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DMN 1.3 support in SMARTS

DMN 1.3 support in SMARTS

In this post, we present how Sparkling Logic continues its involvement in the DMN standard, through its graphical tool SMARTS Pencil, which business analysts use to model business decisions by drawing a diagram to form a decision process.

DMN, a bit of history

The Decision Model and Notation (DMN) was formally introduced by the Object Management Group (OMG) as a v1.0 specification in September 2015. Its goal was to provide a common notation understandable by all the members of a team whose goal is to model their organization’s decisions.

The notation is based on a simple set of shapes which are organized in a graph. This allows the decomposition of a top-level decision into more, simpler ones, whose results must be available before the top-level decision can be made. These additional decisions themselves would be decomposed, and so on and so forth until the model reaches a more complete state. In addition, the implementation of the decisions can be provided, notably in the form of decision tables (which is also a very common means of representing rules).

The normalization of the graphical formalism (the DMN graph) and of the way the business logic is implemented (e.g., decision tables) allows teams to talk about their decisions, using diagrams with a limited set of shapes.

Sparkling Logic was one of the early vendors to provide a tool to edit (and execute) these decision models: Pencil Decision Modeler. It was released in January 2015, before the standard was officially approved.

Since then, the DMN standard evolved significantly, by adding new diagram elements, new constructs and new language features, while clarifying some of the existing notions. It is now at version 1.3. And we didn’t rest on our laurels either: in SMARTS Ushuaia, we made Pencil Decision Modeler part of SMARTS, as a first-class feature and added full compliance to DMN 1.3! This post describes how SMARTS supports DMN 1.3.


DMN 1.3 still defines the building blocks which were in the original standard and which I mentioned in Talking about decisions.

As a recap:

  • A Decision determines its output based on one or more inputs; these inputs may be provided by an input data element, or by another decision
  • An input data is information used as input by one or more decisions, or by one or more knowledge sources
  • A business knowledge model represents knowledge which is encapsulated, and which may be used by one or more decisions, or another business knowledge model. This knowledge may be anything which DMN does not understand (such as a machine learning algorithm, a neural network, etc.) or a DMN construct (called a “boxed expression”, see below)
  • A knowledge source represents the authority for a decision, a business knowledge model, or another knowledge source: this is where the knowledge can be obtained (be it from a written transcription or from someone)

These blocks are organized in a graph and the links between them are called requirements.

What’s new in SMARTS’ DMN Support

More building blocks

In DMN 1.3, the following elements may also be added to a graph:

  • A decision service exposes one or more decisions from a decision model as a reusable element (a service) which might be consumed internally or externally
  • A group is used to group several DMN elements visually (with whatever semantics may be associated with the grouping)
  • A text annotation is a shape which contains a label and can be attached to any DMN element

Custom types and variables

Input data, decision and business knowledge model elements all have an associated variable, which is of a given type (string, number etc., or custom). A variable is a handle to access the value directly passed by an input data element, or calculated by the implementation of a decision or a business knowledge model, from within the decision implementation.

Custom types may be defined to group multiple properties under a single type name (with structure) or to allow variables which will hold multiple values (arrays).

Boxed Expressions

A few constructs are available to provide an implementation for a decision or a business knowledge models; they are termed boxed expressions since such expressions are shown in boxes which have a normalized representation. The following types of boxed expressions are available in DMN 1.3:

  • Literal expression: this is a simple expression which can use the available variables to calculate a result
  • Context: this is a set of entries, each combining a variable and a boxed expression. Each entry in the context can use the variables of the entries defined before it, which is like using “local variables” in some languages
  • Decision table: this is a tabular representation where rows (called rules) provide the value of outputs (supplied in action columns), depending on the value of inputs (supplied in condition columns)
  • Function: a function can be called using an invocation, by passing arguments to its parameters. The result of a function is the result of the execution of its body (which is an expression that can use the values of the passed parameters). A Business knowledge model can only be implemented by a function
  • Invocation: this is used to call a function by name, by passing values to the function’s parameters
  • List: this is a collection of values calculated from each of the boxed expressions in the list
  • Relation: this is a vertical list of horizontal contexts, each with the same entries

In addition to these, SMARTS defines an additional boxed expression, called the rule set. This is a set of named rules, where each rule is composed of a condition (an expression evaluating inputs) and action (an expression providing some values to outputs).

