DecisionCamp 2019, Decision Manager, AI, and the Future

A few days ago my fellow Red Hatters Mario Fusco, Matteo Mortari, Mark Proctor, Donato Marrazzo and myself (Edson Tirelli) had the opportunity to attend Decision Camp 2019. Following the tradition from previous years, this is a conference focused on Decision Management and related topics, with an emphasis on practitioners, vendors and users of the technology. In other words, a 3-day conference that packs a lot of content, mostly technical and strategic.

This year in particular the agenda was packed full of interesting and relevant topics, ranging from human centric topics (like coordination of collaborative decisions), to compelling use cases (like airport gate scheduling), to glimpses of what is coming on the DMN standard (like temporal reasoning and discussions about DMN 2.0).

Red Hat delivered two presentations: 

  • Decision Management + Machine Learning: a standards based approach : Matteo Mortari and myself presented how the purposeful use of the triple crown standards (CMMN, BPMN and DMN) in combination with PMML (Predictive Model Markup Language) enables businesses to leverage machine learning to automate complex decisions in a vendor neutral and effective solution, while promoting transparency and simplifying explainability. 
  • How and Why I Turned a Rule Engine into a First-Class Serverless Component : Mario Fusco and Matteo Mortari presented how the use of the latest advancements in JVM, cloud and containerization technologies made it possible to re-architect the Drools project into a cloud-native, first-class serverless component.

You can find a good review of all the presentations on Sandy Kemsley’s blog. All the slide decks are also available for download from the Decision Camp website.

Trends in Enterprise AI and Digital Decisions

One of the highlights of the conference was the keynote presentation by Mike Gualtieri from Forrester, on “Trends in Enterprise AI and Digital Decisions”.

During the presentation he touched on several subjects, past, present and future, but the message was clear: effective Decision Automation (or Digital Decision, Business AI, or any name you choose to use) is not a one-trick solution. It requires a number of technologies to be combined in order to truly deal with today’s enterprise challenges. 

AI is a hot term in the market right now, but Machine Learning (ML) without the framework of Digital Decisioning (DD, also called Decision Management) and the optimizations from a Constraint Solver (that he called Mathematical Optimization (MO) engine), is too unpredictable and opaque to be effective. In his words: AI = ML + MO + DD.

His argument is that AI is not only a requirement, but inevitable, for any company looking to become a leader in their industry. He does caveat that statement by explaining he is referring to Pragmatic AI (or Applied AI), that is focused on solving specific problems, and not the Pure AI that we sometimes see on Science Fiction movies.

He continued then explaining that although Pragmatic AI (in the context of Machine Learning)  is a game changer, it is essentially a model, with probabilistic predictions as outputs and the quality of the result depends on both the model as well as the quality of the data used for training. Being essentially probabilistic, in rare occasions it can also produce outlier results that make no sense for the business, or, more often, answers that are sub-optimal.

That is where the combination with Mathematical Optimizations (MO) and Digital Decision (DD) technologies can hugely improve the success of these solutions. Machine Optimizations can be used to constrain or improve the results generated by ML predictive models, eliminating those outliers and improving the sub-optimal decisions. Digital Decisioning can leverage those solutions for explainable automated decisions, while keeping humans embedded on the process as DD is effectively a translation of human knowledge into executable computer models.

Lots of insights and food for thought, but also happy to see that his advice and observations match to some degree what we’ve been doing with the Red Hat Business Automation platform.

Red Hat Business Automation ships with a Mathematical Optimization engine (Optaplanner) and a Decision Engine (Drools) out of the box. Optaplanner leverages business rules for the specification of constraint and scoring rules, and can as well be used for decision optimization and resource planning. The big news we live demoed at Decision Camp is that starting with version 7.5, users can now transparently integrate Machine Learning models through PMML (Predictive Model Markup Language), without any glue code, directly into their DMN (Decision Model and Notation) models. That level of seamless integration is critical to reduce time to market, transparency and efficiency of automated decisions.

All in all, a great conference! Thank you to the organizers for putting together such a strong line up of content.

Business Rules Re-Imagined

The Impact of Cloud, AI/ML and RPA and on Decision Management

Decision making is a key component of today’s business applications. Complex business applications must be able to make decisions following the same rules a human would follow when making those same decisions.

Because business applications are so critical –  and because they need to incorporate business know – how into the applications to support accurate decision making – the building of these applications is no longer left solely to IT developers. Software development is seeing increasing involvement from the business side. 

