Business rules provide the flexibility and agility that systems need. By definition, they enable business analysts to adjust the system’s decision logic to ever-evolving business direction. Due to competitive pressure, business regulations, or executive direction, business rules keep adapting. The art of capturing rules requirements dictates the success of the rules implementation to follow.
Rules Requirements are Requirements
As a Product Manager at heart, I value a nicely written PRD (Product Requirement Document). Many articles provide guidance on requirements gathering, and the golden rules to adopt for success. In particular, I like Duncan Haughey’s concise take on the subject. Above all, I cannot stress enough the value of focusing on the problem rather than the solution. Engaging stake-holders or subject matter expert from the start is paramount. Kuddos to him for putting it into writing.
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A little while ago, we hosted a webinar about “why Decision Management?“. However, including the business case presentation alone seemed somewhat incomplete. Indeed, we also needed to focus on ‘how’. As a result, we created a presentation that covered both aspects.
In this blog post, I will focus on the key takeaways of the presentation. However, I encourage you to watch the video, especially if you are a manager, new to decision management technology.
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Integration with data is key to a successful decision application: Decision Management Systems (DMS) benefit from leveraging data to develop, test and optimize high value decisions.
This blog post focuses on the usage of data by the DMS for the development, testing and optimization of automated decisions.
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ETCIO (an initiative of The Economic Times) has interviewed KM Nanaiah, country manager at Equifax. The article highlights the details of the tooling that is now available to financial institutions. They will see dramatic improvements in customer acquisition and loan decisioning.
A key benefit of using a Decision Management System is to allow the life-cycle of automated decisions to be fully managed by the enterprise.
When the decision logic remains in the application code, it becomes difficult to separate access to decision logic code from the rest. For example, reading through pages of commit comments to find the ones relevant to the decision is close to impossible. And so is ensuring that only resources with the right roles can modify the logic.
Clearly, this leads to the same situation you would be in if your business data were totally immersed in the application code. You would not do that for your business data, you should not do that for your business decision logic for exactly the same reasons.
Decision Management Systems separate the decision logic from the rest of the code. Thus, you get the immense benefit of being able to update the decision logic according to the business needs. But the real benefit comes when you combine that with authentication and access control:
- you can control who has access to what decision logic asset, and for what purpose
- and you can trace who did what to which asset, when and why
Of course, a lot of what is written here applies to other systems than Decision Management Systems. But this is particularly important in this case.
Roles and access control
The very first thing to consider is how to control who has access to what in the DMS. This is access control — but note that we also use authorization as an equivalent term.
In general, one thinks of access control in terms of roles ans assets. Roles characterize how a person interacts with the assets in the system.
And the challenge is that there are many roles involved in interacting with your automated decision logic. The same physical person may fill many roles, but those are different roles: they use the decision management system in different ways. In other words, these different roles have access to different operations on different sets of decision logic assets.
Base roles and access control needs
Typically, and this is of course not the only way of splitting them, you will have roles such as the following:
The administrator role administers the system but rarely is involved in anything else. In general, IT or operations resources are those with this role.
- Decision definer
The decision definer role is a main user role: this role is responsible for managing the requirements for the automated decision and its expected business performance. Typically, business owners and business analysts are assigned this role.
- Decision implementer
The decision implementer role is the other main user role: this role designs, implements, tests and optimizes decisions. Generally, business analysts, data analysts or scientists, decision owners, and sometimes business-savvy IT resources are given this role.
- Decision tester
The decision tester role is involved in business testing of the decisions: validating they really do fit what the business needs. Usually, business analysts, data analysts and business owners fill this role.
- Life-cycle manager
The life-cycle manager role is responsible for ensuring that enterprise-compliant processes are followed as the decision logic assets go from requirements to implementation to deployment and retirement.
More advanced needs
There may be many other roles, and the key is to realize that how the enterprise does business impacts what these roles may be. For example, our company has a number of enterprise customers who have two types of decision implementer roles:
- General decision implementer: designs, implements the structure of the decision and many parts of it, tests and optimizes it
- Restricted decision implementer: designs and implements only parts of the decision — groups of rules, or models
The details on what the second role can design and implement may vary from project to project, etc.
Many other such roles may be defined: those who can modify anything but the contract between the automated decision and the application that invokes, etc.
