Category: DM

Business Rules — Focus on “Business”. Guilhem Molines, IBM

Guilhem Molines presented on what business analysts can do in the creation of automated decisions before developers need to get involved (or take over), and what the modeling tools can provide to help them get further in the process independently.

Clearly, business analysts can model business decisions at the level of a decision requirements diagram (e.g., DMN DRD) that shows the entities and information required to make a decision. Then, the data and decision models required for implementation can be created by the business analyst or co-authored together with a developer. IBM’s ODM tool has authoring assistance such as smart predictions to guide an analyst while they are modeling, plus suggestions to define terms in the model as they are used in a rule definition.

In authoring decision tables, assistance may include Excel-like functions for copy and paste or smart drag to extend a range; as a table is being defined, there can be guidance to check for gaps and overlaps in the parameter ranges. Being able to do instant validation for a decision table by entering data values and seeing the calculation result (without deploying the rule) builds confidence in the logic implementation.

A more comprehensive “unit testing” approach allows the analyst to provide a number of input parameter sets in a spreadsheet or similar tabular form and see the results. A further step in testing decisions is simulation based on a large quantity of production data; then integration testing in a full test environment before promotion to production.

Much of this presentation was based on IBM ODM capabilities, although some good ideas here for any modeling environment.

Combining Decision Models for Better Decision Management. Fernando Donati Jorge, FICO

Fernando Donati Jorge presented on whether we have the right decision model to solve mysteries in addition to puzzles — an interesting distinction, where puzzles can be solved given the right information, while mysteries may not have a well-defined answer and can depend on future interactions.

The challenges with mysteries is that there is too much data but no indication of the most relevant bits; they are full of uncertainties; and they depend on future known and unknown interactions. Decision models can properly contextualize data, but don’t measure relevance. Predictive analytics can quantify uncertainties, and decision models can contextualize the use of different types of knowledge models. An analytic decision model (a Gartner term, which does not include DMN decision models) can represent how different decision outcomes can lead to different future interactions.

Typically, there are two separate types of decision models: one that models the input data and business knowledge model that results in a decision (a DMN model), and the other that models how a decision impacts business performance. If you want to combine these into a single model, an extension to DMN is required to be able to model performance metrics, which in turn have models, decisions and data as inputs.

This DMN extension is available in the FICO Analytic Cloud for adding KPIs to decision-making. Good discussion about how they handle latency for calculating the performance metrics, and the issue of metrics aggregation over multiple decision instances. They don’t currently allow a performance metric to inform (provide input to) a decision, but that’s obviously open for future discussion.

Both of the presentations in this section have looked at how vendors are adding value to their DMN-based modeler: IBM in terms of interactive to assist the modelers in creating better models, and FICO in extending DMN to include performance metrics directly in a decision model.

Beyond Decision Models – Using Technical and Business Standards to Transform Financial Services. Brian Stucky, Quicken Loans

Although having recently joined Quicken Loans as a senior enterprise architect, Brian Stucky is also involved in the MISMO mortgage standards organization, which was the focus of his presentation. Earlier this year, MISMO recommended DMN as an official standard for documenting, implementing, exchanging and executing decision models in the mortgage industry; they are also working on officially recognizing BPMN too. The idea is to create a DMN data structure based on the existing MISMO XSD to allow these mortgage-related decision models to be shared, but the industry is still rife with paper-based processes and legacy systems that hinder adoption.

There’s a history of business rules in the mortgage industry, but it didn’t really allow for business control of the rules, didn’t have the agility required, and was expensive. DMN is changing that game — especially with decisions as a service instead of on-premise systems — and allowing mortgage companies to better meet some of the new regulations such as Ability-To-Repay, where the written government regulation can be translated into a standard DMN model to ensure that all parties are using the same evaluation criteria. They’ve proven that time required to change the DMN model for a specific rule can take as little as a couple of hours to analyze and modify the model, which is a huge push for moving from MISMO XSD to the DMN model.

