Decision Modeling
On Decision Representation
There is not one but several representations of prescriptive decision logic. In this blog post, we describe the most used ones, from the simplest to the most sophisticated one.
Decision tables
Decision tables are a tabular representation of decisions. You can think of a decision table as a spreadsheet where rows are decisions and columns are the elements of decisions: inputs/outputs, conditions/conclusions, or conditions/actions.Decision tables are to be used when all the decisions (rows) have similar conditions (first columns) and similar actions (last columns). They should be used for stable decision logic where changes in the number of conditions are not frequent. Otherwise, they will pose the same difficulty as if the decision logic were mixed in with the code of the rest of the application.
Decision trees
A decision tree is a flowchart representation that graphically resembles an upside-down tree. The root of the tree is a decision that needs to be made. The inner nodes represent tests on attributes. The branches of the tree are further steps that need to be run, and the leaves of the tree are the decisions. Paths from root to a leaf represent a final decision.Decision trees are more powerful than decision tables because they allow decisions to have different numbers of conditions and actions. They are a better visual representation than decision tables when managing hierarchical decisions. Decision trees are to be used when the decisions share many conditions.
Decision graphs
Decision graphs are a generalization of decision trees where the flowchart is not from the root to the leaves. Links of the flowchart can go from one internal node to another at the same level or go up to a node at a higher level.Decision graphs are more sophisticated than decision trees. They are particularly useful for reflexive decisions, such as in dynamic questionnaires that can backtrack on a question based on information provided by respondents.
Lookup models
Lookup models are similar to VLookup in Excel. They transform a large data spreadsheet into a smaller indexed spreadsheet. Let’s say you import an auto insurance pricing spreadsheet with state, age, gender, score range, and rate columns. Given a value to each of these columns, the lookup model will retrieve the rate that matches these values.Lookup models are interesting when the tables are very large and really represent a lookup: determining a set of values from another set of values.
Business rules
With business rules, decisions take the form of “condition(s)-action(s)”. A rules engine iterates through the ruleset and triggers the rules with conditions that are true.Business rules provide a natural way to express decisions. They can express any imaginable decision logic and symbolic computation, making it the choice for highly sophisticated decisioning applications where the conditions as well as the actions can take a great variety of forms.
Which representation is the best?
No representation is better than the other. That’s why SMARTS supports them all.With SMARTS, you write your decisions in the form that suits you best. You can build a complete decision flow as a graphical diagram that reflects the actual flow of your transactions and mirrors the steps you get used to. You can display your decisions as a rule set, a decision table, trees, or a decision graph. And if necessary, you can switch from one representation to any other at any time.
About
Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve their operational decisions with a powerful decision management 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 an all-in-one low-code platform for data-driven decision-making. It unifies decision authoring, testing, deployment, and maintenance. You can test it or ask for a demo.
The SMARTS Way for Personalization
Personalization has always been the holy grail of marketing, advertising, sales, and customer relationship management. So far, two approaches have been used. The most fashionable today uses statistical data to make recommendations, the second oldest but which is coming back to the fore uses coded knowledge to make these recommendations. In this blog, we will show when one is preferable to the other as well as when the two approaches can be combined in SMARTS.
Personalization
Personalization can play many roles in marketing, advertising, sales, and customer relationship management such as identifying good prospects for a specific product or service, choosing a communication channel to reach prospective customers, and picking appropriate messages that fit both customer and channel.So far, two approaches coexist. Data-based personalization and knowledge-based personalization. You may wonder which one is the best. In fact, it depends on the sector in which you are.
Data-driven personalization works well when you have a lot of data to draw insights from and when the new data doesn’t deviate too much from the old data you based your insights on. We find this case in fast-moving consumer goods sectors such as retail, as people tend to consume the same consumables every week.
On the other hand, knowledge-based personalization works well when you don’t have enough data but want to offer a product, service or content based on the knowledge you have about the prospects and your offer. We find this case in premium sectors such as luxury, wealth management, and high-touch hotels, where customer intimacy is a must.
By design, SMARTS treats both data and knowledge equally. After all, what is often called data comes from knowledge of the subject matter – It is always someone knowledgeable about the subject who labels or explains the data collected. Thus, SMARTS supports both data-based personalization and knowledge-based personalization.