For example, in the past an insurance company would write an application that records insurance claims. Today, IT is writing applications to sell insurance. That is a huge change. In computer science class, developers are not taught how to sell insurance. And it is not like the insurance company lacks that expertise anyway. They have many people who know how to sell insurance, but none of them work for IT. So more businesses are realizing that they need to involve the business stakeholders in the software development process, and incorporate more business knowledge directly into these applications. 

Clearly, the business stakeholder are not developers. They are not going to write code, but they can produce models of their business, based on the business rules, the processes, the policies, and the decisions they make while conducting company business. These models can be thought of as the source code that will be deployed within the business apps. 

How does IT empower the business user to encode as much of their knowledge as possible so they can add value to the applications the organization depends on? Development teams should utilize business rules rather than simply encoding business logic into applications. When business logic is built into the application – which is the traditional way of developing business applications – the business stakeholders have no visibility. It is hard for them to see what specific policies are being applied; what the rules are; and why a decision is made. 

The solution is to outsource the decision making to a business rules engine, which makes decisions on behalf of the application, based on a set of rules written in English so the business side can understand. Business can gain much more control over the policies that are applied automatically by these applications.

A business rules engine provides multiple advantages, including:

  • Separation of the business rules from the applications
  • Visibility for business stakeholders
  • Business rules expressed in terms that the business can really understand
  • Enabling business and IT experts to collaborate more effectively
  • The ability to change rules easily and quickly
  • Consistency of rules and decision-making

A range of Red Hat customers across various industries are having great success using a rules engine to provide decision services to the organization and keeping business rules separate from application code.

A lot has changed in IT over the past few years that has impacted automated business decision making. In this blog, I will cover three of the major changes that have caused the greatest impact: Cloud, Artificial Intelligence (AI)/Machine Learning (ML), and Robotic Process Automation (RPA).

The Cloud

The cloud has dramatically altered the way we create, deploy, monitor and manage business applications, and that has a tremendous impact on people using business rules engines.

Since the start of IT, organizations have built “Monolithic” applications, which are large, difficult to understand, and challenging and time-consuming to make a change. In the last few years, thanks to the cloud, monolithic apps have been replaced by containers, and microservices architecture, making it easier to create, deploy, manage and change parts of applications. A large app can be broken into smaller components that can be developed, scaled, managed and changed independently from the other components.

The flexibility of containers and microservices simplifies decision-making because rules can be added as components rather than being embedded in the application. In the cloud, rules can modified easily in response to changes in the market or in the way the company does business, and the entire application does not have to be rewritten. This provides businesses with a level of agility they never had before.

Artificial Intelligence (AI)/Machine Learning (ML)

Another set of emerging technologies that is having a major impact on business applications is Artificial Intelligence (AI) and Machine Learning (ML). 

One way of defining ML is “rules that write themselves.” This is obviously a huge leap from where we were 10 years ago. Instead of creating the rules based on your experience and building an app based on those rules, you can now look at historical data, figure out what rules produced those results, and then apply that knowledge to make decisions going forward.

Essentially ML is used for constructing predictive models. For example, with regard to the rules covering when insurance claims are denied or paid, you have data about how you arrived at decisions in particular cases. You could use ML to build predictive models to repeat those decisions in similar cases. 

How do predictive models compare with user written rules in terms of their usefulness and viability for these types of decision applications

  • Rules are created by people, while predictive models are automatically created based on analysis of historical data
  • Rules produce results that are explainable, while predictive models produce results that are not explainable.
  • Rules are subject to human error. One of the challenges with rule-based systems is you get gaps – such as situations you forgot about, or edge cases you did not address. Conversely, predictive models are subject to historical bias. You are limited to reproducing behavior apparent from the training data. So if the data is biased, you get a biased model. 
  • Rules take a significant amount of time to produce, while it is relatively quick to generate a predictive model once you have the data.

The goal when using ML to augment decision making is to combine the advantages of applied rules and predictive models. New rule languages, like Decision Model and Notation (DMN), make this possible by simplifying the process of creating rules.

In the past, rules were created in a complex notation. Thousands of rules leave open many possibilities for error. DMN is a graphical language for encoding rules that make a decision. It makes it much easier for business stakeholders to create the source code for their decision applications. They create a graph to encode complex business logic. Then they can incorporate a predictive model into the DMN diagram, and they can incorporate business rules as well, effectively combing the best of both options

Robotic Process Automation

Robotic Process Automation (RPA) is an exciting, fast moving space right now. With RPA, software robots are developed to perform routine, repetitive work that would otherwise be done by a human worker. The advantages of RPA are all about reducing cost and headcount by automating tasks.

