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Courtesy of Red Hat's Phil Simpson, below is a transcript of his speaking session on 'BPM Re-Imagined: The impact of emerging technologies on business automation' to Build a Thriving Enterprise that took place at BTOES From Home.
BPM platforms have been around for several decades now and execute important work at many organizations. The idea that business people can encode business policies and procedures in the form of process diagrams, that can then be used to automate business processes, has led to much greater efficiency and consistency in business operations.
As we look forward to an even more digital future, new technologies such as AI/ML, RPA, Decision Management and the Cloud are once again changing the way we approach business automation, and redefining what's possible to automate. In this session, Red Hat's Phil Simpson will review some of these critical changes, and how they will impact the roles of developers and business people as we build the next generation of applications.
Since so feel, it's wonderful to have you here. Phil is the senior, principal product marketing manager at Red Hat.
He is a red Hat now is part of IBM, and he's responsible for the go to market strategy and positioning for Red Hat, redheads, business process management and decision management products. Fail brings more than 20 years of experience working with BPM Technology, and is excited to have this opportunity to share his perspective today. With the ... Odense, and feel really honored to have you here, such a great industry leader, sharing this experience firsthand with us, and that really look forward to your presentation.
Feel I don't have your audio right now. Just make sure that you are unmuted.
Oh, let me unmute you here.
Yeah, you're self muted right now. Phil.
There we go. There I go. You got it. You got a technology hurdle to overcome. Thank you so much. All right. So let me share my screen here.
And thank you very much for that introduction. And hello everyone. Welcome to the session this morning. I'm delighted to be able to speak to you today. So the topic of my session is BPM re-imagined, The Impact of Emerging Technology on Business Automation. So, we're going to take a technological journey, looking back at BPM as it originally started out, and then what are the, the really interesting, new, emerging technologies that are coming in and starting to impact the way that the BPM is used in practice.
So, let's get started. I thought I'd start off with is just a quick refresher on BPM as it as it started out, or at least as it as it began to mature. I found this diagram on Wikipedia. It was published in 2007, and it's an illustration of the main components of a business process management system and it's not vendor specific. So this isn't about a particular vendor's product, but essentially this was trying to lay out what are the key components in BPM. What should you expect to get if you, if you purchase and deploy a BPM system. So you can see in the middle of this diagram here, we've got the green box labeled BPM Suite.
And it's divided brilliant, two paths. There is a box on the left. It's titled Workflow Application Services. This is the, what I would call, the runtime components of the BPM system. So this is the components that are actually executing business processes. So moving work through the organization. Routing work to different people making sure that the tasks are completed on time, and so forth. And then on the right, you've got the label business model law, which I would categorize the design time components, right?
These are the tools that typically business analysts and other business users would use to describe the business processes that they are looking to automate, so. And typically they would draw a flowchart. There'll be some kind of graphical modeling tool that allowed that allows you to draw a flowchart that depicts the steps in the business process, and who's responsible for each step and so on and so forth. And the general idea was that you could model the process from end to end. And in some cases, that was sufficiently, you're just looking to do modeling and then you print out these diagrams and stick them on the wall, and Sara for six months.
Or you can go on to actually automate those processes, hand those models over to the runtime site and what execute actual instances of those processes for you and move work for the organization. It's also interesting to me how much technology is around the outside of the BPM system. Right. So we're looking at databases. often relational databases solely using Oracle or SQL Server, Postgres or whatever to store the information, that, that is maintaining the state, effectively. The system you've got a document management system illustrated on. The bottom here documents, obviously, very important automated workflows, you need to be able to track all the documents that relate to a credit application, or a loan application, whatever it may be.
And then you've got all the, the internet infrastructure on the left, here, the web servers, and the load balancing, and then clustering, and so on and so forth.
So, these systems have been around in this phase, we see for quite a long time, they've been deployed, vary widely and as many of you know, that they're actually very good at what they do know, The ones that set up and once you've got the workflows and creative, and you're able to move with work through the organization. And there are tremendous value to it, to a business, that we can save a tremendous amount of manual labor and effort because we're automating our business processes.
So this was back in 2007, so around 13 years ago, so the question I asked myself then was, Well, OK, what's changed? What's changed in the last 15 or so years?
