonial positions. They have very little authority in how the city runs, because the city may belong to a state and the state might have a chief minister or a premier, for example. And at the national level, you could have a president or a prime minster. This overall governance hierarchy needs to be factored when smart city projects are undertaken.
These two factors bring in complexity and differentiation in how smart city projects are planned and implemented.
Sunderland: I agree with everything that’s been said so far. In the particular case of New York City -- and with a lot of cities in the US -- cities are fairly autonomous. They aren’t bound to the states. They have an opportunity to go in the direction they set.
The problem is, of course, the idea of long-term planning in a political context. Corporations can choose to create multiyear plans and depend on the scale of the products they procure. But within cities, there is a forced changeover of management every few years. Sometimes it’s difficult to implement a meaningful long-term approach. So, they have to be more reactive.
Create demand to drive demand
Driving greater continuity can nonetheless come by creating ongoing demand around the services that smart cities produce. Under [former New York City mayor] Michael Bloomberg, for example, when he launched 311 and nyc.gov, he had a basic philosophy which was, you should implement change that can’t be undone.
If you do something like offer people the ability to reduce 10,000 [city access] phone numbers to three digits, that’s going to be hard to reverse. And the same thing is true if you offer a simple URL, where citizens can go to begin the process of facilitating whatever city services they need.
In like-fashion, you have to come up with a killer app with which you habituate the residents. They then drive demand for further services on the basis of it. But trying to plan delivery of services in the abstract -- without somehow having demand developed by the user base -- is pretty difficult.
By definition, cities and governments have a captive audience. They don’t have to pander to learn their demands. But whereas the private sector goes out of business if they don’t respond to the demands of their client base, that’s not the case in the public sector.
The public sector has to focus on providing products and tools that generate demand, and keep it growing in order to create the political impetus to deliver yet more demand.
Gardner: Anders, it sounds like there is a chicken and an egg here. You want a killer app that draws attention and makes more people call for services. But you have to put in the infrastructure and data frameworks to create that killer app. How does one overcome that chicken-and-egg relationship between required technical resources and highly visible applications?
Lisdorf: The biggest challenge, especially when working in governments, is you don’t have one place to go. You have several different agencies with different agendas and separate preferences for how they like their data and how they like to share it.
This is a challenge for any Enterprise Architecture (EA) because you can’t work from the top-down, you can’t specify your architecture roadmap. You have to pick the ways that it’s convenient to do a project that fit into your larger picture, and so on.
It’s very different working in an enterprise and putting all these data structures in place than in a city government, especially in New York City.
Gardner: Dr. Harding, how can we move past that chicken and egg tension? What needs to change for increasing the capability for technology to be used to its potential early in smart cities initiatives?
Framework for a common foundation
Harding: As Anders brought up, there are lots of different parts of city government responsible for implementing IT systems. They are acting independently and autonomously -- and I suspect that this is actually a problem that cities share with corporate enterprises.
Very large corporate enterprises may have central functions, but often that is small in comparison with the large divisions that it has to coordinate with. Those divisions often act with autonomy. In both cases, the challenge is that you have a set of independent governance domains -- and they need to share data. What’s needed is some kind of framework to allow data sharing to happen.
This framework has to be at two levels. It has to be at a policy level -- and that is going to vary from city to city or from enterprise to enterprise. It also has to be at a technical level. There should be a supporting technical framework that helps the enterprises, or the cities, achieve data sharing between their independent governance domains.
Gardner: Dr. Saha, do you agree that a common data framework approach is a necessary step to improve things?
Saha: Yes, definitely. Having common data standards across different agencies and having a framework to support that interoperability between agencies is a first step. But as Dr. Anders mentioned, it’s not easy to get agencies to collaborate with one another or share data. This is not a technical problem. Obviously, as Chris was saying, we need policy-level integration both vertically and horizontally across different agencies.
Some cities set up urban labs as a proof of concept. You can make assessment on how the demand and supply are aligned.
One way I have seen that work in cities is they set up urban labs. If the city architect thinks they are important for citizens, those services are launched as a proof of concept (POC) in these urban labs. You can then make an assessment on whether the demand and supply are aligned.
Obviously, it is a chicken-and-egg problem. We need to go beyond frameworks and policies to get to where citizens can try out certain services. When I use the word “services” I am looking at integrated services across different agencies or service providers.
The fundamental principle here for the citizens of the city is that there is no wrong door, he or she can approach any department or any agency of the city and get a service. The citizen, in my view, is approaching the city as a singular authority -- not a specific agency or department of the city.
Gardner: Don Brancato, if citizens in their private lives can, at an e-commerce cloud, order almost anything and have it show up in two days, there might be higher expectations for better city services.
Is that a way for us to get to improvement in smart cities, that people start calling for city and municipal services to be on par with what they can do in the private sector?
Public- and private-sector parity
Brancato: You are exactly right, Dana. That’s what’s driven the do it yourself (DIY) movement. If you use a cell phone at home, for example, you expect that you should be able to integrate that same cell phone in a secure way at work. And so that transitivity is expected. If I can go to Amazon and get a service, why can’t I go to my office or to the city and get a service?
This forms some of the tactical reasons for better using frameworks, to be able to deliver such value. A citizen is going to exercise their displeasure by their vote, or by moving to some other place, and is then no longer working or living there.
Traceability is also important. If I use some service, it’s then traceable to some city strategy, it’s traceable to some data that goes with it. So the traceability model, in its abstract form, is the idea that if I collect data it should trace back to some service. And it allows me to build a body of metrics that show continuously how services are getting better. Because data, after all, is the enablement of the city, and it proves that by demonstrating metrics that show that value.
So, in your e-commerce catalog idea, absolutely, citizens should be able to exercise the catalog. There should be data that shows its value, repeatability, and the reuse of that service for all the participants in the city.
Gardner: Don Sunderland, if citizens perceive a gap between what they can do in the private sector and public -- and if we know a common data framework is important -- why don’t we just legislate a common data framework? Why don’t we just put in place common approaches to IT?
Sunderland: There have been some fairly successful legislative actions vis-à-vis making data available and more common. The Open Data Law, which New York City passed back in 2012, is an excellent example. However, the ability to pass a law does not guarantee the ability to solve the problems to actually execute it.
In the case of the service levels you get on Amazon, that implies a uniformity not only of standards but oftentimes of [hyperscale] platform. And that just doesn’t exist [in the public sector]. In New York City, you have 100 different entities, 50 to 60 of them are agencies providing services. They have built vast legacy IT systems that don’t interoperate. It would take a massive investment to make them interoperate. You still have to have a strategy going forward.
The idea of adopting standards and frameworks is one approach. The idea is you will then grow from there. The idea of creating a law that tries to implement uniformity -- like an Amazon or Facebook can -- would be doomed to failure, because nobody could actually afford to implement it.