Time spent copying information from a back office database into a spreadsheet, and moving data around from system to system – this is not the type of work where a human worker adds value to the process, but it still has to be done. RPA allows an organization to automate those repetitive tasks. 

One of the best advantages of RPA is that it enables you to automate work without having to change your underlying systems. You can keep your legacy mainframe and other applications exactly as they are. The robot pretends to be a human and carries out the same tasks exactly the same way a human worker would. 

But it is very important to consider RPA as just another software development approach. In future, as RPA evolves, I would expect to see containerized robots roaming around your hybrid cloud. But for now, RPA is just another app and it needs to be managed just like any other app. Version control and QA are very important. 

When you create a bot, beware of the attack surface. You create an opportunity for someone to add a few lines of script to that bot. Think about the damage that could be inflicted by an automated bot with high level access to all your enterprise data. That is a key reason why RPA should be subject to the same governance as any other software.

Also it is very important to understand that the value of RPA is in the ability to automate human work, not to patch holes in IT systems. If you find yourself building bots to fix holes in IT, you really need to take a good look at the infrastructure instead. It is not productive to use bots as band aids, because the bots themselves will continuously break as things change within the infrastructure. It is more productive to focus on fixing the source of the issue in the underlying infrastructure.

For RPA to remain relevant and continue to support software development, bots should be compatible with the cloud, and be able to run in containerized environments. This is technology we expect to see in the next few years or so.

The Red Hat Solution

Red Hat is very active in the software development space and offers a range of tools designed to solve the challenges associated with incorporating rules and decision making into business applications:

  • Red Hat Decision Manager is a platform for developing containerized microservices and applications that automate business decisions. Decision Manager includes business rules management, complex event processing, and support for building DMN models.
  • Red Hat Process Automation Manager is a platform for developing containerized microservices and applications that automate both business decisions and processes. Process Automation Manager includes business process management (BPM), business rules management (BRM), and business resource optimization and complex event processing (CEP) technologies. It also includes a user experience platform to create engaging user interfaces for process and decision services with minimal coding. 
  • For development in the Cloud, Red Hat OpenShift is an enterprise-ready Kubernetes container platform with full-stack automated operations to manage hybrid cloud and multicloud deployments 
  • Red Hat Runtimes is a set of products, tools, and components for developing and maintaining cloud-native applications. It offers lightweight runtimes and frameworks for highly-distributed cloud architectures, such as microservices. 

Business Process Management Reimagined – New Services for Application Developers

You may be familiar with Business Process Management (BPM).  It is a discipline in which people use various methods to discover, model, analyze, measure, improve, optimize, and automate business processes. Today BPM, or more specifically the technology that supports BPM, is widely used in organizations large and small to automate business operations. The story of BPM is long, with roots going back to the early 1990’s, and it has constantly reinvented itself to meet the evolving needs of enterprises.  Once focused on driving efficiencies into back-office functions, BPM platforms have evolved into essential tools for enterprises looking to digitally transform operations, and to deliver a personalized customer experience that’s integrated across points of interaction.  

BPM in the Application Environment

The focus on digital transformation has led to the modern role of BPM solutions in application development, and to the rearchitecting of the old monolithic BPMS as a set of middleware services that developers can easily incorporate into applications.  Now often referred to as a Digital Application Platform (DAP), the BPMS has become part of the application environment – a catalog of components that can be included in applications requiring process management, decision management or optimization capabilities.  Now, for example, when building an application that requires, say, to make a determination of whether an insurance application complies with underwriting rules, a developer can quickly locate the corresponding decision service within their app environment and include it in their application.  Conversely, it’s the new DAP solutions that enable such application services to be quickly created from models provided by the business. Business friendly tools support the creation of a range of model types – from decision models, built with the new graphical Decision Model & Notation (DMN) standard, to models of entire business processes constructed in Business Process Model & Notation (BPMN).  DAP technology today is truly making it possible for business users to contribute to application development alongside developers.

Cloud-Native Digital Automation

The advantages of modern digital automation middleware are not limited to application development, however.  Once built, those new applications must be deployed on a variety of cloud platforms, they must scale automatically to meet varying demands, they must be secure, and they must be easy to replace or upgrade without impacting the user experience.  At Red Hat, the application environment, in which DAP services are included, is designed from the ground up to be cloud-native. DAP services are deployed in containers, and managed by Kubernetes, to provide the scalability and resiliency that enterprises need.