It gets more complicated: you may also need to account for the fact that only specific roles can manage certain specific assets. For example, you may have a decision that incorporates a rate computation table that only a few resources can see, although it is part of what the system manages and executes.
Requirements for the Decision Management System
Given all this, the expectation is that the DMS support directly, or through an integration with the enterprise systems, the following:
- Role-based access control to the decision logic asset
- And ability to define custom roles to fit the needs of the enterprise and how it conducts its business
- And ability to have roles that control access to specific operations on specific decision logic assets
This can be achieved in a few ways. In general:
- If all decision assets are in a system which is also managed by the enterprise authentication and access control system: you can directly leverage it
- And if that is not the case: you delegate authentication and basic access control to the enterprise authentication and access control system, and manage the finer-grained access control in the DMS, tied to the external authentication
Of course, roles are attached to a user, and in order to guarantee that the user is the right one, you will be using an authentication system. There is a vast number of such systems in the enterprise, and they play a central role in securing the assets the enterprise deals with.
The principle is that for each user that needs to have access to your enterprise systems, you will have an entry in your authentication system. Thus, the authentication system will ensure the user is who the user claims, and apply all the policies the enterprise wants to apply: two-factor authentication, challenges, password changes, etc. Furthermore, it will also control when the user has access to the systems.
This means that all systems need to make sure a central system carries out all authentications. And this includes the Decision Management System, of course. For example:
- The DMS is only accessible through another application that does the proper authentication
- Or it delegates the authentication to the enterprise authentication system
The second approach is more common in a services world with low coupling.
Requirements for the Decision Management System
The expectation is that the DMS will:
- Delegate its authentication to the enterprise authentication and access control systems
- Or use the authentication information provided by an encapsulating service
Vendors in this space have the challenge that in the enterprise world there are many authentication systems, each with potentially more than one protocol. Just in terms of protocols, enterprises use:
- OpenID Connect
- and more
Additionally, enterprises are interested in keeping a close trace of who does what and when in the Decision Management System. Of course, using authentication and the fact that users will always operate within the context of an authenticated session largely enables them to do so.
But this is not just a question of change log: you also want to know who has been active, who has exported and imported assets, who has generated reports, who has triggered long simulations, etc.
Furthermore, there are three types of usages for these traces:
- Situational awareness: you want to know what has been done recently and why
- Exception handling: you want to be alerted if a certain role or user carries out a certain operation. For example, when somebody updates a decision in production.
- Forensics: you are looking for a particular set of operations and want to know when, who and why. For example, for compliance verification reasons.
A persisted and query-able activity stream provides support for the first type of usage. And an integration with the enterprise log management and communication management systems support the other types of usages.
Requirements for the Decision Management System
The expectation is that the DMS will:
- Provide an activity stream users can browse through and query
- And support an integration with the enterprise systems that log activity
- And provide an integration with the enterprise systems that communicate alerts
There are many more details related to these authentication, access control and trace integrations. Also, one interesting trend is the move towards taking all of these into account for the beginning as the IT infrastructure moves to the models common in the cloud, even when on-premise.
This blog is part of the Technical Series, stay tuned for more![Image Designed by security from Flaticon]
Decision Management and Business Rules Management platforms cater to the needs of business oriented roles (business analysts, business owners, etc.) involved in operational decisions. But they also need to take into account the constraints of the enterprise and its technology environment.
Among those constraints are the ones that involve integrations. This is the first series of posts exploring the requirements, approaches and trade-offs for decision management platform integrations with the enterprise eco-system.
Operational decisions do not exist in a vacuum. They
- are embedded in other systems, applications or business processes
- provide operational decisions that other systems carry out
- are core contributors to the business performance of automated systems
- are critical contributors to the business operations and must be under tight control
- must remain compliant, traced and observed
- yet must remain flexible for business-oriented roles to make frequent changes to them
Each and every one of these aspects involves more than just the decision management platform. Furthermore, more than one enterprise system provides across-application support for these. Enterprises want to use such systems because they reduce the cost and risk involved in managing applications.
For example, authentication across multiple applications is generally centralized to allow for a single point of control on who has access to them. Otherwise, each application implements its own and managing costs and risk skyrocket.