Since you can’t do top-down solutions -- even if you pass a law -- the other way is via bottom-up opportunities. Build standards and governance opportunistically around specific centers of interest that arise. You can identify city agencies that begin to understand that they need each other’s data to get their jobs done effectively in this new age. They can then build interconnectivity, governance, and standards from the bottom-up -- as opposed to the top-down.
Gardner: Dr. Harding, when other organizations are siloed, when we can’t force everyone into a common framework or platform, loosely coupled interoperability has come to the rescue. Usually that’s a standardized methodological approach to interoperability. So where are we in terms of gaining increased interoperability in any fashion? And is that part of what The Open Group hopes to accomplish?
Not something to legislate
Harding: It’s certainly part of what The Open Group hopes to accomplish. But Don was absolutely right. It’s not something that you can legislate. Top-down standards have not been very successful, whereas encouraging organic growth and building on opportunities have been successful.
The prime example is the Internet that we all love. It grew organically at a time when governments around the world were trying to legislate for a different technical solution; the Open Systems Interconnection (OSI) model for those that remember it. And that is a fairly common experience. They attempted to say, “Well, we know what the standard has to be. We will legislate, and everyone will do it this way.”
That often falls on its face. But to pick up on something that is demonstrably working and say, “Okay, well, let’s all do it like that,” can become a huge success, as indeed the Internet obviously has. And I hope that we can build on that in the sphere of data management.
It’s interesting that Tim Berners-Lee, who is the inventor of the World Wide Web, is now turning his attention to Solid, a personal online datastore, which may represent a solution or standardization in the data area that we need if we are going to have frameworks to help governments and cities organize.
A prime example is the Internet. It grew organically when governments were trying to legislate a solution. That often falls on its face. Better to pick up on something that is working in practice.
Gardner: Dr. Lisdorf, do you agree that the organic approach is the way to go, a thousand roof gardens, and then let the best fruit win the day?
Lisdorf: I think that is the only way to go because, as I said earlier, any top-down sort of way of controlling data initiatives in the city are bound to fail.
Gardner: Let’s look at the cost issues that impact smart cities initiatives. In the private sector, you can rely on an operating expenditure budget (OPEX) and also gain capital expenditures (CAPEX). But what is it about the funding process for governments and smart cities initiatives that can be an added challenge?
How to pay for IT?
Brancato: To echo what Dr. Harding suggested, cost and legacy will drive a funnel to our digital world and force us -- and the vendors -- into a world of interoperability and a common data approach.
Cost and legacy are what compete with transformation within the cities that we work with. What improves that is more interoperability and adoption of data standards. But Don Sunderland has some interesting thoughts on this.
Sunderland: One of the great educations you receive when you work in the public sector, after having worked in the private sector, is that the terms CAPEX and OPEX have quite different meanings in the public sector.
Governments, especially local governments, raise money through the sale of bonds. And within the local government context, CAPEX implies anything that can be funded through the sale of bonds. Usually there is specific legislation around what you are allowed to do with that bond. This is one of those places where we interact strongly with the state, which stipulates specific requirements around what that kind of money can be used for. Traditionally it was for things like building bridges, schools, and fixing highways. Technology infrastructure had been reflected in that, too.
What’s happened is that the CAPEX model has become less usable as we’ve moved to the cloud approach because capital expenditures disappear when you buy services, instead of licenses, on the data center servers that you procure and own.
This creates tension between the new cloud architectures, where most modern data architectures are moving to, and the traditional data center, server-centric licenses, which are more easily funded as capital expenditures.
The rules around CAPEX in the public sector have to evolve to embrace data as an easily identifiable asset [regardless of where it resides]. You can’t say it has no value when there are whole business models being built around the valuation of the data that’s being collected.
There is great hope for us being able to evolve. But for the time being, there is tension between creating the newer beneficial architectures and figuring out how to pay for them. And that comes down to paying for [cloud-based operating models] with bonds, which is politically volatile. What you pay for through operating expenses comes out of the taxes to the people, and that tax is extremely hard to come by and contentious.
So traditionally it’s been a lot easier to build new IT infrastructure and create new projects using capital assets rather than via ongoing expenses directly through taxes.
Gardner: If you can outsource the infrastructure and find a way to pay for it, why won’t municipalities just simply go with the cloud entirely?
Cities in the cloud, but services grounded
Saha: Across the world, many governments -- not just local governments but even state and central governments -- are moving to the cloud. But one thing we have to keep in mind is that at the city level, it is not necessary that all the services be provided by an agency of the city.
It could be a public/private partnership model where the city agency collaborates with a private party who provides part of the service or process. And therefore, the private party is funded, or allowed to raise money, in terms of only what part of service it provides.
Many cities are addressing the problem of funding by taking the ecosystem approach because many cities have realized it is not essential that all services be provided by a government entity. This is one way that cities are trying to address the constraint of limited funding.
Gardner: Dr. Lisdorf, in a city like New York, is a public cloud model a silver bullet, or is the devil in the details? Or is there a hybrid or private cloud model that should be considered?
Lisdorf: I don’t think it’s a silver bullet. It’s certainly convenient, but since this is new technology there are lot of things we need to clear up. This is a transition, and there are a lot of issues surrounding that.
One is the funding. The city still runs in a certain way, where you buy the IT infrastructure yourself. If it is to change, they must reprioritize the budgets to allow new types of funding for different initiatives. But you also have issues like the culture because it’s different working in a cloud environment. The way of thinking has to change. There is a cultural inertia in how you design and implement IT solutions that does not work in the cloud.
There is still the perception that the cloud is considered something dangerous or not safe. Another view is that the cloud is a lot safer in terms of having resilient solutions and the data is safe.
This is all a big thing to turn around. It’s not a simple silver bullet. For the foreseeable future, we will look at hybrid architectures, for sure. We will offload some use cases to the cloud, and we will gradually build on those successes to move more into the cloud.
Gardner: We’ve talked about the public sector digital transformation challenges, but let’s now look at what The Open Group brings to the table.
Dr. Saha, what can The Open Group do? Is it similar to past initiatives around TOGAFas an architectural framework? Or looking at DoDAF, in the defense sector, when they had similar problems, are there solutions there to learn from?
Smart city success strategies
Saha: At The Open Group, as part of the architecture forum, we recently set up a Government Enterprise Architecture Work Group. This working group may develop a reference architecture for smart cities. That would be essential to establish a standardization journey around smart cities.
One of the reasons smart city projects don’t succeed is because they are typically taken on as an IT initiative, which they are not. We all know that digital technology is an important element of smart cities, but it is also about bringing in policy-level intervention. It means having a framework, bringing cultural change, and enabling a change management across the whole ecosystem.
At The Open Group work group level, we would like to develop a reference architecture. At a more practical level, we would like to support that reference architecture with implementation use cases. We all agree that we are not going to look at a top-down approach; no city will have the resources or even the political will to do a top-down approach.