How has that impacted this technology, and how is his business process management technology today different from what it was back then? So I think the short answer is a lot, right, or a lot has changed. There are many new technologies that are starting to impact this area of business automation, but then the 35 minutes that I have, I picked three, but I think are perhaps the most significant. So the Clout festival, Clout Computing, has had a profound impact on business credit management technology and we'll talk about exactly what that is. Artificial intelligence, more recently, has become very popular and is starting to have a tremendous impact, also, on the way that we think about business automation. And then, finally, you know, almost no discussion of what's happening in BPM today with the complete, I think, without a discussion of robotic process automation, RPA, one of the hottest new technologies of the last 2, 3 years or so.
So festival, the cloud cloud computing really wasn't a thing back in 2007, 10, 15 years ago. Certainly, the internet was very prevalent live as it was widely used. But the notion of offloading enterprise workloads into the cloud really hadn't taken off of that point. So, these systems were not designed to run in the cloud. They were designed to be deployed in your data center on service that your IT department is provisioning the most likely be a whole rack of servers that are going to be hosting your BPM solution for your organization.
The cloud has been driven, mostly, initially, at least by economic benefits, You know, the, the, the, the, the key advantage to deploying the workload in the cloud has been the promise of cost savings, not that they always materialize. But, but fundamentally, the theory is that you can save significant amount of your IT budget by moving some of these workloads to the cloud BPM. In particular, has, some advantages are, that make, it particularly suitable, I think, for cloud deployment. And it has to do with the, the, the idea that in the cloud, you only pay for what you use. They're consuming compute resources as a utility.
When you think about it, a BPM system, even if it's heavily used and maybe running thousands of processes, typically a BPM system is doing nothing at all.
It's waiting for something to happen. The processes that is running are typically long running processes that might take weeks to complete. So even if there are thousands of them active at any one time, in computer time terms, there's usually a very little going on on a BPM system. It's sitting there waiting for something to happen.
So, the notion that, paying for usage, it gives you a great advantage with, with. With BPM, it's very suitable, because you're, in theory going to cut down, your, your costs vary considerably by just paying for the time that you're actually using the system. and not enough for the idle time. So, economics, I think, drove the cloud to begin with.
And a lot of good reasons why we companies started to see the value there and start to think about moving the BPM and workflow workloads out onto the cloud. Don't think the comparable with accessibility, it made it much easier for users to be able to access these systems, with your business analysts, looking to create a workflow, created a process model or a user looking to interact with an operating process. Current makes it so much easier.
Regardless of where you are geographically, you can get to those applications and using, without having to install anything on your, on your local turn on and so forth, So there's considerable value there.
But there are some significant implications of moving workloads to the Cloud.
Essentially, what we, what we learned is that you can't just take a system that's running in your data center and plunk it in the cloud, and expected to work exactly the same way. The cloud really encourages new application architecture, new ways of putting together the applications that work effectively and scale well, in the cloud environment. And also, as a result, it encourages new approaches to application development. There are new ways of creating these applications that involve different types of technology that really necessary if you're going to take full advantage of all the cloud has to offer. And so, these two things combined have an impact on the BPM, or on BPM technology if we take a look up.
Firstly, the architecture, the cloud encourages what we're, what we like to refer to as a microservices architecture. And then, this diagram.
The, the traditional business enterprise workloads of 15 years ago was, what we might call a monolithic approach to. An application architecture is typically a multi-tiered system, as shown on the left here. When you got a user interface of business logic, you've got databases. And everything is bound together and deployed as a single unit. This is often what we refer to as a monolithic application.
The downside of these, this type of approaches that these applications tend to be very large, very difficult to understand. And, very difficult to change, You have to a minor change requires you to redeploy the entire application. Which can be a very significant process.
Microservices, approach really takes the, the, the approach that we're gonna break down that application into smaller, more manageable services that are independently deployed. And each one can be modified independently of the others, so that we can replace parts of this application at any one time. And we can also make in different groups, different departments within our organization, responsible for individual microservices. So we can kind of democratize this application a little bit more in terms of how it's put together.
Microservices de facto architecture now, for cloud based applications, that works extremely well. In the cloud, We can deploy an app that consists of multiple micro services, And then, each one can be scaled, independently, maintained independently. And it makes optimum the use of the resources we have in the cloud, so, microservices architecture. So, this is one of the consequences of cloud computing. The other is the way that we approach application development.