Helping Industry Adoption

With SMARTS Ushuaia, decision models are first-class citizens. The full compliance with DMN 1.3 means that all the DMN elements and boxed expressions, as well as the ability to interchange diagrams with other tools, are part of the package.

As is usual, any model can be tested and executed in the same context as your SMARTS decision –a decision is never made in isolation, and a model is never used in isolation either. And of course, you will benefit from the great tooling we provide.

Finally, we at Sparkling Logic strongly believe that decision management technologies should be put in the hands of all business analysts. This is why we are part of the DMN On-Ramp Group, whose mission is to provide a checklist to help customers find the DMN tool to suit your needs, educate and raise awareness about DMN, and help with DMN compliance. For a great presentation of the group, check out here.


Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful digital decisioning platform, accessible to business analysts and ‘citizen developers’. Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.

Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.

Marc Lerman is VP of User Experience at Sparkling Logic. You can reach him at

If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, or a product configurator, Sparkling Logic can help. Our SMARTS digital decisioning platform automate decisions by reducing manual processing, accelerating processing time, increasing consistency, and liberating expert resources to focus on new initiatives. SMARTS also improve decisions by reducing risk and increasing profitability.

Software industry trends behind the digital transformation revolution

This article presents the three software industry trends driving the digital transformation revolution: DevOps, low-code / no-code automation, vertical integration with digital decisioning.


The pandemic changed tech priorities for many people both at work and home making a ‘hybrid’ work a top initiative. Where and how we do the work accelerated the need to improve customer digital experiences and efficiency across work, shopping, and everyday chores.

The data supports this new trend. The independent research firm Omdia Future of Work survey compiled over 300 responses from executives at large companies indicated that working away from traditional offices will become the new norm. 58% percent of respondents said they will adopt a hybrid home/work. Even more interesting is that 68% of enterprises believe employee productivity has improved since the move to remote work.

Similarly, adoption of everyday on-line activities such as shopping, banking and entertainment further accelerated the pace of digital transformation. The need for improved applications increased the pressure on companies to relaunch efficient, friendly front-end customer apps with more intuitive UX. The back end now needs to support faster turnaround with the need to automate processes for the new on-line community of users demanding faster, cleaner, and more intelligent offerings.

To respond to this digital transformation, companies are rapidly adopting easy-to-use integrated enterprise software tools to optimize and accelerate development of these efficient digital products.

Several trends like DevOps, Low-code/automation and vertical integrations with integrated digital decisioning have emerged to help enterprises take the digital transformation journey faster and cheaper.


DevOps is a software development concept bringing together historically disconnected functions in the lifecycle of the software development. Traditionally, business analysts would define the problem, developers would interpret the concept and build applications, and operations teams would test, report bugs and provide feedback. The disconnect between the functions, silo’d approach created inefficiencies, increased costs and slowed down application releases.

The emergence of integrated tools and processes which integrate this multiple aspect of software development and promote collaboration between these different functions supported growth of the DevOps industry.

In fact, the market data shows that these trends are supported by the investment community and exit activity. According to Venture Beat, in 2Q 2021, Venture funding for global DevOps startups reached $4 billion and the exit activity deal value was dominated by the IPOs of UiPath (robotic process automation) and Confluent, (data / application integration platform).

Low code / no code automation

Application development is also coming closer to non-developers with low/no-code approach and automation.