Given that we are looking at a bottom-up, or a middle-out, approach we need to identify use cases that are more relevant and successful for smart cities within the Government Enterprise Architecture Work Group. But this thinking will also evolve as the work group develops a reference architecture under a framework.
Gardner: Dr. Harding, how will work extend from other activities of The Open Group to smart cities initiatives?
Collective, crystal-clear standards
Harding: For many years, I was a staff member, but I left The Open Group staff at the end of last year. In terms of how The Open Group can contribute, it’s an excellent body for developing and understanding complex situations. It has participants from many vendors, as well as IT users, and from the academic side, too.
Such a mix of participants, backgrounds, and experience creates a great place to develop an understanding of what is needed and what is possible. As that understanding develops, it becomes possible to define standards. Personally, I see standardization as kind of a crystallization process in which something solid and structured appears from a liquid with no structure. I think that the key role The Open Group plays in this process is as a catalyst, and I think we can do that in this area, too.
Gardner: Don Brancato, same question; where do you see The Open Group initiatives benefitting a positive evolution for smart cities?
Brancato: Tactically, we have a data exchange model, the Open Data Element Framework that continues to grow within a number of IoT and industrial IoT patterns. That all ties together with an open platform, and into Enterprise Architecture in general, and specifically with models like DODAF, MODAF, and TOGAF.
Data catalogs provide proof of the activities of human systems, machines, and sensors to the fulfillment of their capabilities and are traceable up to the strategy.
We have a really nice collection of patterns that recognize that the data is the mechanism that ties it together. I would have a look at the open platform and the work they are doing to tie-in the service catalog, which is a collection of activities that human systems or machines need in order to fulfill their roles and capabilities.
The notion of data catalogs, which are the children of these service catalogs, provides the proof of the activities of human systems, machines, and sensors to the fulfillment of their capabilities and then are traceable up to the strategy.
I think we have a nice collection of standards and a global collection of folks who are delivering on that idea today.
Gardner: What would you like to see as a consumer, on the receiving end, if you will, of organizations like The Open Group when it comes to improving your ability to deliver smart city initiatives?
Use-case consumer value
Sunderland: I like the idea of reference architectures attached to use cases because -- for better or worse -- when folks engage around these issues -- even in large entities like New York City -- they are going to be engaging for specific needs.
Reference architectures are really great because they give you an intuitive view of how things fit. But the real meat is the use case, which is applied against the reference architecture. I like the idea of developing workgroups around a handful of reference architectures that address specific use cases. That then allows a catalog of use cases for those who facilitate solutions against those reference architectures. They can look for cases similar to ones that they are attempting to resolve. It’s a good, consumer-friendly way to provide value for the work you are doing.
Gardner: I’m sure there will be a lot more information available along those lines at www.opengroup.org.
When you improve frameworks, interoperability, and standardization of data frameworks, what success factors emerge that help propel the efforts forward? Let’s identify attractive drivers of future smart city initiatives. Let’s start with Dr. Lisdorf. What do you see as a potential use case, application, or service that could be a catalyst to drive even more smart cities activities?
Lisdorf: Right now, smart cities initiatives are out of control. They are usually done on an ad-hoc basis. One important way to get standardization enforced -- or at least considered for new implementations – is to integrate the effort as a necessary step in the established procurement and security governance processes.
Whenever new smart cities initiatives are implemented, you would run them through governance tied to the funding and the security clearance of a solution. That’s the only way we can gain some sort of control.
This approach would also push standardization toward vendors because today they don’t care about standards; they all have their own. If we included in our procurement and our security requirements that they need to comply with certain standards, they would have to build according to those standards. That would increase the overall interoperability of smart cities technologies. I think that is the only way we can begin to gain control.
Gardner: Dr. Harding, what do you see driving further improvement in smart cities undertakings?
Prioritize policy and people
Harding: The focus should be on the policy around data sharing. As I mentioned, I see two layers of a framework: A policy layer and a technical layer. The understanding of the policy layer has to come first because the technical layer supports it.
The development of policy around data sharing -- or specifically on personal data sharing because this is a hot topic. Everyone is concerned with what happens to their personal data. It’s something that cities are particularly concerned with because they hold a lot of data about their citizens.
Gardner: Dr. Saha, same question to you.
Saha: I look at it in two ways. One is for cities to adopt smart city approaches. Identify very-high-demand use cases that pertain to environmental mobility, or the economy, or health -- or whatever the priority is for that city.
Identifying such high-demand use cases is important because the impact is directly seen by the people, which is very important because the benefits of having a smarter city are something that need to be visible to the people using those services, number one.
The other part, that we have not spoken about, is we are assuming that the city already exists, and we are retrofitting it to become a smart city. There are places where countries are building entirely new cities. And these brand-new cities are perfect examples of where these technologies can be tried out. They don’t yet have the complexities of existing cities.
It becomes a very good lab, if you will, a real-life lab. It’s not a controlled lab, it’s a real-life lab where the services can be rolled out as the new city is built and developed. These are the two things I think will improve the adoption of smart city technology across the globe.
Gardner: Don Brancato, any ideas on catalysts to gain standardization and improved smart city approaches?
City smarts and safety first
Brancato: I like Dr. Harding’s idea on focusing on personal data. That’s a good way to take a group of people and build a tactical pattern, and then grow and reuse that.
In terms of the broader city, I’ve seen a number of cities successfully introduce programs that use the notion of a safe city as a subset of other smart city initiatives. This plays out well with the public. There’s a lot of reuse involved. It enables the city to reuse a lot of their capabilities and demonstrate they can deliver value to average citizens.
In order to keep cities involved and energetic, we should not lose track of the fact that people move to cities because of all of the cultural things they can be involved with. That comes from education, safety, and the commoditization of price and value benefits. Being able to deliver safety is critical. And I suggest the idea of traceability of personal data patterns has a connection to a safe city.
Traceability in the Enterprise Architecture world should be a standard artifact for assuring that the programs we have trace to citizen value and to business value. Such traceability and a model link those initiatives and strategies through to the service -- all the way down to the data, so that eventually data can be tied back to the roles.
For example, if I am an individual, data can be assigned to me. If I am in some role within the city, data can be assigned to me. The beauty of that is we automate the role of the human. It is even compounded to the notion that the capabilities are done in the city by humans, systems, machines, and sensors that are getting increasingly smarter. So all of the data can be traceable to these sensors.
Gardner: Don Sunderland, what have you seen that works, and what should we doing more of?
Sunderland: I am still fixated on the idea of creating direct demand. We can’t generate it. It’s there on many levels, but a kind of guerrilla tactic would be to tap into that demand to create location-aware applications, mobile apps, that are freely available to citizens.