In particular, decision management platforms end up being a core part of the enterprise applications, frequently as core as databases. It may be easy and acceptable to use disconnected tools to generate reports, or write documents; but it rarely is acceptable to not manage part of core systems. In effect, there is little point in offering capabilities which cannot cleanly fit into the management processes for the enterprise; the gain made by giving business roles control of the logic is negated by the cost and risk in operating the platform.
In our customer base, most do pay attention to integrations. Which integrations are involved, and with which intensity, depends on the customer. However, it is important to realize that the success of a decision management platform for an enterprise also hinges on the quality of its integrations to its systems.
Which integrations matter?
We can group the usual integrations for decision management platforms in the following groups:
- Authentication and Access Control
- Implementation Support
- Management Audit
- Life-cycle management
- Execution Audit
- Business Performance Tracking
Authentication and access control integrations are about managing which user has access to the platform, and, beyond that, to which functionality within the platform.
Implementation support integrations are those that facilitate the identification, implementation, testing and optimization of decisions within the platform: import/export, access to data, etc.
Management audit integrations enable enterprise systems to track who has carried out which operations and when within the platform.
Life-cycle management integrations are those that support the automated or manual transitioning of decisions through their cycles: from inception to implementation and up to production and retirement.
Similarly, execution integrations enable the deployment of executable decisions within the context of the enterprise operational systems: business process platforms, micro-services platforms, event systems, etc. Frequently, these integrations also involve logging or audit systems.
Finally, performance tracking integrations are about using the enterprise reporting environment to get a business-level view of how well the decisions perform.
Typically, different types of integrations interest different roles within the enterprise. The security and risk management groups will worry about authentication, access control and audit. The IT organization will pay attention to life-cycle management and execution. Business groups will mostly focus on implementation support and performance tracking.
The upcoming series of blog posts will focus on these various integrations: their requirements, their scope, their challenges and how to approach them.
In the meantime, you can read the relevant posts in the “Best Practices” series:
Where do you start? Do you upload a predefined object model? Or do you develop it with your decision logic?
Object Model First
It is our experience that, in the vast majority of the projects, object models already exist. The IT organization defines and maintains them. This makes perfect sense, since the object model is the contract for the decision service. We need to know all features of the application before processing it. The invoking system also needs to know where to find the decision and all related parameters.
The object model, or data model, or schema, really defines the structure of the data exchanged with the decision service. Some sections and fields will play the role of input data. Some will be output. The business rules will determine or calculate those.
In our world, at Sparkling Logic, we call the object model the form. When you think about the application as data, the form represents the structure specifying what each piece of data means. For example, Customer Information is a section; and first name, last name and date of birth are fields in this section.
While business rules are based on these fields, the field definition typically belong to the system. The system will produce the transaction payload, aka the transaction data, and receive it back after the rules execute and produce the final decision.
To summarize it, the ownership of the object model lies with the IT organization, since they are responsible for making the actual service invocation.
Modifying the Object Model
Does that mean that we cannot make changes to this object model? Absolutely not. Augmenting the object model with calculations and statistics is expected. The customer info will likely include a date of birth, but your business rules will likely refer to the age of the person. It is common practice to add an Age field, that is easily calculated using a simple formula. More fields could be added in the same fashion for aggregating the total income of all co-borrowers, or for calculating the debt to income ratio.
In most systems, these calculations remain private to the decision service. As a result, the IT organization will not even know that they exist.
Quite a similar mechanism exists to add business terms to the form. In order to complement your business concepts in the form, Business terms constitute an additional lingo that is shared across project. for example, you might want to define once and for all what your cut-off values are for a senior citizen. Your business term could even specify cut-off values per state. Your rules will not have to redefine those conditions. They can simply refer to the business term directly: ‘if the applicant is a senior citizen and his family status is single’. Each project leveraging that form will reuse the same terminology without having to specify it again and again.
Like calculations, business rules can use business terms, but IT systems will not see them.
It eventually happens that variables might need to be created. That’s okay. There is no issues with introducing intermediate calculations in order to simplify your business rules. Although these fields will be visible to IT, they can be ignored. As intermediate variables, the system might not even persist these values in the database of record.
When is the Object Model provided?
It is ideal to start your decision management projects with an established object model. Uploading your data is most definitely the very first step in your project implementation. This is true regardless of whether you have actual historical data, or are building data sample for unit testing your rules as you go.