Digital automation today is an essential part of modern applications, and its importance is only likely to increase as we look to the future role of the Digital Automation Platform as the logical home for emerging technologies like Robotic Process Automation (RPA), and artificial intelligence / machine learning.  At Red Hat, we are focused on growing our application environment to support this widening technology landscape, so that our customers can succeed in an ever more digital world.

From BPM and business automation to digital automation platforms

The business processes that create customer value are the critical piece that links together all of the different aspects of digital transformation. But still, many of the critical activities that contribute to it are either manual or a succession of disconnected workflows or applications that prevent organizations from having an end-to-end view of how their processes deliver customer value.  

Evolving from workflows to BPM – business process management – added a whole collaborative layer and execution structure to the traditional hierarchy and project-based structure of the enterprise. When it was paired with access to the critical data and documents, alongside activity visibility and business rules, it helped to exponentially grow productivity and agility in the enterprise for many years.

Nowadays, enterprises have discovered already how to use these technologies and apply them to work with structured and unstructured processes, to create business rules to guide and support decision making, or the importance of integrating process outputs and inputs in real time to external systems that interact with the processes. These process-centric applications are even cloud-ready so you can run your processes in the cloud and open them up more securely to all of your internal and external collaborators.

But times are changing. Productivity and agility are no longer the name of the game. It is no longer enough to provide ease of use, business, and IT collaboration, or fast modification of processes and rules. Speed and support for digital transformation have become top priorities. Those process-based applications need to be quickly deployed into production, be portable, reusable and consistent across environments, and scaled in the hybrid cloud. Our customers expect cloud-native technologies at the core of their processes. They expect to run their process workloads to scale across the hybrid cloud to provide a consistent experience to their customers and collaborators. Ideally, they also want to future-proof their investments with modern technologies such as containers.

Continue reading “From BPM and business automation to digital automation platforms”

Effective Case Management within a BPM Framework

In real life, organizations have workflows which may not fit into prescribed, sequential process path or which require human intervention or approval before the entire process can be completed. Within the business process world, more unstructured and unpredictable work is handled through case management rather than process management.

There are slightly different standards defined for case management and process management, which reflect the differences in the types of process flows and data being handled in each type of model.

But the question for business architects is which standard to use or whether to try to balance both — and then for developers to try to create models on different or shared development platforms.

A Quick Comparison of CMMN and BPM for Development Standards

First, it may be helpful to explain why there is a difference between business process management and case management. Both models are defined by two separate specifications, Business Process Model and Notation (BPMN) and Case Model and Notation (CMMN), respectively.

Continue reading “Effective Case Management within a BPM Framework”

Process management and business logic for responsive cloud-native applications: Red Hat Process Automation Manager is released

Today, Red Hat announced the latest major release of its business process suite, with a new name and several major changes that pivot the focus of the product itself. Red Hat Process Automation Manager is about more than providing a business process modeler or optimizing resource allocation. This is the first generation (at Red Hat) of a digital automation platform — a hub where business users and technical developers can collaborate to create strategically-relevant, intelligent applications.

Red Hat Process Automation Manager has two core conceptual areas:

  • The first is based on decision management (the “intelligent” part of intelligent or even-driven applications). This includes the decision engine of Red Hat Decision Manager and allows automated, immediate responses to interactions, from event processing to resource optimization.
  • Second, Process Automation Manager provides the means of modeling and applying business logic within an application. In combination with a graphical UI, these creates a platform for business users to be able to design business logic in collaboration with the technical teams.

New feature: Process management + case management

The heart of a BPM platform is the “BP” — business process modeling. The previous BPM Suite supported BPMN, the notation specification for business process models, and DMN, the notation specification for data models. The assumption behind a lot of these specs is that the workflows or processes being modeled are relatively static or sequential. For certain types of business processes, that is an accurate assumption (things like resource optimization or scheduling). However, in many organizations, there are also processes which are not linear or which may follow different steps in a dynamic sequence or may be interrupted or require human intervention at certain points. These are generally defined within a related notation specification, Case Management Model Notation (CMMN).

While there are differences, there is also a lot of conceptual overlap between business processes / BPMN and case management processes / CMMN. Process Automation Manager combines the functionality of both process models and case management models within a single digital automation platform. (This is covered in more detail in the blog post here.)

Supporting both linear process / task models and dynamic or unpredictable case management models within the same platform allows developers to have a simpler development process (and, combined with other features like Process Automation Manager’s new graphical UI, makes collaboration with business users easier).