The apps can use existing data rather than trying to go out and solve all the data sharing problems for a municipality. Instead, create a value-added app that feeds people location-aware information about where they are -- whether it comes from within the city or without. They can then become habituated to the idea that they can avail themselves of information and services directly, from their pocket, when they need to. You then begin adding layers of additional information as it becomes available. But creating the demand is what’s key.
When 311 was created in New York, it became apparent that it was a brand. The idea of getting all those services by just dialing those three digits was not going to go away. Everybody wanted to add their services to 311. This kind of guerrilla approach to a location-aware app made available to the citizens is a way to drive more demand for even more people.
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The next BriefingsDirect cloud efficiency case study explores how a storage-as-a-service offering in a university setting gains performance and lower total cost benefits by a move to all-flash storage.
Here are some excerpts:
Gardner: How is satisfying the storage demands at a large and diverse university setting a challenge? Is there something about your users and the diverse nature of their needs that provides you with a complex requirements list?
Dunington: A university setting isn't much different than any other business. The demands are the same. UBC has about 65,000 students and about 15,000 staff. The students these days are younger kids, they all have iPhones and iPads, and they just want to push buttons and get instant results and instant gratification. And that boils down to the services that we offer.
We have to be able to offer those services, because as most people know, there are choices -- and they can go somewhere else and choose those other products.
Our team is a rather small team. There are 15 members in our team, so we have to be agile, we have to be able to automate things, and we need tools that can work and fulfill those needs. So it's just like any other business, even though it’s a university setting.
Gardner: Can you give us a sense of the scale that describes your storage requirements?
Dunington: We do SaaS, we also do infrastructure-as-a-service (IaaS). EduCloud is a self-service IaaS product that we deliver to UBC, but we also deliver it to 25 other higher institutions in the Province of British Columbia.
We have been doing IaaS for five years, and we have been very, very successful. So more people are looking to us for guidance.
Because we are not just delivering to UBC, we have to be up running and always able to deliver, because each school has different requirements. At different times of the year -- because there is registration, there are exam times -- these things have to be up. You can’t not be functioning during an exam and have 600 students not able to take the tests that they have been studying for. So it impacts their life and we want to make sure that we are there and can provide the services for what they need.
Gardner: In order to maintain your service levels within those peak times, do you in your IaaS and storage services employ hybrid-cloud capabilities so that you can burst? Or are you doing this all through your own data center and your own private cloud?
Dunington: We do it all on-campus. British Columbia has a law that says all the data has to stay in Canada. It’s a data-sovereignty law, the data can't leave the borders.
That's why EduCloud has been so successful, in my opinion, because of that option. They can just go and throw things out in the private cloud.
The public cloud providers are providing more services in Canada: Amazon Web Services (AWS) and Microsoft Azure cloud are putting data centers in Canada, which is good and it gives people an option. Our team’s goal is to provide the services, whether it's a hybrid model or all on-campus. We just want to be able to fulfill those needs.
Gardner: It sounds like the best of all worlds. You are able to give that elasticity benefit, a lot of instant service requirements met for your consumers. But you are starting to use cloud pay-as-you-go types of models and get the benefit of the public cloud model -- but with the security, control and manageability of the private clouds.
What decisions have you made about your storage underpinnings, the infrastructure that supports your SaaS cloud?
Dunington: We have a large storage footprint. For our site, it’s about 12 petabytes of storage. We realized that we weren’t meeting the needs with spinning disks. One of the problems was that we had runaway virtual workloads that would cause problems, and they would impact other services. We needed some mechanism to fix that.
We wanted to make sure that we had the ability to attain quality of service levels and control those runaway virtual machines in our footprint.
We went through the whole request for proposal (RFP) process, and all the IT infrastructure vendors responded, but we did have some guidelines that we wanted to go through. One of the things we did is present our problems and make sure that they understood what the problems were and what they were trying to solve.
And there were some minimum requirements. We do have a backup vendor of choice that they needed to merge with. And quality of service is a big thing. We wanted to make sure that we had the ability to attain quality of service levels and control those runaway virtual machines in our footprint.
Gardner: You gained more than just flash benefits when you got to flash storage, right?
Streamlined, safe, flash storage
Dunington: Yes, for sure. With an entire data center full of spinning disks, it gets to the point where the disks start to manage you; you are no longer managing the disks. And the teams out there changing drives, removing volumes around it, it becomes unwieldy. I mean, the power, the footprint, and all that starts to grow.
Also, Vancouver is in a seismic zone, we are right up against the Pacific plate and it's a very active seismic area. Heaven forbid anything happens, but one of the requirements we had was to move the data center into the interior of the province. So that was what we did.
When we brought this new data center online, one of the decisions the team made was to move to an all-flash storage environment. We wanted to be sure that it made financial sense because it's publicly funded, and also improved the user experience, across the province.
Gardner: As you were going about your decision-making process, you had choices, what made you choose what you did? What were the deciding factors?
Dunington: There were a lot of deciding factors. There’s the technology, of being able to meet the performance and to manage the performance. One of the things was to lock down runaway virtual machines and to put performance tiers on others.
But it’s not just the technology; it's also the business part, too. The financial part had to make sense. When you are buying any storage platform, you are also buying the support team and the sales team that come with it.
Our team believes that technology is a certain piece of the pie, and the rest of it is relationship. If that relationship part doesn't work, it doesn’t matter how well the technology part works -- the whole thing is going to break down.
Because software is software, hardware is hardware -- it breaks, it has problems, there are limitations. And when you have to call someone, you have to depend on him or her. Even though you bought the best technology and got the best price -- if it doesn't work, it doesn’t work, and you need someone to call.
So those service and support issues were all wrapped up into the decision.
We chose the Hewlett Packard Enterprise (HPE) 3PAR all-flash storage platform. We have been very happy with it. We knew the HPE team well. They came and worked with us on the server blade infrastructure, so we knew the team. The team knew how to support all of it.
We also use the HPE OneView product for provisioning, and it integrated into that all. It also supported the performance optimization tool (IT Operations Management for HPE OneView) to let us set those values, because one of the things in EduCloud is customers choose their own storage tier, and we mark the price on it. So basically all we would do is present that new tier as new data storage within VMware and then they would just move their workloads across non-disruptively. So it has worked really well.
The 3PAR storage piece also integrates with VMware vRealize Operations Manager. We offer that to all our clients as a portal so they can see how everything is working and they can do their own diagnostics. Because that’s the one goal we have with EduCloud, it has to be self-service. We can let the customers do it, that's what they want.
Gardner: Not that long ago people had the idea that flash was always more expensive and that they would use it for just certain use-cases rather than pervasively. You have been talking in terms of a total cost of ownership reduction. So how does that work? How does the economics of this over a period of time, taking everything into consideration, benefit you all?
Economic sense at scale
Dunington: Our IT team and our management team are really good with that part. They were able to break it all down, and they found that this model would work at scale. I don’t know the numbers per se, but it made economic sense.