Kind of illustrated here, again, on the left, going back to 2000, 2005, 2000, you know, early two thousands. You know, how do we build these? these monolithic applications. Well, we had a team of developers and our IT department using development environments to write the code that became these, these monolithic applications, and this is obviously a very simplified perspective, but essentially, this is how this application has been put together almost since the inception of IT.
What we're seeing today is a number of important changes, two of which are illustrated here, because we're building, we tend to now build microservices based applications rather than monolithic apps. We're using new technologies to do that, and a key technology that that's fairly recently introduced this notion of a container platform. The microservices are packaged into these modules, quote containers, which are then the units which get deployed into the Cloud.
A container platform is the, the underlying technology that's managing all of these different containers and managing these microservices and allowing us to scale up and scale down and replace individual containers and so forth. Container technology is a very big thing for Red Hat. We're very active in that space. And it's, we see that as the primary technology that's enabling the creation of these types of Microsoft's space applications in the cloud.
The other interesting change is the lower part of the diagram. And in terms of who is now building applications, we're seeing a democratization of application development. It's no longer purely IT developers that are building applications for the cloud. It's possible now for business people to use tools that are relevant to them, to also create microservices based on potentially models of the business, not necessarily code. So we're kind of changing a notion of what we believe source code is, it's no longer purely Java or C or c-sharp code, it can be models of your business. that can be another way of thinking about source code that can ultimately be converted into a working microservice.
So application development has been profoundly impacted by cloud technologists, and I think that the long-term impact here was this democratization of app dev and the ability of a much wider variety of users to be able to create applications.
So, going back to our BPM system, what does that mean? If we look at the, the 2007 diagram, that there are clearly issues here with how we could put this in the cloud. And, in theory, you could take this entire application and dump it in the Cloud, and they would kind of work. But we have a lot of problems with scalability. We wouldn't be able to change individual pieces, like, if I want to change the business rules engine, for example, a process model.
I can't do that without bringing the whole thing down and bringing it back up again, so it's very hard to re architect this as a microservices based application.
And it's worth also pointing out, there's a lot of code and a BPM system or a traditional BPM system that's related to functions that are have nothing to do with BPM.
You know, they quite often manage their own clustering, their own user management and authentication, and authorization. There's a lot of basic capability that's coded into these systems and represents, in many cases, more than 50% of the total code that that comprises that system.
All of this can now be managed by the cloud. There's no need to include that in the application. So, there's a lot of reasons you have to think about.
You know, how, how would we architect this now, hardly put this together for a cloud native world, and then trying to answer that question. I've looked to the open source community, which is very active and cloud computing. And I want to show you what an illustration from a really, really interesting open-source project.
I pulled this directly from the project's website, this is a project called ..., is an open-source project that is looking to re-imagine business process management systems for the Cloud. And this illustration here kind of shows you where, where they're going. That they've broken down that big monolith BPM system into separate microservices that are communicating with each other. Using an open source messaging system, called Kafka, or very widely used messaging solution now for for cloud applications. And you can see that creating basically a loosely coupled arrangement of different services that can each be deployed separately and scaled independently for the cloud.
On the right here, Kubernetes is the, the container platform. So I mentioned earlier that this container technology is really taking off in the cloud. Kubernetes is an open source container management solution. started out at Google. That's now become one of the de facto container platforms. And so building this on top of Kubernetes leverages on that, all those capabilities, There's a lot more information on cogito that I could give you. But interest of time, I just wanted to show you this. This one diagram that shows a little bit about how folks are re-imagining the runtime side of the BPM system. So, so, this is very much how you execute those business processes. When we think about the other part of, that earlier diagram, the, the modeling side, That, design time aside, it's worth pointing out. The BPM modeling tools fit extremely well in that, that illustration of how modern applications are developed.
It's actually almost custom built for BPM. Though, this notion that we could take the modeling tools that we've already been using in BPM systems, and re really imagine them in this new cloud native well, as tools that create source code that can be converted into applications. And that's really illustrative of, I think one of the other profound changes in BPM over the last few years is there's the BPM technology is repositioning as application development. Many of the vendors in this space are. now, positioning themselves as app dev companies.