In the future, this could mean that DMN plus the MISMO data model could be used directly to disseminate a regulatory rule change, rather than the 800-page text document used now. That brings up other issues, such as versioning of the model or even of DMN, and engine compliance in executing the DMN models as distributed. A better way to do it may be to roll out the model as a service with an open API, where every mortgage provide uses the same decision service; this guarantees that it will be evaluated identically everywhere. The ultimate goal may be a digital mortgage, potentially using blockchain to ensure the chain of events in this smart contract.

Meeting the Expectations with DMN and Constraint Solving: The Notary Case. Marjolein Deryck, KU Leuven

Marjolein Deryck presented on research in decision modeling and knowledge representation, and how she applied it to property registration taxes in Belgium, which is typically calculated by a notary. The use case was to support a notary when performing these calculations, using DMN for collaborative analysis with the notary and an executable prototype; then IDP to go beyond DMN capabilities in a constraint approach.

An interesting requirement was that the support application be non-intrusive: the notary felt that if he was spending too much time typing on a computer while figuring this out with a client, he would be seen as less of an expert. A tablet-based app with minimal requirement for data entry, plus interactive in terms of presenting the next best question rather than following a fixed script were seen as essential.

In her initial evaluation, DMN was seen as lacking script interactivity/adaptability (although I saw a really interesting way to use DMN and BPMN to resolve this last week at CamundaCon), and she instead considered IDP as a more powerful implementation. This provides a better solution, although the models are less understandable by the notaries, and required enhancements to be able to provide an explanation of a specific calculation.

The lessons learned included the use of DMN as an intermediate model — for gathering and analyzing requirements together with the business user — as well as how to combine DMN and IDP in a project.

Panel: DMN and Beyond

We closed off day 1 of DecisionCAMP 2019 with a panel that included Mike Gualtieri, Alan Fish, Jan Vanthienen, Jan Purchase, Gary Hallmark and Brian Stucky, moderated by Jacob Feldman.

A few points that came up during the panel (unattributed to the specific speaker):

• Many buyers of decision management systems don’t known enough about DMN to evaluate it or even ask for it.
• DMN still falls short in complex representations, although works well to represent static hierarchical information from decision tables. It has the potential to include other representations and other models such as machine learning. Making it more powerful could, however, have DMN lose the simplicity that makes it more likely to be adopted.
• DMN is difficult to debug, making it hard to figure out logic flaws.
• The diagram/graph level of DMN is very understandable to business users/analysts, but by the time you’re doing more complex nested expression logic at the FEEL execution level, you’ve lost most of them.
• Highly-regulated industries such as lending, where rules are already documented in spreadsheets, are a good target for DMN implementation.
• Being able to follow the execution path is not the same as an explanation of the decision logic. The DMN standard includes support for remarks/annotations to improve explainability but that may not be sufficient.
• Knowledge sources in DMN models have no programmatic representation, putting the onus on the modeler to ensure explainability and traceability.
• Ethics are important to decision management in terms of decision fairness and consistency. DMN model-based decision making can improve that as long as the models are based on the right rules and data.
• There’s a need to be able to integrate DMN and machine learning while still providing decision explainability.
• Models are always fit for purpose: there is no all-encompassing model that is suitable for everything. As an aside, that’s definitely true in the BPMN realm too.

That’s it for day 1; I’m off to find a gelato and an Aperol Spritz on this warm evening in Bolzano.

How and Why I Turned a Rule Engine into a First-Class Serverless Component. Mario Fusco and Matteo Mortari, Red Hat

Mario Fusco, who heads up the Drools project within Red Hat, presented on modernization of the Drools architecture to support serverless execution, using GraalVM and Quarkus. He discussed Kogito, a cloud-native, open source business automation project that uses Red Hat process and decision management along with Quarkus.

I’m not a JAVA developer and likely did not appreciate many of the details in the presentation, hence the short post. You can check out his slides here.

Combining DMN, First Order Logic and Machine Learning: The creation of Saint-Gobain Seals’ Digital Engineer. Nicholas Decleyre, Saint-Gobain and Bram Aerts, KU Leuven

The seals design and manufacturing unit of Saint-Gobain had the goal to create a “digital engineer” to capture knowledge, with the intent to standardize global production processes, reduce costs and time to market, and aid in training new engineers. They create an engineering automation tool to automatically generate solutions for standard designs, and an engineering support tool to provide information and other support to engineers while they are working on a solutions.