The reason you want your object model established prior to writing rules is quite simple, frankly. Each time you modify the object model, rules that depend on the affected portions of the object model (or form in our case) will need refactoring.
Granted, some changes are not destructive. If that is your case, you can absolutely keep extending your object model happily.
Some changes only move sections within the form. As long as the type of the affected fields remain the same, your rules will not need rewriting. The only exception being for the rules that use full path rather than short names. If you rule says “age < 21", you will be okay whether the age field is located. If your rule says "customer.age < 21", then you will have to modify it if age moves to a different section.
And finally some changes are quite intrusive. If you go from having one driver in the policy, to multiple drivers, all driver rules will have to account for the change in structure. You will have to decide if the age rule is applicable to all drivers, any driver in the policy, or only to the primary driver. This is where refactoring can become a burden.
The more established the object model is, the better suited you will be for writing rules.
One point I want to stress here too is that it is important for the IT team and the business analyst team to communicate and clearly set expectations on the fields of the object model. Make sure that:
- Values are clearly documented and agreed upon: CA versus California, for example
- You know which fields are used as input: if state appears in several addresses, know which one takes precedence for state requirements
Sorry for this quick tangent… This is where we see the most of ‘rules fixing’ spent!
When do Rules own the Object Model?
It is rare, but it happens. We see it mostly for green field projects. When the database of record does not exist, and there is no existing infrastructure, new projects might have the luxury of defining their own object model. When there is none, all options are on the table: have data modelers define the object model, or proceed with capturing it as you capture your business rules.
In these cases, we see the DMN standard (decision modeling and notation) leveraged more often than not. As business analysts capture their source rules in a tool like Pencil, its glossary gets assembled.
For those of you not familiar with DMN, let me summarize the approach. The decision model representation guides the business analyst through the decomposition of the decision logic. Let’s say that you want to calculate Premiums. You will need to establish the base rate, and the add-on rates. For the base rate, you will need to know details about the driver: age, risk level, and location. You will also need to know details about the car: make, model and year. Your work as a business analyst is to drill down over the layers of decisioning until you have harvested all the relevant rules.
The glossary is the collection of all the properties you encounter in this process, like age, risk level, location, model, make, year, etc. Input and output properties are named in the process. You can also organize these properties within categories. When you have completed this effort, your glossary will translate to a form, your categories to sections, your properties to fields. In this case, your harvesting covers both decision logic and object model.
Besides minor additions like computations and variables, the object model is by and large owned and provided from the start by the IT organization. Only green field projects will combine rules and data model harvesting.
A little while ago, I ran into a question in Quora that hit me in the stomach… figuratively, of course. Someone asked “why do rules engines fail to gain mass adoption?“. I had mixed feelings about it. In one hand, I am very proud of our decision management industry, and how robust and sophisticated our rules engines have become. In the other hand, I must admit that I see tons of projects not using this technology that would help them so much. I took a little time to reflect on the actual roadblocks to rules engines.
A couple of points I want to stress first
In addition to the points I make below, with a little more time to think about it, I think it boils down to evangelization. We, in the industry, have not been doing a good job educating the masses about the value of the technology, and its ease of use. We rarely get visibility up the CxO level. Business rules is never one of the top 10 challenges of executives, though it might be in disguise. We need to do a better job.
I’m signing up, with my colleagues, for an active webinar series, so that we can address one of the roadblocks to rules engines, and decision management!
Rules are so important, they are already part of platforms
The other key aspect to keep in mind is that business rules are so important in systems that they often become a de-facto component of the ecosystem. Business rules might be used under the form of BPM rules or other customization, but not called out as a usage for rules engines. Many platforms will claim they include business rules. The capability might be there, though it may not be as rich as a decision management system. Many vertical platforms like Equifax’s InterConnect platform include a full-blown decision management system though. When decision makers have to allocate the budget for technology, this becomes another of the roadblocks to rules engines, as they assume that rules are covered by the platform. Sometimes they are right, often not.