They're not so much business process management companies, but more where we are enabling a new new range of develop as a new, a new range of business people to be able to build applications using the tools that we've had for the last 20 years. And the BPM system that we're re-imagining and in this way as app dev platforms for microservices. So really profound impacts, I think, in terms of how the cloud has changed the world of BPM.
Um, couple of the two technologies that we're going to talk about fairly briefly, let's talk about artificial intelligence. I mean, that's, that's something that's on everyone's mind right now, it's really risen to prominence in the last couple of years. Obviously, this technology is not new. It is also has a long history, but it's really the last couple of years that we've seen commercial interest on the scale that we're seeing now. In an artificial intelligence And machine learning, particular machine learning, as many of, you know, is a way of automating decisions. So, it's typically.
The way that you deploy machine learning solution is illustrated, somewhat oversimplified here on this diagram, but think of it as a way of creating the business rules that govern how a decision is made automatically. So, it's, business rules are right themselves. That, that's, that's often how I like to think about machine learning. The basic idea is that you, you start with a large amount of data.
And in the business world, it can often be historical data, that these are, for example, credit applications and the results of those credit applications, perhaps going back over the last five years. So, if I'm a bank, and I'm looking to automate how I decide whether, how I did I decide on a credit application, whether I'm going to offer alone or not, I, would start with, say, five years worth of information, historical data to these the applications that we receive. And these are the results of those applications. We put that through a model training process, and create a predictive model, and that predictive model that, there are multiple ways of creating those models, multiple formats, different neural networks, and all the other types of formats.
But, fundamentally, you end up with a mechanism, that you can now feed new data into that same model, and have it make the decision based on what it learned from the training data.
This is essentially a very high level, very straightforward, which is simply perpetuating decisions, the methodology that we use to make decisions in the past and when we're encapsulating that methodology, if you like in a predictive model. On the, on the right side, what we call model serving. That's the process by which I believe we deploy a model and then run new data against it, in order to make some kind of a decision.
Obviously, way more complex than that, but that's a very fundamental level, what we're referring to, machine learning.
Know, I was thinking about, Well, how does that affect BPM? What, how are we seeing this actually deployed in practice? And three use cases come to mind, what we call next best action. You know, that that's a technique where we're using a predictive model to inform a BPM system about what the next step in a process should be. So, that's in itself quite a profound change from the old the BPM of 15 years ago, where the entire model was described in advance. So, that every step was simply described by the model moving to a more dynamic mode, where we're making determinations about what the next step should be based on information that we have at the time machine. Learning is tremendously useful in that regard, because it can take the historical information that logged by a BPM system.
As training data? So we can learn from what happened in the past, when we got to this point in the process, with this type of information, what typically, it was the next actually taken. And used that, basically, to create a predictive model that says, well, in this case, it looks like you should do this next.
And we're seeing that fairly commonly now. Next best action is a, a use of machine learning techniques and business process management. The other area where I see it being deployed is task assignment, right.
So one of the goals of a BPM system is to assign work to the right individual within an organization. When we, when we have work that needs to be performed by a human, rather than automatically, we're going to have to select from amongst pool of resources, who should, who would be the best person to, you, execute this particular task? There's various ways that we can use machine learning in that scenario to make that prediction, to decide, based on the information that's coming in. And, again, on historical data or historical performance data, to be the best person for that particular tasks, are seeing and predictive models. Beginning to be used in that way as a way of making better decisions about about assigning work to individuals. But I think probably by far, the primary use case that we're seeing is using machine learning to simply automate the business decisions that occur within the, the ...
normal operation of business process.
So, when making a loan decision, and we wanted to decide whether we want to make, we want to offer a loan to this applicant, or not. That's something that BPM has been doing for many, many years, typically through the use of Business rules engine. So, typically, an analyst will specify the rules directly. These are the underwriting rules, and these are the rules that we use to determine who qualifies for credit. We're seeing increasingly, is now machine learning to augment that process and to, to kind of, mixed the machine learning model with the business rules.
An important technique for doing that is .... I mentioned ... earlier, ... is a decision modeling language that now we're seeing quite a lot of uptake with going beyond the old idea of business rules to a really graphical way of representing the rules behind making a decision making and operational business decision.
I don't have a lot of time to go through ... in any detail, but the key thing I wanted to point out is the .... standard itself includes, the ability to add a predictive model, to a decision. So, you can have a node, within a ... diagram here that actually references machine, learns predictive model. And so we can take the output of a machine learning model mixed up with business rules, in order to come up with a final decision.