Automation for known solutions is fairly straightforward in execution: given the input specifications, determine a standard seal that can be used as a solution. This required quite a bit of knowledge elicitation from design engineers and management, which could then be represented in decision tables and FEEL for readability by the domain experts. Not only the solution selection is automated, however: the system also generates a bill of materials and pricing details.

The engineering support system is for when the solution is not known: a design engineer uses the support system to experiment on possible solutions and compare designs. This required building a knowledge base in first-order logic to define physical constraints and preferences, represented in IDP, then allowing the system to make recommendations about a partial or complete solution or set of solutions. They built a standalone tool for engineers to use this system, presenting a set of design constraints for the engineer to apply to narrow down the possible solutions. They compared the merits of DMN versus IDP representations, where DMN is easier to model and understand, but has limitations in what it can represent as well as being more cumbersome to maintain. At RuleML yesterday, they presented a proposal for extended DMN for better representing constraints.

They finished up talking about potential applications of machine learning on the design database: searching for “similar” existing solutions, learning new constraints, and checking data consistency. They have several automated engineering tools in development, with one in testing and one in production. Their engineering support tool has working core functionality although need to expand the knowledge base and prototype the UI. On the ML work, they are expecting to have a prototype by the end of this year.

Collaborative decisions: coordinating automated and human decision-making. Alan Fish, FICO

Alan Fish presented on the coordination of decisions between automation, individuals and groups. He considered how DMN isn’t enough to model these interactions, since it doesn’t allow for modeling certain characteristics; for example, partitioning decisions over time is best done with a combination of BPMN and DMN, where temporal dependencies can be represented, while combining CMMN and DMN can represent the partitioning decisions between decision-makers.

He also looked at how to represent the partition between decisions and meta-decisions — which is not currently covered in DMN — where meta-decisions may be an analytical human activity that then determines some of the rules around how decisions are made. He defines an organization as a network of decision-making entities passing information to each other, with the minimum requirement for success based on having models of processes, case management, decisions and data. The OMG “Triple Crown” of DMN, BPMN and CMMN figure significantly in his ideas on a certain level of organizational modeling, and the success of the organizations that embrace them as part of their overall modeling and improvement efforts.

He sees radical process reengineering as being a risky operation, and posits that doing process reengineering once then constantly updating decision models to adapt to changing conditions. An interesting discussion on organizational models and how decision management fits into larger representations of organizations. Also some good follow-on Q&A about whether to consider modeling state in decision models, or leaving that to the process and case models; and about the value of modeling human decisions along with automated ones.

Making the Right Decision at the Right Time: Introducing Temporal Reasoning to DMN. Denis Gagné, Trisotech

Denis Gagné covered the concepts of temporal reasoning in DMN, including a new proposal to the DMN RTF for adding temporal reasoning concepts. Temporal logic is “any system of rules and symbolism for representing, and reasoning about, propositions qualified in terms of time”, that is, representing events in terms of whether they happened sequentially or concurrently, or what time that a particular event occurred.

The proposal will be for an extension to FEEL — which already has some basic temporal constructs with date and time types — that provides a more comprehensive representation based on Allen’s interval algebra and Zaidi’s point-interval logic. This would have built-in functions regarding intervals and points, with two levels of abstraction for expressiveness and business friendliness, allowing for DMN to represent temporal relationships between points, between points and intervals, and between intervals.

The proposal also includes a more “business person common sense” interpretation for interval overlaps and other constructs: note that 11 of the possible interval-interval relationships fall into this category, which makes this into a simpler before/after/overlap designation. Given all of these representations, plus more robust temporal functions, the standard can then allow expressions such as “interval X starts 3 days before interval Y” or “did this happen in September”.

This is my first time at DecisionCAMP (formerly RulesFest), and I’m totally loving it. It’s full of technology practitioners — vendors, researchers and consultants — who more interested in discussing interesting ways to improve decision management and the DMN standard rather than plugging their own products. I’m not as much of a decision management expert as I am in process management, so great learning opportunities for me.