The SMARTS way
SMARTS is a low-code platform that enables creating, testing, deploying, and improving automated decisions in the form of decision tables, business rules, and other representations. I will not detail it here, but you can find a brief overview of SMARTS on our blog page and a full description on our resources page. Instead, I will focus the rest of this article on how to use SMARTS for data-based personalization and knowledge-based personalization.Data-based personalization
For data-based personalization, you can import recommendation models developed by your data scientists and leverage them in SMARTS. The models could be in Python, SPSS, SAS, or Project R among others. SMARTS integrates them if they are compliant to PMML, a standard for sharing and deploying predictive models.SMARTS supports importing as PMML neural networks, multinomial, general, and linear/log regression, trees, support vector machines, naïve bayes, clustering, ruleset, scorecard, K-Nearest Neighbors (KNN), random forest, and other machine learning models.
There may be situations where the model must be called as an external service. SMARTS provides support for remote functions, which makes it possible to invoke the model through JSON-RPC or REST services.
Knowledge-based personalization
RedPen. For knowledge-based personalization, you can use our rule authoring tool RedPen to write decisions in the form of rules using a use-case driven approach. A loaded data sample supplies the context for the rules and enables immediate execution and testing of each rule. RedPen mimics what subject-matter experts do when they flag decisions. When you activate RedPen, you can pin an existing rule, a field of this rule, or a rule set and change it as if you were using a real pen on real paper. You can also create new rules with RedPen, SMARTS automatically turns them into executable rules.Pencil. You can also use Pencil, our DMN compliant graphical decision design tool for uncovering, documenting, and sharing decisions with colleagues and partners. With Pencil, you just drag and drop graphical shapes to form a complete personalization diagram. Then you add logic to the graphical shapes and let SMARTS execute it.
SparkL. Finally, you can also use SparkL, Sparkling Logic’s language for writing rules in a natural language fashion. SparkL comes with everything you need to write rules and calculations —mathematical expressions, string manipulations, regular expressions, patterns, dates, logical manipulations, constraints, and much more. You can express any imaginable personalization logic and symbolic computation, making it the choice for highly sophisticated personalization applications.
Personalization based on data and knowledge
BluePen. As said before, SMARTS treats data and knowledge equally. When you have both, you can use BluePen, our machine learning tool.BluePen lets you explore and analyze your data using your domain knowledge to find predictors. Then, using these predictors, you can generate a model in the form of legible rules and integrate them into your decision logic.
Using BluePen, you can engineer or change the models anytime you need to. Without heavy investment in data analytics tools and efforts, you can evaluate BluePen models in simulations and quickly deploy them in the context of an operational decision.
Wrap-up
- Personalization has always been the holy grail of marketing, advertising, sales, and customer relationship management.
- So far, two approaches have been used: data-based personalization and knowledge-based personalization.
- No one is superior, it depends on the sector in which you are: mass marketing vs. intimacy marketing.
- SMARTS treats data and knowledge equally. So, you can use it for both data-based personalization and knowledge-based personalization.
About
Sparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.Our motto is “your decisions, our business.” Using SMARTS, organizations have automated complex decisions in days, not weeks, or months. Our mission is to enable customers to implement the most demanding decisioning requirements and to easily maintain and improve them over time.
Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Unlike other tools that focus solely on the authoring and maintenance of business rules, SMARTS is decision-centric and focuses on measuring and improving business outcomes in the context in which our clients work, especially with complex regulations. Major enterprise customers like Equifax, First American, SwissRE, Centene, and NICE Actimize integrate SMARTS in their core systems.
Data vs. knowledge in automated decision management — Why not both?
In the tech industry, we also have our well-known “Coke vs. Pepsi”, “Avis vs. Hertz”, or “Mac vs. PC” debates. In the automated decision management category, the question that keeps coming up is “data vs. knowledge.” The aim of this blog post is to show that from a practical point of view, data and knowledge can be found in the same application. To do this, we will show it with SMARTS, Sparkling Logic’s decision management platform that allows users to combine data and knowledge without them entering the “data vs. knowledge” debate.
Origin of the debate
The “data vs. knowledge” debate dates to an old debate about whether knowledge about a subject should be hand coded or machine learned. A first camp of researchers and practitioners sought to encode this knowledge in the form of rules and an inference engine that runs on these rules to supply answers to user questions. A second camp sought to develop programs that learn from available data using statistical methods to generate models that can make predictions from unseen data. At Sparkling Logic, we support a pragmatic approach that consists in using data, knowledge, or both depending on the problem and the situation at hand.It is all about the situation
There is no such thing as a stand-alone decision management application. It is often built with the purpose of being integrated into a larger system for loan origination, risk management, product configuration, or other similar applications. As I wrote before, there is no one single approach. It is all about the situation.Data is everywhere, easy to collect, organize, and transform into predictive knowledge. So, if you have a lot of data, it may be better to build your decision management application around that data if the new observed data does not deviate too much from the old, learned data.