Spinning disks will still have a place in the data center. I don't know a year from now if an all-flash data center will make sense, because there are some records that people will throw in and never touch. But right now with the numbers on how we worked it out, it makes sense, because we are using the standard bronze, the gold, the silver tiers, and with the tiers it makes sense.
The 3PAR solution also has dedupe functionality and the compression that they just released. We are hoping to see how well that trends. Compression has only been around for a short period of time, so I can’t really say, but the dedupe has done really well for us.
Gardner: The technology overcomes some of the other baseline economic costs and issues, for sure.
We have talked about the technology and performance requirements. Have you been able to qualify how, from a user experience, this has been a benefit?
Dunington: The best benchmark is the adoption rate. People are using it, and there are no help desk tickets, so no one is complaining. People are using it, and we can see that everything is ramping up, and we are not getting tickets. No one is complaining about the price, the availability. Our operational team isn't complaining about it being harder to manage or that the backups aren’t working. That makes me happy.
The big picture
Gardner: Brent, maybe a word of advice to other organizations that are thinking about a similar move to private cloud SaaS. Now that you have done this, what might you advise them to do as they prepare for or evaluate a similar activity?
Not everybody needs that speed, not everybody needs that performance, but it is the future and things will move there.
Dunington: Look at the full picture, look at the total cost of ownership. There’s the buying of the hardware, and there's also supporting the hardware, too. Make sure that you understand your requirements and what your customers are looking for first before you go out and buy it. Not everybody needs that speed, not everybody needs that performance, but it is the future and things will move there. We will see in a couple of years how it went.
Look at the big picture, step back. It’s just not the new shiny toy, and you might have to take a stepped approach into buying, but for us it worked. I mean, it’s a solid platform, our team sleeps well at night, and I think our customers are really happy with it.
Gardner: This might be a little bit of a pun in the education field, but do your homework and you will benefit.
Dunington: Yes, for sure.
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We’ll now learn how a leading-edge manufacturer, Hirotec, in the global automotive industry, takes advantage of IoT and Operational Technology (OT) combined to deliver dependable, managed, and continuous operations.
Here to help us to find the best factory of the future attributes is Justin Hester, Senior Researcher in the IoT Lab at Hirotec Corp. in Hiroshima, Japan. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: What's happening in the market with business and technology trends that’s driving this need for more modern factories and more responsive supply chains?
Hester: Our customers are demanding shorter lead times. There is a drive for even higher quality, especially in automotive manufacturing. We’re also seeing a much higher level of customization requests coming from our customers. So how can we create products that better match the unique needs of each customer?
As we look at how we can continue to compete in an ever-competitive environment, we are starting to see how the solutions from IoT can help us.
Gardner: What is it about IoT and Industrial IoT (IIoT) that allows you to do things that you could not have done before?
Hester: Within the manufacturing space, a lot of data has been there for years; for decades. Manufacturing has been very good at collecting data. The challenges we've had, though, is bringing in that data in real-time, because the amount of data is so large. How can we act on that data quicker, not on a day-by-day basis or week-by-week basis, but actually on a minute-by-minute basis, or a second-by-second basis? And how do we take that data and contextualize it?
It's one thing in a manufacturing environment to say, “Okay, this machine is having a challenge.” But it’s another thing if I can say, “This machine is having a challenge, and in the context of the factory, here's how it's affecting downstream processes, and here's what we can do to mitigate those downstream challenges that we’re going to have.” That’s where IoT starts bringing us a lot of value.
The analytics, the real-time contextualization of that data that we’ve already had in the manufacturing area, is very helpful.
Gardner: So moving from what may have been a gather, batch, analyze, report process -- we’re now taking more discrete analysis opportunities and injecting that into a wider context of efficiency and productivity. So this is a fairly big change. This is not incremental; this is a step-change advancement, right?
A huge step-change
Hester: It’s a huge change for the market. It's a huge change for us at Hirotec. One of the things we like to talk about is what we jokingly call the Tuesday Morning Meeting. We talk about this idea that in the morning at a manufacturing facility, everyone gets together and talks about what happened yesterday, and what we can do today to make up for what happened yesterday.
Instead, now we’re making that huge step-change to say, “Why don't we get the data to the right people with the right context and let them make a decision so they can affect what's going on, instead of waiting until tomorrow to react to what's going on?” It’s a huge step-change. We’re really looking at it as how can we take small steps right away to get to that larger goal.
In manufacturing areas, there's been a lot of delay, confusion, and hesitancy to move forward because everyone sees the value, but it's this huge change, this huge project. At Hirotec, we’re taking more of a scaled approach, and saying let's start small, let’s scale up, let’s learn along the way, let's bring value back to the organization -- and that's helped us move very quickly.
Gardner: We’d like to hear more about that success story but in the meantime, tell us about Hirotec for those who don't know of it. What role do you play in the automotive industry, and how are you succeeding in your markets?
Hester: Hirotec is a large, tier-1 automotive supplier. What that means is we supply parts and systems directly to the automotive original equipment manufacturers (OEMs), like Mazda, General Motors, FCA, Ford, and we specialize in door manufacturing, as well as exhaust system manufacturing. So every year we make about 8 million doors, 1.8 million exhaust systems, and we provide those systems mainly to Mazda and General Motors, but also we provide that expertise through tooling.
For example, if an automotive OEM would like Hirotec’s expertise in producing these parts, but they would like to produce them in-house, Hirotec has a tooling arm where we can provide that tooling for automotive manufacturing. It's an interesting strategy that allows us to take advantage of data both in our facilities, but then also work with our customers on the tooling side to provide those lessons learned and bring them value there as well.
Gardner: How big of a distribution are we talking about? How many factories, how many countries; what’s the scale here?
Hester: We are based in Hiroshima, Japan, but we’re actually in nine countries around the world, currently with 27 facilities. We have reached into all the major continents with automotive manufacturing: we’re in North America, we’re in Europe, we’re all throughout Asia, in China and India. We have a large global presence. Anywhere you find automotive manufacturing, we’re there supporting it.
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Gardner: With that massive scale, very small improvements can turn into very big benefits. Tell us why the opportunity in a manufacturing environment to eke out efficiency and productivity has such big payoffs.
Hester: So especially in manufacturing, what we find when we get to those large scales like you're alluding to is that a 1 percent or 2 percent improvement has huge financial benefits. And so the other thing is in manufacturing, especially automotive manufacturing, we tend to standardize our processes, and within Hirotec, we’ve done a great job of standardizing that world-class leadership in door manufacturing.
And so what we find is when we get improvements not only in IoT but anywhere in manufacturing, if we can get 1 percent or 2 percent, not only is that a huge financial benefit but because we standardized globally, we can move that to our other facilities very quickly, doubling down on that benefit.
Gardner: Well, clearly Hirotec sees this as something to really invest in, they’ve created the IoT Lab. Tell me a little bit about that and how that fits into this?