Rules in code is not a good idea
Let me stress once more that burying your rules into code or SQL procedures is not a good idea. It is one of the roadblocks to rules engines excuse we have heard probably the most. I explain down below that this is tempting for software developers to go back to their comfort zone. This is not sustainable. This is not flexible. We did that many decades ago as part of Cobol system, mostly because decision management systems did not exist back then. We suffered with maintenance of these beasts. In many occurrences, the maintenance was so painful that we had to patch the logic with pre-processing or post-processing to avoid touching the code. We have learned from these days that logic, when it is complex and/or when it changes decently often, needs to be externalized.
Business owners do not want to go to IT and submit a change request. They want to be able to see the impact of a change before they actually commit to the change. They want the agility to tweak their thresholds or rate tables within minutes or hours, not days and weeks. While there is testing needed for rules like for any software, it is much more straight-forward as it does not impact the code. It is just about QA testing and business testing.
Here is my answer:
I have been wondering the same thing. Several decades ago, I discovered expert systems at school, in my AI class. I fell in love with them, and even more so with rules engines as they were emerging as commercial products.
While coding is more powerful and intuitive than it used to be, the need is still there to make applications more agile. Having software developers change code is certainly more painful than changing business logic in a separate component, ie the decision service.
Some argue that the technology is difficult:
- ability to find issues
Is the technology too difficult to use?
Because of syntax
I can attest that writing LISP back in the days was nothing ‘intuitive’. Since then, thanks God, rules syntax has improved, as programming syntax did too. Most business analysts I have worked with have found the syntax decently understandable (except for the rare rules engine that still use remnants of OPS5). With a little practice, the syntax is easily mastered.
Furthermore, advances in rules management have empowered rules writers with additional graphical representations like decision tables, trees, and graph. At Sparkling Logic, we went a step further to display the rules as an overlay on top of transaction data. This is as intuitive as it gets in my opinion.
Because of debugging
The second point seems more realistic. When rules execute, they do not follow a traditional programmatic sequence. The rules that apply simply fire. Without tooling, you might have to have faith that the result will be correct. Or rely on a lot of test case data! Once again, technology has progressed and tooling is now available to indicate which rules fired for a given transaction, what path was taken (in the form of an execution trace), etc. For the savvy business analyst, understanding why rules did or did not execute has become a simplistic puzzle game… You just have to follow the crumbs.
So why are rules not as prevalent as they should be?
Is the technology too easy to not use?
Because of ownership
I am afraid to say that IT might be responsible. While it is now a no-brainer to delegate to a database for storage, and for some other commonly accepted components in the architecture for specialized functions, it remains a dilemma for developers to let business analysts take ownership. You need to keep in mind that business rules are typically in charge of the core of the system: the decisions. If bad decisions are made, or if no decision can be made, some heads will roll.
Because of budget
Even if the management of decisions is somewhat painful, and inflexible, it is a common choice to keep these cherished rules inside the code, or externalized in a database or configuration file. The fact that developers have multiple options to encode these rules is certainly not helping. First, they can see it as their moment of fun, for those that love to create their own framework (and there are plenty of them). Second, it does not create urgency for management to allocate a budget. It ends up being a build vs. buy decision.
Without industry analysts focused exclusively on decision management, less coverage by publications, and less advertisement by the tech giants, the evangelization of the technology is certainly slower than it deserves to be.
Yet, it should be used more…
Because of Decision Analytics
I would stress that the technology deserves to be known, and used. Besides agility and flexibility (key benefits of the technology), there is more that companies could benefit from. In particular, decision analytics are on top of my list. Writing and executing rules is clearly an important aspect. But I believe that measuring the business performance is also critical. With business rules, it is very easy to create a dashboard for your business metrics. You can estimate how good your rules are before you deploy them, and you can monitor how well they perform on a day-to-day basis.
Because of ease of integration
For architects, there are additional benefits too in terms of integration. You certainly do not want to rewrite rules for each system that needs to access them. Rules engines deployed as a component can be integrated with the online and batch system, and any changing architecture, without any rewrite, duplication, or any nightmare.
With that in mind, I hope that you will not let these roadblocks to rules engines stop you. There are plenty of reasons to consider decision management systems, or rules engines as they are often called. You will benefit greatly:
- Flexibility to change your decision logic as often and as quickly as desired
- Integration and deployment of predictive analytics
- Testing from a QA and business perspective
- Measure business performance in sand-box and in production
- and yet, it will integrate beautifully in your infrastructure