On the other hand, when you have knowledge whether in the form of rules or procedures, it is better to build your application around this valuable knowledge if it is easy to capture and code into the application.
If you have both data and knowledge, why not using the two, when you can do so in a modern decision management platform such as SMARTS, the subject of the next section.
The SMARTS way
SMARTS is a decision management platform that enables creating, testing, deploying, and improving automated decisions in an integrated platform. I will not detail it here, but you can find a brief overview of SMARTS on our blog page and a full description on our resources page. Instead, I will focus the rest of this article on how to use SMARTS when you have plenty of data or domain knowledge about the application you want to develop.You have plenty of data
For situations where you have plenty of data, SMARTS proposes two tools: RedPen and BluePen.
With RedPen, you write decisions in the form of rules using a use-case driven approach. A loaded data sample supplies the context for the rules and enables immediate execution and testing of each rule. RedPen mimics what subject-matter experts do when they flag decisions.
When you activate RedPen, you can pin an existing rule, a field of this rule, or a rule set and change it as if you were using a real pen on real paper. You can also create new rules with RedPen, SMARTS automatically turns them into executable rules.
On the other hand, BluePen lets you explore and analyze your data using your domain knowledge to find predictors. Then, using these predictors, you can generate a model in the form of legible rules and integrate them into your decision logic.
Using BluePen, you can engineer or change the models any time you need to. Without heavy investment in data analytics tools and efforts, you can evaluate BluePen models in simulations and quickly deploy them in the context of an operational decision.
You have domain knowledge
For situations where you have knowledge, SMARTS proposes two additional tools: SparkL and Pencil.
SparkL is Sparkling Logic’s language for writing rules in a natural language fashion. SparkL comes with everything you need to write rules —mathematical expressions, string manipulations, regular expressions, patterns, dates, logical manipulations, constraints, and much more. You can express any imaginable decision logic and symbolic computation, making it the choice for highly sophisticated decisioning applications where the conditions as well as the actions can take a wide variety of forms.
Pencil is our DMN compliant graphical decision design tool for uncovering, documenting, and sharing decisions with colleagues and partners. With Pencil, you just drag and drop graphical shapes to form a complete decision diagram. Then you add business logic to the graphical shapes and let SMARTS execute it.
Pencil helps you think about the ultimate decisions in a structured way, starting from the top-level decision to smaller sub-decisions. This iterative process is very friendly and amazingly easy to share with colleagues or partners working on the same project.
In addition to the above tools and language, SMARTS comes with a built-in dashboard to measure and improve business outcomes of the taken decisions.
You have both
If you are lucky enough to have both data and knowledge, you can leverage your models’ outputs by using it as the input to rules. For example, your loan management application could run a model that calculates a score and another model that calculates a risk and use that score and risk in a rule to calculate a price.
You can also do it the other way around, using the outputs of rules as inputs to your models that you would have trained with data. For example, your application might run a rule to classify a loan applicant, then run a model to calculate their risk of default and another model to calculate the price.
Whether you have data, knowledge, or both, SMARTS uses them as sources of information for the automation of your operational decisions.
Summary
- Data and knowledge do not have to be antagonistic. They can both be used as inputs to automate decisions.
- SMARTS is a modern decision management platform that enables their combination in an elegant and seamless way. For SMARTS, data and knowledge can be used as sources of information.
- When you have a lot of data, you can use RedPen to write rules without learning a special rule language or syntax, just starting with the data. You can also use BluePen to learn from data and turn it into rules.
- When you have knowledge, you can use SparkL to encode it into rules, from the simplest to the most complex rules that your application may require. You can also use Pencil when designing, documenting, and sharing your decisions are part of the requirements.
- Our mission is to enable customers to implement the most demanding decisioning requirements and to easily change and improve them over time. Whether you have data, knowledge, or both, we can help. Just contact us or request a free trial.
About
Sparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Hassan Lâasri is a data strategy consultant, now leading marketing at Sparkling Logic. You can reach him out at hlaasri@sparklinglogic.com.
SMARTS as regulatory compliance technology
So far, we’ve covered how to use SMARTS for decision management, micro-calculation, and data transformation. In this blog post, we show how you can use it to implement regulatory technology (regtech).