The IoT Lab works
Hester: The IoT Lab is a very exciting new group, it's part of our Advanced Engineering Center (AEC). The AEC is a group out of our global headquarters and this group is tasked with the five- to 10-year horizon. So they're able to work across all of our global organizations with tooling, with engineering, with production, with sales, and even our global operations groups. Our IoT group goes and finds solutions that can bring value anywhere in the organization through bringing in new technologies, new ideas, and new solutions.
And so we formed the IoT Lab to find how can we bring IoT-based solutions into the manufacturing space, into the tooling space, and how actually can those solutions not only help our manufacturing and tooling teams but also help our IT teams, our finance teams, and our sales teams.
Gardner: Let's dig back down a little bit into why IT, IoT and Operational Technology (OT) are into this step-change opportunity, looking for some significant benefits but being careful in how to institute that. What is required when you move to a more an IT-focused, a standard-platform approach -- across all the different systems -- that allows you to eke these great benefits?
Tell us about how IoT as a concept is working its way into the very edge of the factory floor.
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Hester: One of the things we’re seeing is that IT is beginning to meld, like you alluded to, with OT -- and there really isn't a distinction between OT and IT anymore. What we're finding is that we’re starting to get to these solution levels by working with partners such as PTC and Hewlett Packard Enterprise (HPE) to bring our IT group and our OT group all together within Hirotec and bring value to the organization.
What we find is there is no longer a need in OT that becomes a request for IT to support it, and also that IT has a need and so they go to OT for support. What we are finding is we have organizational needs, and we’re coming to the table together to make these changes. And that actually within itself is bringing even more value to the organization.
Instead of coming last-minute to the IT group and saying, “Hey, we need your support for all these different solutions, and we’ve already got everything set, and you are just here to put it in,” what we are seeing, is that they bring the expertise in, help us out upfront, and we’re finding better solutions because we are getting experts both from OT and IT together.
We are seeing this convergence of these two teams working on solutions to bring value. And they're really moving everything to the edge. So where everyone talks about cloud-based computing -- or maybe it’s in their data center -- where we are finding value is in bringing all of these solutions right out to the production line.
We are doing data collection right there, but we are also starting to do data analytics right at the production line level, where it can bring the best value in the fastest way.
Gardner: So it’s an auspicious time because just as you are seeking to do this, the providers of technology are creating micro data centers, and they are creating Edgeline converged systems, and they are looking at energy conservation so that they can do this in an affordable way -- and with storage models that can support this at a competitive price.
What is it about the way that IT is evolving and providing platforms and systems that has gotten you and The IoT Lab so excited?
Excitement at the edge
Hester: With IoT and IT platforms, originally to do the analytics, we had to go up to the cloud -- that was the only place where the compute power existed. Solution providers now are bringing that level of intelligence down to the edge. We’re hearing some exciting things from HPE on memory-driven computing, and that's huge for us because as we start doing these very complex analytics at the edge, we need that power, that horsepower, to run different applications at the same time at the production line. And something like memory-driven solutions helps us accomplish that.
It's one thing to have higher-performance computing, but another thing to gain edge computing that's proper for the factory environment. In a manufacturing environment it's not conducive to a standard servers, a standard rack where it needs dust protection and heat protection -- that doesn't exist in a manufacturing environment.
The other thing we're beginning to see with edge computing, that HPE provides with Edgeline products, is that we have computers that have high power, high ability to perform the analytics and data collection capabilities -- but they're also proper for the environment.
I don't need to build out a special protection unit with special temperature control, humidity control – all of which drives up energy costs, which drives up total costs. Instead, we’re able to run edge computing in the environment as it should be on its own, protected from what comes in a manufacturing environment -- and that's huge for us.
Gardner: They are engineering these systems now with such ruggedized micro facilities in mind. It's quite impressive that the very best of what a data center can do, can now be brought to the very worst types of environments. I'm sure we'll see more of that, and I am sure we'll see it get even smaller and more powerful.
Do you have any examples of where you have already been able to take IoT in the confluence of OT and IT to a point where you can demonstrate entirely new types of benefits? I know this is still early in the game, but it helps to demonstrate what you can do in terms of efficiency, productivity, and analytics. What are you getting when you do this well?
IoT insights save time and money
Hester: Taking the stepped strategy that we have, we actually started at Hirotec very small with only eight machines in North America and we were just looking to see if the machines are on, are they running, and even from there, we saw a value because all of a sudden we were getting that real-time contextualized insight into the whole facility. We then quickly moved over to one of our production facilities in Japan, where we have a brand-new robotic inspection system, and this system uses vision sensors, laser sensors, force sensors -- and it's actually inspecting exhaust systems before they leave the facility.
We very quickly implemented an IoT solution in that area, and all we did was we said, “Hey, we just want to get insight into the data, so we want to be able to see all these data points. Over 400 data points are created every inspection. We want to be able to see this data, compared in historical ways -- so let’s bring context to that data, and we want to provide it in real-time.”
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What we found from just those two projects very quickly is that we're bringing value to the organization because now our teams can go in and say, “Okay, the system is doing its job, it's inspecting things before they leave our facility to make sure our customers always get a high-quality product.” But now, we’re able to dive in and find different trends that we weren't able to see before because all we were doing is saying, “Okay, this system leaves the facility or this system doesn't.”
And so already just from that application, we’ve been able to find ways that our engineers can even increase the throughput and the reliability of the system because now they have these historical trends. They were able to do a root-cause analysis on some improvements that would have taken months of investigation; it was completed in less than a week for us.
And so that's a huge value -- not only in that my project costs go down but now I am able to impact the organization quicker, and that's the big thing that Hirotec is seeing. It’s one thing to talk about the financial cost of a project, or I can say, “Okay, here is the financial impact,” but what we are seeing is that we’re moving quicker.
And so, we're having long-term financial benefits because we’re able to react to things much faster. In this case, we’re able to reduce months of investigation down to a week. That means that when I implement my solution quicker, I'm now bringing that impact to the organization even faster, which has long-term benefits. We are already seeing those benefits today.
Gardner: You’ll obviously be able to improve quality, you’ll be able to reduce the time to improving that quality, gain predictive analytics in your operations, but also it sounds like you are going to gain metadata insights that you can take back into design for the next iteration of not only the design for the parts but the design for the tooling as well and even the operations around that. So that intelligence at the edge can be something that is a full lifecycle process, it goes right back to the very initiation of both the design and the tooling.
Data-driven design, decisions
Hester: Absolutely, and so, these solutions, they can't live in a silo. We're really starting to look at these ideas of what some people call the Digital Thread, the Digital Twin. We’re starting to understand what does that mean as you loop this data back to our engineering teams -- what kind of benefits can we see, how can we improve our processes, how can we drive out into the organization?