Software engineering, traditionally owned by IT and software engineers, has always been coveted by other, non-IT stakeholders in the enterprise. In 1991, Powerbuilder introduced a revolutionary concept of a development framework, aiming at democratizing development by allowing non-software professionals to get access to application development. Perhaps ahead of its time with clunky UX, WYSIWYG, Powerbuilder started the revolution of introducing emergence of ‘citizen-developers’, people who originally participated alongside IT in shaping the application and business models but could not code and create the applications themselves. It also introduced data integration with application logic and object-oriented concepts like inheritance and polymorphism and encapsulation, bringing software engineering to the masses.

Fast forward to 2020’s, virtually every enterprise tool platform and enterprise customer have adopted a low-code/no-code approach. The mission is the same as 30 years ago – to provide easy to use, graphical UI/UX, drag and drop concept to application development and allow business analysts, ‘citizen-developers’ and non-software engineers to create, test and even deploy enterprise applications.

Vertical integration with digital decisioning

The perennial challenge of allowing non-developers to create applications is the conundrum of how deep they can develop without coding and to what extent they can customize complex enterprise cloud applications without IT and coding.

To accelerate digital transformation, enterprise software vendors are emerging mostly from the workflow / BPA world, such as Pega and ServiceNow. They are applying a two prong approach – core tool collection and vertical integration. The workflow vendors have developed (or acquired) a collection of point tools in a core-component framework. Those components typically include AI/ML, reporting, workflow, RPA (Robotic Process Automation), case management, rules engine, decision management, knowledge bases, BPA (business process automation) and process orchestration. Those components typically feature common UI and work across a normalized data model and unified architecture.

But that is not enough. To satisfy modern rapid digital transformation needs, in case of fintech enterprise customers (i.e. banks, insurance companies and financial services) also now require pre-built workflow, data and application models. These vertical templates are higher level and more specific, providing out-of-the box, drag/drop solutions like credit card operations, loan management and payment operations. Using the low-code approach, a business analyst can graphically drag/drop pre-defined steps into a loan origination workflow with pre-defined commonly used tasks, created using best practices defined by the ‘centers of excellence’. Companies like UIPath have created a 3rd party marketplace for additional steps and templates created by analysts and consultants. (Those steps could be ‘get customer data’, ‘OCR input form’, ‘scrub customer data’, authorize user’, ‘assess risk profile’ etc.).

Beyond the top level tasks, the functionality ultimately becomes more complex and the sophisticated customer needs powerful decision capabilities to introduce their own business rules and implement proprietary features. The ‘secret-sauce’, which separtes most common steps from proprietary concepts distinguishes top corporations from the competition, requires more sophisticated digital decisioning tools. These digital decisioning tools enable non-developers to customize and manage decision logic, implement AI/ML features, run A/B testing and visualize performance results on training and production data in real time.

To satisfy most common customer base, digital workflow vendors typically provide rudimentary business rules integrated in their low-code platforms and further integrate them with the downstream workflow platforms and vertical ecosystem vendors (i.e. FiServ, Jack Henry, SAP, Salesforce and FIS in banking for example).

The most sophisticated and demanding customers, however, need a more sophisticated set of digital decisioning tools like standalone professional DM platforms. To simplify and visualize this complex decision management, a new generation of low-code digital decision management platforms like Sparkling Logic emerged. These platforms integrate historical business rules engine, data and AI, demystifying machine-learning and providing low-code approach to development and monitoring of application logic performance, continuously as the business logic and training data change and drift.

The pandemic, hybrid work and pervasiveness of the cloud computing have irreversibly changed the software application development. Enterprise customers are seeking and deploying better, faster, more integrated software tools. DevOps integration, low-code, vertical templates, integrated AI and digital decisioning are becoming a new normal while defining the next generation of applications, created not only by software engineers, but by mere mortals across the enterprise.


Davorin Kuchan is the CEO of Sparkling Logic, Inc, an AI-driven digital decision management enterprise tools platform. Major enterprise customers like Equifax, Centene, First American, Nike, SwissRE and Enova deploy and integrate Sparkling Logic SMARTS digital decision engine. Sparkling Logic, Inc is based in Sunnyvale, California.