Regtech
Regulations, from Basel rules on bank capital requirement to Sarbanes-Oxley Act on corporate financial statements, to MiFID on pre- and post-trade transparency requirements across EU financial markets, have forced regulated companies to develop processes to find, assess and mitigate risks. To comply, investment firms, retail bankers, and insurance companies have turned to regtech for help.Regtech is an acronym for governance, risk, and compliance management technologies in companies, more particularly those working in highly regulated industries such as financial services, insurance, and healthcare. They allow the implementation of legal requirements in the nervous system of companies, in front-, middle- and/or back-office.
All regulations being prescriptive, it was natural that rule-based systems were among the first technologies used, with varying degrees of success which we will detail here, before showing how SMARTS overcame them.
SMARTS as regulatory compliance technology
Your data is uploaded and transformed in the same tool
Highly regulated companies must deal with continuous growth in transaction volumes and a massive accumulation of data that they must ingest or produce daily while constantly complying with ongoing regulations. They could do it, but they had to use other tools besides rules-based systems and they either had to connect them or transfer the data back and forth. With SMARTS, they don’t have to, since it allows data to be uploaded and transformed into rules or calculations in the same tool.
Your data is turned into insights and your insights into decisions
Before the widespread use of big data and analytics, investment bankers relied on complex analysis of information using statistical learning. Today, the entire financial services and insurance industry has integrated advanced data analytics into its artillery to detect market signals and predict market trends. The most advanced want not only to transform data into insights, but these insights into decisions. SMARTS was one of the only tools if not the only tool to offer an integrated solution to transform such companies into lifelong learning organizations where data helps find opportunities and risks, machine learning turns that data into knowledge and rules transforms this knowledge into decisions, thereby closing the virtuous circle that data promises.
You limit your risk for noise, errors, and biases
Since the advent of data, regulators have been closely monitoring the bias issues of automated decision-making systems, particularly those that rely solely on data and use machine learning to calculate scores and then decide instead of a human. In SMARTS, users implement decisions in the form of business rules, decision trees, decision tables, decision flows, and lookup models. All these intuitive representations make decisioning self-explainable so that they can test decisions individually as well as collectively. So, at any time, they can check potential noise and errors before they translate into biases.
Your data and transactions are tracked in real-time
A key need of the financial services industry is real-time responsiveness to suspicious events such as unusual transactions that may show fraud, money laundering, insider trading, or may not be unusual in themselves but nevertheless exceptional in relation to other transactions before or after. Based on their experience in the earlier generation of decision management systems, the founders of Sparkling Logic decided to integrate real-time decision analysis from the ideation of SMARTS. The product has always integrated a dashboard that tracks data and transactions so that the user can react by changing rules in real time.
You react very quickly before an error, an anomaly, or a fraud spreads
Companies must make thousands of complex risky decisions – monetary, reputational, and legal risks. For example, in every decision they make, there are tiered combinations of terms and conditions, legal constraints, eligibility criteria, and levels of risk involved. Rule-based systems allowed them to implement these decisions in the form of tables or decision trees, or rules, but at the expense of side effects on the business. With SMARTS, they graphically define KPIs and drag and drop them into a dashboard to visually check the impact of each decision or group of decisions on business performance. Users can also set thresholds and define patterns which if reached will trigger notifications and alerts. This way, the users will be able to react very quickly before an error, an anomaly, or a fraud spreads and results in enormous damage.
Your system is easy to maintain and upgrade
Prior to the emergence of regtech as a hot technology, highly regulated companies used rules engines to encode the directive logic of laws, regulations, and internal policies. Additionally, they could implement complicated decisions with tens of thousands or more if-then rules. All went well until they discovered that, like any hard-coded software, rule-based systems could be complex to maintain. With SMARTS, they don’t code and hope the code is correct. They create, test, deploy, run, monitor, and change graphically through web forms and point-and-click. Therefore, systems developed with SMARTS are easy to maintain and upgrade.
Wrap-up
The SMARTS is not strictly speaking regtech in the sense that it does not come with all the code of financial and insurance regulations, but it allows them to be implemented quickly and explicitly in the form of rules, trees or graphs. This makes the code easier to change if regulations change as they often do, such as Basel which is in its third version and MiFID in its second version.SMARTS not only eases the implementation of governance, risk and compliance rules, but it also facilitates their monitoring in real-time. SMARTS not only eases the implementation of governance, risk and compliance rules, but it also facilitates their monitoring. KPIs, dashboards and metrics were fundamental from the start of the product and not an afterthought once the product was released.