As part of the operation of a business process, by enshrining predictive models, and in a standard for making decisions, that we're really seeing, you know, again, new ways of leveraging this machine learning technology, and increasing the amount of automation that we can apply to business decisions.
And we can put that together with that same diagram. and really think about that as another way of creating applications, right? So predictive models ultimately become microservices in our cloud, and really, it's just another route to building an application. And so, it's worth kind of thinking about all these different technologies as how they contribute ultimately, to building applications that automate business processes.
Um, couple of very quick notes on AI. However, that it is worth pointing out, you don't have to be careful with AI, it's not a magic bullet. Remember, predictive models are only as good as the training data. It's very hard to detect bias and that training data, if your training data is not sufficiently representative of the way that you make decisions than the model itself, will not be represented at the way and make decisions. And that can lead to all kinds of issues that are very hard to detect. Also, decisions made by predictive models are very hard to explain. There's a tremendous amount of research going on right now into how we can make predictive models, explain how they got to a particular decision. But if you're making a credit decision, for example, typically, a bank is required to be able to explain why they denied credit.
If they are unable to accept an application, a predictive model is ending with the current technology, it's very hard to figure out why your credit application ended up in the deny bucket. Another good reason why you want to mix a predictive model with the business rules, so that you can see what rules are actually being applied in order to come up with an explanation.
Finally, and very quickly, I mentioned robotic process automation, one of the really hottest new technologies, or the last for the last couple of years. Or so. we're seeing very widespread deployment of software robots.
So RPA is as essentially a technique that allows non engineering, non IT folks, to create what are called software robots, which emulate human interaction with with a, with an IT system in order to automate, typically very repetitive labor intensive work. So where folks, for example, might as part of their regular job, need to, say, copy and paste information from one window to another, move information from a spreadsheet to a database, that kind of thing. The idea behind RPA is you can just train a robot to do that, and it will carry out that work automatically and relieve the, the, the human of all that repetitive labor.
This area has exploded over the last couple of years. We're seeing thousands and thousands of robots being deployed across IT. Extremely popular, very high valuations on RPA companies. So, and clearly, you know, immediate ROI, that being able to automate human tasks, saves you having having to do that work, and then there's an immediate realization of ROI. And really, as what's driven RPA. RPA is complimentary to BPM. The way I look at it, you know, there are two ways of kind of interacting between RPA and BPM.
Robots are very good at doing manual what otherwise would have been manual tasks, so that the obvious way of utilizing RPA from a BPM perspective is simply to consider a robot as a resource that executes a task. And so we can route work to robots just as we would wrap up to people. And most BPM systems today have the ability to call out to an RPA system and invoke a robot to carry out a task. That's probably the most logical way of thinking about RPA.
In terms of, in the context of BPM, there's also the aspect that, you know, RPA bots as part of there, part of the work that they're doing, they may need to invoke a BPM process, They may need to kick off a process. So there's this notion that RPA system can also call into the BPM solution. In order to kick kick off a process, maybe processor, blown application or whatever they may be working on, some task, that in half, is interacting with the user that, ultimately, results. And then, new processes being being set up on the BPM system. So typically, you have this kind of two-way interaction. The BPM system can invoke robots, and vice versa in order to more completely automate the business. And this really is the ultimate value of bringing these two technologies together that we're able to increase the level of automation, and do more of the business without necessarily human intervention.
RPA also, there are some notes around things to be careful with around RPA. Know what we see with RPA, what the benefits are RP is that is extremely easy to build a robot. Anyone can build a robot. That's also a disadvantage. It results in many, many robots suddenly being deployed around your IT environment. My general rule is, as think about RPA as a technology to automate human work. The, that, we see a lot of, what I would call the misuse of RPA, where it's being used simply to patch holes in IT systems. Generally, if there's a problem with accessing data from one system via another, that's not a task for a robot. That's something you need to look at an integration solution to, to really approach fixing those holes.
So one way of limiting that, the spread of these robots is to really think about them as useful primarily for automating human work, and remember that their software, just like every other item of software, we're talking reward. Is an application that needs to be governed like an application? It's, it's no different still, need version, control, automated testing, and, and, and so forth, and be wearing that.