Noise reduction in digital decisioning with Sparkling Logic SMARTS

noise-digital-decisioning-explicit-decisions-dashboards-analyticsIn this post, we present how to deal with the problem of noise, which is both a source of errors and biases in digital decision-making in organizations, through explicit decision rules, dashboards, and analytics. To illustrate our point, we use the example of the Sparkling Logic SMARTS decision management platform.

Noise in organizations’ decisioning and what to do about it

In an interview with McKinsey, Olivier Sibony, one of the renowned experts in decisioning, recommends algorithms, rules, or artificial intelligence to solve the problem of noise, a generator of errors and biases in decisioning in organizations. This recommendation resonates with our vision of automating decisioning — not all of the decisioning but the operational decisions that organizations make by thousands and sometimes millions per day. Think credit origination, claim processing, fraud detection, emergency routing, and so on.

In our vision, one of the best ways to reduce noise, and therefore errors and biases, is to make decisions explicit (like the rules of laws) so that those who define the decisions can test them out, one at a time or in groups, and visualize. The consequences of these choices on the organization before putting them into production. In particular, decisions should be kept separate from the rest of the system calling those decisions — the CRM, the loan origination system, the credit risk management platform, etc.

Noise reduction with explicit decision rules, dashboards, and analytics

Our SMARTS decisioning platform helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation, reducing biases that could be a failure to comply with industry regulations, a deviation from organizational policies, or a source of an applicant disqualification. The consequences of biases could be high in terms of image or fees, and even tremendous for certain sensitive industries such as financial, insurance, and healthcare services.

In SMARTS, business users (credit analysts, underwriters, call center professionals, fraud specialists, product marketers, etc.) express decisions in the form of business rules, decision trees, decision tables, decision flows, lookup models, and other intuitive representations that make decisioning self-explainable so that they can test decisions individually as well as collectively. So, at any time, they can check potential noise, errors, and biases before they translate into harmful consequences for the organization.

In addition to making development of decisioning explicit, SMARTS also comes with built-in dashboards to assess alternative decision strategies and measure the quality of performance at all stages of the lifecycle of decisions. By design, SMARTS focuses the decision automation effort on tangible objectives, measured by Key Performance Indicators (KPIs). Users define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. Moreover, they can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.

During the testing phase, the users have access to SMARTS’ built-in map-reduce-based simulation capability to measure these metrics against large samples of data and transactions. Doing so, they can estimate the KPIs for impact analysis before the actual deployment. And all of this testing work does not require IT to code these metrics, because they are transparently translated by SMARTS.

And once the decisioning application is deployed, the users have access to SMARTS’ real-time decision analytics, a kind of cockpit for them to monitor the application, make the necessary changes, without stopping the decisioning application. SMARTS platform automatically displays KPI metrics over time or in a time window. The platform also generates notifications and alerts when some of the thresholds users have defined are crossed or certain patterns are detected. Notifications and alerts can be pushed by email, SMS, or generate a ticket in the organization’s incident management system.

Rather than being a blackbox, SMARTS makes decisioning explicit so that the users who developed it can easily explain it to those who will operate it. Moreover, the latter can adjust the decision making so that biases can be quickly detected and corrected, without putting the organization at risk for violating legal constraints, eligibility criteria, or consumer rights.
So, if you are planning to build a noise-free, error-free, and bias-free decisioning application, SMARTS can help. The Sparkling Logic team enjoys nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have them satisfied, but also proud of the system they build. We helped companies to build flaw-proof, data-tested, and scalable applications for loan origination, claims processing, credit risk assessment, or even fraud detection and response. So dare to give us a challenge, and we will solve it for you in days, not weeks, or months. Just email us or request a free trial.


Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful digital decisioning platform, accessible to business analysts and ‘citizen developers’. Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.

Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.

Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at

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