Process Automation Manager also supports other types of modeling and visualizing data and worflows:

  • Data modeling
  • Decision modeling
  • Custom data dashboards
  • Process simulations

New Feature: An easier way for business users to collaborate (graphical UI)

Previous versions of Red Hat JBoss BPM Suite were designed around business process logic, but were intended to be used by Java developers within the application development process. Beginning with this Process Automation Manager 7.0 release, there is a new Entando UI included with the platform. This provides an easier, graphical interface where business users can just drag and drop elements into their models — using ultimately the same platform that the developers are using to create the application. Business processes, rules, and logic can be written into the application essentially without having to write a single line of code.

This also effectively changes the workflow for creating event- and process-driven application. Previously, developers did all the work within their development environment. Now, business users can work in parallel (using the Process Automation Manager UI) to create artifacts which can be pulled into the developer’s IDE and code. Everything can then be packaged up and deployed in containers or other environments.

New feature: Cloud (and container) native applications

With more distributed, hybrid infrastructures, it is imperative that applications be able to function exactly the same regardless of the underlying platform. And those applications need to be designed, natively, to work in a distributed, dynamic environment so that they can be rapidly deployed, updated, or scaled.

Process Automation Manager can itself run in Red Hat OpenShift containers, in public or private clouds, on-premise, or in all environments — depending on the needs of your development and infrastructure teams. Additionally, the models and applications created using Process Automation Manager as a platform can be deployed into cloud instances, OpenShift containers, or local instances. This allows truly hybrid development, testing, and production environments.

Process Automation Manager components, applications, and models can all be exposed and accessed using REST APIs, allowing integration with other software applications or management tools.

Additional Resources

  • Dive a little deeper into process automation technology with our tech overview.
  • For general information about the Process Automation Manager, check out the datasheet.
  • There are different use cases for process automation and a business decision engine. The FAQ runs through some things to consider.
  • Get started by actually using the Process Automation Manager. Red Hat Developers has a whole “hello world” example, waiting for you.

Digital Automation Platforms: Injecting speed into application development

Red Hat has just published a new study by Carl Lehmann of the 451 Group, “Intelligent Process Automation and the Emergence of Digital Automation Platforms,” that examines the increasing importance of business automation technologies in modern business, and the ways that converged solutions (digital automation platforms) are bringing value to organizations engaged in digital transformation projects.

Carl writes that competitive advantage is enabled when an organization either does the same things as its rivals, but differently, or it does different things that are acknowledged as superior by customers. In today’s competitive markets, businesses are turning to next-generation digital automation platforms (DAP) to enable greater automation of key business functions and greater flexibility in responding to their customers’ needs.

A DAP is a set of tools and resources structured within a uniform framework to enable developers to rapidly design, prototype, develop, deploy, manage, and monitor process-oriented applications – from simple task-related workflows to dynamic unstructured collaborative activity streams and even highly structured cross-functional enterprise applications. To do so, DAPs are equipped with a range of new capabilities that go beyond those of their BPM and application development predecessors.

Continue reading “Digital Automation Platforms: Injecting speed into application development”

Why are our Application Platform Partners succeeding in Digital Transformation?

Last year we set out to start the Application Platform Partner Initiative with the objective to enable deeper collaboration with partners focused on application platform and emerging technologies. We planned to create a collaborative go-to-market strategy between Red Hat and participating partner organizations focused on optimizing the value chain for application development and integration projects.

The Application Platform Partner Initiative focuses on Application Development-related and other emerging technology offerings, which revenue increased 42% in our last fiscal year up to $624 million. Partners like the APPs are contributing to this growth and we are happy to see the momentum continuing, and their trust on Red Hat as a strategic partner. What started out as a pilot has developed into a fully fledged initiative with 28 partners across North America, who are as committed as we are to the role opens source plays at the core of digital transformation.

As part of the success of this initiative, for the first time this year, we have created the Application Platform Partner Pavilion in Red Hat Summit.  Arctiq, Crossvale, Kovarus, Levvel, Li9, Lighthouse, OSI, Shadow-Soft, VeriStor and Vizuri will join us this year in the pavilion. Don’t miss a chance to get to know the advanced solutions they have created on top of Openshift and Red Hat Middleware products, which they will be showcasing at Red Hat Summit. Check out, for example, Arctiq Value Stream Mapping (VSM), Crossvale CloudBalancer for Red Hat® OpenShift or Vizuri log aggregation solutions.