Were also the security implications of robots are very high levels of access to your enterprise data. Simply operating on that data on attended to the there are no security attack surfaces that are expressed A need to be aware of. So finally, RPA is another way of creating ultimately applications and kinda fits into this model. And this really is kind of, know, that high level picture of where things have come from that earlier picture of the business process management system. Very much focus now on deploying microservices based automation applications in the cloud, using all of these technologies. And, and if I had more time, we could add even more technologists to this list and look at how those of impacted business automation.
But, really, I think that the message is BPM itself is very much alive in this new world, and it's providing very much in the key technology that helps modeling, and also, on the backend, the automation of those automated processes.
Wanted to leave you very briefly, with that little picture of where Red Hat fits into this. I know at ETS, and some of the Red Hat products that you can utilize to kind of start to build out this cloud based business automation solution. Our Cloud Platform is called OpenShift. It's based on Kubernetes, the Open Source project. Container management product, I mentioned cogito there, I put the ... logo up there, that's not currently available as a product from Red Hat, but we're looking at very closely is a next gen business automation solution. On the modeling side, you know, we offered a protocol, that Decision Manager for decision modeling, which which supports the ...
standard, Process Automation Manager is our business process management solution, uses primarily BPM and the business process modeling notation in the flowchart and notation business processes. And we have a very large number of products for developers, and pride in one here, which is a cloud based application development platform runs entirely in the cloud and an app dev solution for developers. We don't operate directly and machine learning or RPA. But we do maintain partnerships, We have an ecosystem of partners that we work very closely with there, that help us put this this whole picture together.
So, that's, that's really where red hot stones and I hope, I hope this this has been interesting and useful. And, you know, that's that's our view on BPM today and how far we've come in the last 15 or 20 years or so.
So, um, thank you very much.
Thank you very much for listening, and she's very happy to take any questions right now.
Thank you so much. That was terrific. What a what a masterclass on Technologies and and what's going on in the in the in the world of a BPM and technologist for IBM. I know that you're looking there on that. Yeah, there you go. You got it. You got a field. Level control. So, Terrific. So, I'm going to ask the audience to keep providing questions as we go for our segment here. This can be both exciting and intimidating because there's just so much. And I know that there's so much depth to each one of these topics that, that then you cover today. And the, and there's just so much you can cover in the time that, that we have here, and you did a masterful job in providing the pros and cons of the different technologies. I want to ask a very practical question.
For an organization that's coming in and saying that, Hey, I want to apply some of the latest technologies to my I BPM, not because, you know, it's a cool technology, it's because it can create a lot of value for my organization.
Know, from your practice, if you put your practitioners hat on, what has been your experience on the menu of technologies that are available right now, Which ones would you rank higher in? Creating the most value early on in the, in companies that are just starting to adopt some of these, some of these technologies. Yeah, and my experience and, you know, speaking from the Red Hat's perspective, most of our customers begin this journey with the cloud.
That's, you know, it's really about benefits, not just BPM that benefits your whole IT infrastructure, and all kinds of workloads. You know, we focus on what we call a hybrid cloud, which is enabling you to take your IT workloads and deploy them in a public cloud, like Amazon, or Google, or a private cloud in your own data center. And creating, effectively, one big, giant computer, out of all the competing resources that are available to you. Whether they be on premise or in the public cloud. And that's what we would call a hybrid cloud. And that gives you the flexibility, then to decide where it's most cost effective to run each workload.
And the ability to move workloads around from from one location to another very easily. And it's the, the adoption of container technology in the container management platforms that really enable that. If you're using Kubernetes, the open source project, or OpenShift Red Hat Enterprise version of Kubernetes. It creates a level playing field. It makes all these different cloud resources. Look the same. So, I can move my workload from Google to Amazon, to Azure as well. I can bring it on premise. I can, I can spike in the cloud, If I have a sudden increase in workload. I can, I can move some of that into cloud based resources temporarily. That's kind of the foundation on which our modern IT environment is built.
And then then you can kind of move on to think about BPM specifically, and, you know, cloud native way of automating your business and business processes. And all these technologies they talked about ultimately can be deployed in that way. And on this, there's a modern, container based platforms. And so, that's where it starts. It starts, I think, with, with clouds and containers, And then you, you, you, you then have the basis on which you can kind of move forward with other types of workloads.