And one of the biggest things with IoT-based solutions is that they can't stay inside this box, where we talked about OT to IT, we are talking about manufacturing, engineering, these IoT solutions at their best, all they really do is bring these groups together and bring a whole organization together with more contextualized data to make better decisions faster.
And so, exactly to your point, as we are looping back, we’re able to start understanding the benefit we’re going to be seeing from bringing these teams together.
Gardner: One last point before we close out. It seems to me as well that at a macro level, this type of data insight and efficiency can be brought into the entire supply chain. As you're providing certain elements of an automobile, other suppliers are providing what they specialize in, too, and having that quality control and integration and reduced time-to-value or mean-time-to-resolution of the production issues, and so forth, can be applied at a macro level.
So how does the automotive supplier itself look at this when it can take into consideration all of its suppliers like Hirotec are doing?
Hester: It's a very early phase, so a lot of the suppliers are starting to understand what this means for them. There is definitely a macro benefit that the industry is going to see in five to 10 years. Suppliers now need to start small. One of my favorite pictures is a picture of the ocean and a guy holding a lighter. It [boiling the ocean] is not going to happen. So we see these huge macro benefits of where we’re going, but we have to start out somewhere.
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A lot of suppliers, what we’re recommending to them, is to do the same thing we did, just start small with a couple of machines, start getting that data visualized, start pulling that data into the organization. Once you do that, you start benefiting from the data, and then start finding new use-cases.
As these suppliers all start doing their own small projects and working together, I think that's when we are going to start to see the macro benefits but in about five to 10 years out in the industry.
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Learn here why a new era of responsive and dynamic IT infrastructure is demanded, and how one high-performance digital manufacturing leader aims to get there sooner rather than later.
Here to describe how an entertainment industry innovator leads the charge for bleeding-edge IT-as-a-service capabilities is Jeff Wike, CTO of DreamWorks Animation in Glendale, California. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Tell us why the older way of doing IT infrastructure and hosting apps and data just doesn't cut it anymore. What has made that run out of gas?
Wike: You have to continue to improve things. We are in a world where technology is advancing at an unbelievable pace. The amount of data, the capability of the hardware, the intelligence of the infrastructure are coming. In order for any business to stay ahead of the curve -- to really drive value into the business – it has to continue to innovate.
Gardner: IT has become more pervasive in what we do. I have heard you all refer to yourselves as digital manufacturing. Are the demands of your industry also a factor in making it difficult for IT to keep up?
Wike: When I say we are a digital manufacturer, it’s because we are a place that manufacturers content, whether it's animated films or TV shows; that content is all made on the computer. An artist sits in front of a workstation or a monitor, and is basically building these digital assets that we put through simulations and rendering so in the end it comes together to produce a movie.
That's all about manufacturing, and we actually have a pipeline, but it's really like an assembly line. I was looking at a slide today about Henry Ford coming up with the first assembly line; it's exactly what we are doing, except instead of adding a car part, we are adding a character, we’re adding a hair to a character, we’re adding clothes, we’re adding an environment, and we’re putting things into that environment.
We are manufacturing that image, that story, in a linear way, but also in an iterative way. We are constantly adding more details as we embark on that process of three to four years to create one animated film.
Gardner: Well, it also seems that we are now taking that analogy of the manufacturing assembly line to a higher plane, because you want to have an assembly line that doesn't just make cars -- it can make cars and trains and submarines and helicopters, but you don't have to change the assembly line, you have to adjust and you have to utilize it properly.
So it seems to me that we are at perhaps a cusp in IT where the agility of the infrastructure and its responsiveness to your workloads and demands is better than ever.
Greater creativity, increased efficiency
Wike: That's true. If you think about this animation process or any digital manufacturing process, one issue that you have to account for is legacy workflows, legacy software, and legacy data formats -- all these things are inhibitors to innovation. There are a lot of tools. We actually write our own software, and we’re very involved in projects related to computer science at the studio.
We’ll ask ourselves, “How do you innovate? How can you change your environment to be able to move forward and innovate and still carry around some of those legacy systems?”
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And one of the things we’ve done over the past couple of years is start to re-architect all of our software tools in order to take advantage of massive multi-core processing to try to give artists interactivity into their creative process. It’s about iterations. How many things can I show a director, how quickly can I create the scene to get it approved so that I can hand it off to the next person, because there's two things that you get out of that.
One, you can explore more and you can add more creativity. Two, you can drive efficiency, because it's all about how much time, how many people are working on a particular project and how long does it take, all of which drives up the costs. So you now have these choices where you can add more creativity or -- because of the compute infrastructure -- you can drive efficiency into the operation.
So where does the infrastructure fit into that, because we talk about tools and the ability to make those tools quicker, faster, more real-time? We conducted a project where we tried to create a middleware layer between running applications and the hardware, so that we can start to do data abstraction. We can get more mobile as to where the data is, where the processing is, and what the systems underneath it all are. Until we could separate the applications through that layer, we weren’t really able to do anything down at the core.
Core flexibility, fast
Now that we have done that, we are attacking the core. When we look at our ability to replace that with new compute, and add the new templates with all the security in it -- we want that in our infrastructure. We want to be able to change how we are using that infrastructure -- examine usage patterns, the workflows -- and be able to optimize.
Before, if we wanted to do a new project, we’d say, “Well, we know that this project takes x amount of infrastructure. So if we want to add a project, we need 2x,” and that makes a lot of sense. So we would build to peak. If at some point in the last six months of a show, we are going to need 30,000 cores to be able to finish it in six months, we say, “Well, we better have 30,000 cores available, even though there might be times when we are only using 12,000 cores.” So we were buying to peak, and that’s wasteful.
What we wanted was to be able to take advantage of those valleys, if you will, as an opportunity -- the opportunity to do other types of projects. But because our infrastructure was so homogeneous, we really didn't have the ability to do a different type of project. We could create another movie if it was very much the same as a previous film from an infrastructure-usage standpoint.
By now having composable, or software-defined infrastructure, and being able to understand what the requirements are for those particular projects, we can recompose our infrastructure -- parts of it or all of it -- and we can vary that. We can horizontally scale and redefine it to get maximum use of our infrastructure -- and do it quickly.
Gardner: It sounds like you have an assembly line that’s very agile, able to do different things without ripping and replacing the whole thing. It also sounds like you gain infrastructure agility to allow your business leaders to make decisions such as bringing in new types of businesses. And in IT, you will be responsive, able to put in the apps, manage those peaks and troughs.
Does having that agility not only give you the ability to make more and better movies with higher utilization, but also gives perhaps more wings to your leaders to go and find the right business models for the future?
Wike: That’s absolutely true. We certainly don't want to ever have a reason to turn down some exciting project because our digital infrastructure can’t support it. I would feel really bad if that were the case.