These partners are delivering a strong investment in enablement, and commitment in their go-to-market alliance with Red Hat, including co-marketing and sales collaboration. As some examples of planned activities, Arctiq is running a Modern Mobile App Development event and Crossvale an OpenShift roadshow).

Levvel has been an active participant in the APP program, doing joint webinars, customer workshops and panel discussions to promote Red Hat emerging technologies. As a result, they have influenced and closed quite a few customers and have a long list of potential opportunities. Don’t forget to attend their coming up event “App Transformation Workshop: Monoliths to Microservices”!

Shadow-Soft has been particularly focused on growing the customer base with our OpenShift and JBoss product family with innovative sales and marketing strategies that are turning into a growing pipeline of opportunities, and running events around digital transformation.

Veristor joined recently the APP program and is growing rapidly their different practices around OpenShift and Red Hat Middleware, like DevOps and Agile Consulting, Services and Software Development practice.

OSI, an international company with a long experience with JBoss, is also growing in the US and have worked on an Agile Integration demo environment focusing on JBoss Fuse Integration platform to support their customer engagements, including integration with cloud and on-premise systems. Try to attend their “Monoliths to Microservices: App Transformation Workshop” right after Summit.

Vizuri has been a Red Hat partner for over 10 years. Having delivered more than 120 JBoss-related engagements, their JBoss experience and expertise helps customers reduce risk and improve time-to-value, while avoiding project delays and unplanned downtime. You can’t miss their take on How To Manage Business Rules In A Microservices Architecture using OpenShift and JBoss BRMS.

Having recently joined the APP program, Astellent has heavily invested in enablement and marketing, while achieving exciting customer success. Read their views on the newly launched Red Hat Decision Manager 7.

Lighthouse has been helping businesses with the right mix of Red Hat’s public, on-premises, and hybrid cloud technologies, customizing them to fit their unique business needs. They have also been active with unique marketing events like the one with the Red Sox coming in May.

As you can see, APP partners are working closely with Red Hat to establish a sales, marketing, and delivery practice around Red Hat technologies, including Red Hat JBoss Middleware, Red Hat OpenShift, and Red Hat Mobile Application Platform.

In the words of John Bleuer, VP, Strategic Partners, North America, “I am thrilled that as year one of the program ends, the sophistication of our partner solutioning and delivery abilities has increased dramatically; many partners are working with us in industry and line of business (including healthcare, payments, and e-commerce); other partners are adding sophistication into the DevOps / automation practices with Openshift, Jenkins, and Ansible, while others are honing their skills delivering app modernization and integration & BPM solutions in a cloud native environment, containerized in OpenShift.  It’s an exciting time at Red Hat”.

The market is looking to digital transformation initiatives to grow and maintain competitive advantage. Challenges range from confined platforms to complex architectures, from rigid processes to lack of agility. Together with our partners, we can play a critical role to help our customers overcome those to become growing, competitive organizations.

We hope to see you at Red Hat Summit checking them out, as well as at the Red Hat Summit Ecosystem Expo!

A DevOps approach to decision management

Sometimes we would like to change the behavior of an application fast. I mean, really fast.

Traditional development cycles for enterprise applications take weeks if not months for a new version to be ready in production. Even in the world of DevOps, containers, and microservices, where we can spin up new versions of an app in days, or even hours, we need to go through development cycles that are too far away from the business users.

Welcome to the world of business rules and decision services, along with low code development.

Continue reading “A DevOps approach to decision management”

“Micro-rules,” event-driven apps, and Red Hat Decision Manager

As we described in an earlier blog, microservices are mini-applications which are devoted to a single, specific function. They are discrete (independent of other services in the architecture), polyglot with a common messaging or API interface, and they have well-defined parameters.

As application development and IT operations teams have started streamlining and speeding up their processes with methodologies like Agile and DevOps, they have increasingly begun treating IT applications as microservices. This breaks up potential bottlenecks, reduces dependencies on services used by other teams, and can help make IT infrastructure less rigid and more distributed.

One area where we are seeing this looser, more distributed approach to service development is with business rules.

“Micro-rules”

Business rules and processes in a traditional structure tend to be centralized, with the complete set of functionality defined for all workflows. The problem with centralization is because there is a single, centralized collection of business rules, any changes to one set of rules can affect many other sets, even those for different business functions.

Micro-rules essentially treat each functional set of rules as its own service — well-defined, highly focused, and independent of other rules.

Figure – Function rule sets as micro-rules

Continue reading ““Micro-rules,” event-driven apps, and Red Hat Decision Manager”