Terrific, terrific, I'm going to pick up on some questions here that the audience is sending our way. And the and the and keep, keep sending those questions because I'm seeing them, and I'll capture as many of them as possible. In our allotted time Here, there is Gopalakrishnan is asking about.
How does a process of discovery tool like salone, nice And I'm not sure if everybody goes along it or not, but salon is the tool that talks about how the process discovery tools fit into the framework you shared and the, whether they use machine learning for next best action.
Yeah, We didn't talk about process mining, or process discovery. I, I would broadly lump them into the category of modeling tools, right? So, I'm modeling tool is something you use to create a model that can then be, or can then automate, can be automated. So, a model of a business process or a business decision. That tool can be something that is, simply allows a user to directly specify what that model is. Or it can be an analytical tool that's looking at your enterprise data, and then the logs from the various systems.
And kind of inferring automatically what your business processes are. And that's the whole area of process mining, being able to automatically derive models of business processes, that you didn't even realize you had, based on, essentially, the logs of your enterprise IT systems, and looking at what they're doing. And in what sequence, and inferring what you or your processes must be. So, it's another way of creating a model and it fits into that category of modeling tools. You could think about that being the broadest.
possible based interpretation of modeling tools are tools that you either have people creating models or that are using AI as in the case of some process mining tools to build those models. So on my diagram, that's where I would fit that technology. I would fit it in the modeling tool section. And it's a, it's a very, also, very exciting new technology. The again, promises to enable us to automate more of our business, because we're able to discover processes, we didn't really know we have in the first place. So that it fits a very important use case, I think. And that's how I view that type of technology.
Very good, very good. I think we have time for maybe 1, 1 more question here, so the one that's coming up at about low code platforms, What about those local platforms, how they fit in the picture with RPA BPM? How does that play out? Very good Question. And that would have been my fourth bullet point, if I had to have had the time to do for technologies are, low code would have been next on the list, So Loco technologies were the designers, essentially, for business, people, write their design, for non non traditional programmers to create applications. And so they, could, they kind of fit alongside these other technologies. It's another way of building an application. The.
The thing about low code is, we do have to be a little bit careful. in terms of how we use that. It's a little bit like RPA, in the sense that you, you can, you can get uncontrolled creation of these apps.
And you end up with applications, deployed all of your infrastructure, that you really have proper governance and control over. The one thing I would say about low code today is that it does tend to operate in little bit of a silo.
It's great for business people who want to create apps very quickly, but it, it doesn't interact with the other participants in the application development process. So IT generally isn't a separate silo. And so we did that. We're not having different silos or application development with an organization. What we're trying to get to in the, our view of the world is bringing everyone together at this. The central point of this contain a point so that we're using the same governance mechanisms across all these different approaches for application development. So low code is absolutely. Again, it's a hot technology, as it produces an ROI very quickly. It's very like RPA, and there are certain caveats, you have to be kind of careful about how it's deployed. And how you govern the applications that are produced by low code, but very relevant nonetheless.
Tale, thank you again for a masterclass on on the all these technologies and how they work with IPP. I'm very grateful to have you here. Thank you for sharing your wisdom with us today. Thank you. Thank you for the opportunity.
All right, ladies and gentlemen, we add this, completes our session, and I asked you that, as you close the session, there will be up. You can click on the popup to fill out a short survey and provide feedback on why you, on your experience has been. And I hope to see at the top of the hour, because we have a very special session with Oliver Michael, who is the partner and vice-president Assignments. And he's gonna share with us how they spit up their proposal phase is by 300% using an intelligent Tender Documents Screening approach. And so, how I BPM works with some of their critical business processes, and automation, and from our true practitioner, and a great global organization. So, look forward to seeing you at the top of the hour, and I'll close the session out. Thank you.
Sr. Principal Product Marketing Manager,
Phil Simpson is a Sr. Principal Product Marketing Manager at Red Hat, responsible for go-to-market strategy and positioning for Red Hat’s business rules and business process management products.
Phil has extensive experience with business rules and BPM, having held senior product management roles at an early business-rules pioneer and senior marketing roles at several leading enterprise technology companies.
Prior to joining Red Hat, Phil was a product manager for the data analytics firm Renesys and was a director at SeaChange International, Ironhead Analytics, and Rulespower.
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