In fact, that was the case at one time, way back when we produced Spirit: Stallion of the Cimarron. Because it was such a big movie from a consumer products standpoint, we were asked to make another movie for direct-to-video. But we couldn't do it; we just didn’t have the capacity, so we had to just say, “No.” We turned away a project because we weren’t capable of doing it. The time it would take us to spin up a project like that would have been six months.
The world is great for us today, because people want content -- they want to consume it on their phone, on their laptop, on the side of buildings and in theaters. People are looking for more content everywhere.
Yet projects for varied content platforms require different amounts of compute and infrastructure, so we want to be able to create content quickly and avoid building to peak, which is too expensive. We want to be able to be flexible with infrastructure in order to take advantage of those opportunities.
Gardner: How is the agility in your infrastructure helping you reach the right creative balance? I suppose it’s similar to what we did 30 years ago with simultaneous engineering, where we would design a physical product for manufacturing, knowing that if it didn't work on the factory floor, then what's the point of the design? Are we doing that with digital manufacturing now?
Artifact analytics improve usage, rendering
Wike: It’s interesting that you mention that. We always look at budgets, and budgets can be money budgets, it can be rendering budgets, it can be storage budgets, and networking -- I mean all of those things are commodities that are required to create a project.
Artists, managers, production managers, directors, and producers are all really good at managing those projects if they understand what the commodity is. Years ago we used to complain about disk space: “You guys are using too much disk space.” And our production department would say, “Well, give me a tool to help me manage my disk space, and then I can clean it up. Don’t just tell me it's too much.”
One of the initiatives that we have incorporated in recent years is in the area of data analytics. We re-architected our software and we decided we would re-instrument everything. So we started collecting artifacts about rendering and usage. Every night we ran every digital asset that had been created through our rendering, and we also collected analytics about it. We now collect 1.2 billion artifacts a night.
And we correlate that information to a specific asset, such as a character, basket, or chair -- whatever it is that I am rendering -- as well as where it’s located, which shot it’s in, which sequence it’s in, and which characters are connected to it. So, when an artist wants to render a particular shot, we know what digital resources are required to be able to do that.
One of the things that’s wasteful of digital resources is either having a job that doesn't fit the allocation that you assign to it, or not knowing when a job is complete. Some of these rendering jobs and simulations will take hours and hours -- it could take 10 hours to run.
At what point is it stuck? At what point do you kill that job and restart it because something got wedged and it was a dependency? And you don't really know, you are just watching it run. Do I pull the plug now? Is it two minutes away from finishing, or is it never going to finish?
Just the facts
Before, an artist would go in every night and conduct a test render. And they would say, “I think this is going to take this much memory, and I think it's going to take this long.” And then we would add a margin of error, because people are not great judges, as opposed to a computer. This is where we talk about going from feeling to facts.
So now we don't have artists do that anymore, because we are collecting all that information every night. We have machine learning that then goes in and determines requirements. Even though a certain shot has never been run before, it is very similar to another previous shot, and so we can predict what it is going to need to run.
Now, if a job is stuck, we can kill it with confidence. By doing that machine learning and taking the guesswork out of the allocation of resources, we were able to save 15 percent of our render time, which is huge.
I recently listened to a gentleman talk about what a difference of 1 percent improvement would be. So 15 percent is huge, that's 15 percent less money you have to spend. It's 15 percent faster time for a director to be able to see something. It's 15 percent more iterations. So that was really huge for us.
Gardner: It sounds like you are in the digital manufacturing equivalent of working smarter and not harder. With more intelligence, you can free up the art, because you have nailed the science when it comes to creating something.
Creative intelligence at the edge
Wike: It's interesting; we talk about intelligence at the edge and the Internet of Things (IoT), and that sort of thing. In my world, the edge is actually an artist. If we can take intelligence about their work, the computational requirements that they have, and if we can push that data -- that intelligence -- to an artist, then they are actually really, really good at managing their own work.
It's only a problem when they don't have any idea that six months from now it's going to cause a huge increase in memory usage or render time. When they don't know that, it's hard for them to be able to self-manage. But now we have artists who can access Tableau reports everyday and see exactly what the memory usage was or the compute usage of any of the assets they’ve created, and they can correct it immediately.
On Megamind, a film DreamWorks Animation released several years ago, it was prior to having the data analytics in place, and the studio encountered massive rendering spikes on certain shots. We really didn't understand why.
After the movie was complete, when we could go back and get printouts of logs to analyze, we determined that these peaks in rendering resources were caused by his watch. Whenever the main character’s watch was in a frame, the render times went up. We looked at the models, and well-intended artists had taken a model of a watch and every gear was modeled, and it was just a huge, heavy asset to render.
But it was too late to do anything about it. But now, if an artist were to create that watch today, they would quickly find out that they had really over-modeled that watch. We would then need to go in and reduce that asset down, because it's really not a key element to the story. And they can do that today, which is really great.
Gardner: I am a big fan of animated films, and I am so happy that my kids take me to see them because I enjoy them as much as they do. When you mention an artist at the edge, it seems to me it’s more like an army at the edge, because I wait through the end of the movie, and I look at the credits scroll -- hundreds and hundreds of people at work putting this together.
So you are dealing with not just one artist making a decision, you have an army of people. It's astounding that you can bring this level of data-driven efficiency to it.
Movie-making’s mobile workforce
Wike: It becomes so much more important, too, as we become a more mobile workforce.
Now it becomes imperative to be able to obtain the information about what those artists are doing so that they can collaborate. We know what value we are really getting from that, and so much information is available now. If you capture it, you can find so many things that we can really understand better about our creative process to be able to drive efficiency and value into the entire business.
Gardner: Before we close out, maybe a look into the crystal ball. With things like auto-scaling and composable infrastructure, where do we go next with computing infrastructure? As you say, it's now all these great screens in people's hands, handling high-definition, all the networks are able to deliver that, clearly almost an unlimited opportunity to bring entertainment to people. What can you now do with the flexible, efficient, optimized infrastructure? What should we expect?
Wike: There's an explosion in content and explosion in delivery platforms. We are exploring all kinds of different mediums. I mean, there’s really no limit to where and how one can create great imagery. The ability to do that, the ability to not say “No” to any project that comes along is going to be a great asset.
We always say that we don't know in the future how audiences are going to consume our content. We just know that we want to be able to supply that content and ensure that it’s the highest quality that we can deliver to audiences worldwide.
Gardner: It sounds like you feel confident that the infrastructure you have in place is going to be able to accommodate whatever those demands are. The art and the economics are the variables, but the infrastructure is not.
Wike: Having a software-defined environment is essential. I came from the software side; I started as a programmer, so I am coming back into my element. I really believe that now that you can compose infrastructure, you can change things with software without having to have people go in and rewire or re-stack, but instead change on-demand. And with machine learning, we’re able to learn what those demands are.
I want the computers to actually optimize and compose themselves so that I can rest knowing that my infrastructure is changing, scaling, and flexing in order to meet the demands of whatever we throw at it.
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