If you envision modernizing the implementation of a regulation, be it Basel, Sarbanes-Oxley, MiFID, GDPR, or any other regulation, SMARTS can help. Just contact us or request a free trial.
About
Sparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.Our motto is “your decisions, our business.” Using SMARTS, organizations have automated complex decisions in days, not weeks, or months. Our mission is to enable customers to implement the most demanding decisioning requirements and to easily maintain and improve them over time.
Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Unlike other tools that focus solely on the authoring and maintenance of business rules, SMARTS is decision-centric and focuses on measuring and improving business outcomes in the context in which our clients work, especially with complex regulations. Major enterprise customers like Equifax, First American, SwissRE, Centene, and NICE Actimize integrate SMARTS in their core systems.
Sparkling Logic SMARTSTM in 10 Questions and Answers
Sparkling Logic helps businesses automate and improve the quality of their operational decisions with a technology platform that is powerful and simple: SMARTS for short. In this post, we present SMARTS through 10 selected questions and answers.
Q&A
1) What is SMARTS?
SMARTS is a decision management platform for business analysts and ‘citizen developers’ to author, test, simulate, deploy, run, and change decisions autonomously, without involving developers or IT beyond first installation.
2) Is SMARTS a business rules engine?
SMARTS is more than a business rules engine. It integrates multiple decision technologies into the same platform. SMARTS provides eight execution engines: A decision flow engine to sequence tasks of a business process; a state-machine engine to orchestrate tasks; a rule set engine to sequence decisions; a sequential engine that either fires all or just the first valid decision; a Rete-NT engine for inference; a lookup engine for data search in large datasets; a PMML engine to execute predictive models; and a DMN 1.3 engine to execute decision models. Depending on the problem you have, you may choose one or the other, or even combine them in the same set-up.
3) What are the typical applications for which SMARTS is the best fit?
In the financial, insurance, and healthcare services, SMARTS often won over the competition for origination and underwriting, pricing and rating engines, account management, fraud detection, and collections and recovery. More generally, SMARTS is a good fit when there are a lot of decisions that are data-based, frequently invoked, and likely to change often.
4) What is the difference between authoring business decisions and rules with SMARTS and coding them directly in the final application?
You can code decision logic but you will need detailed specifications from business analysts. This process may take too much time when compared to SMARTS. And once the decision logic is coded, it becomes complicated for business analysts to understand and take control of. SMARTS targets business-critical decision logic that either implements business models, corporate policies or industry directives in a dynamic and continually changing economy. Think of all the financial, insurance, and healthcare regulations since the financial crisis of 2008 and the changes since the coronavirus crisis of 2020. These two crises are typical examples of complex situations where business decisions not only need to be implemented quickly and accurately, but they also need to change dynamically and continuously.
5) Does SMARTS come with a decision design process?
SMARTS not only supports but it also augments the Decision Model and Notation (DMN) standard of the OMG (Object Management Group). DMN models decision dependencies very well, but not decision sequencing, which is also a natural way experts use to describe a complete decision logic. SMARTS addresses both dependency and sequencing through the combination of Pencil, RedPen, and the decision flow.
6) What machine learning models does SMARTS support?
SMARTS supports the execution of 13 machine learning models including classification, linear and logistic regression, support vector machines (SVMs), decision trees, random forests and ensemble learning, clustering, and neural networks. SMARTS uses PMML, the standardized predictive model markup language, to import and execute whatever model your data scientists have built.
7) Does SMARTS integrate with business process management platforms?
Yes, a SMARTS decision service can be natively invoked by a business process like any other service. Also, for decision-centric processes, SMARTS provides an orchestration capability.
8) What is the difference between an RPA tool and SMARTS?
If you think of a process as a sequence of “what to do”, “how to do it”, “do it”, and “report it”, then SMARTS automates the “what to do” and “how to do it” tasks while an RPA tool automates the “do it” and “report it” tasks.
9) Is SMARTS cloud-based?
SMARTS was designed from the ground-up for the cloud. Whether you have chosen to host your application or use our SaaS solution, we provide you with the most modern tools. SMARTS comes in a container, ready to install on your premises, AWS, GCP, Azure, or Aliyun. Choose yours, change your mind, no need to recode to redeploy your application.
10) What makes you unique?
Our motto is “your decisions, our business”. We enjoy nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have clients satisfied but also to be proud of the system they built. 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.
In this post, we introduced SMARTS through 10 selected questions and answers. If you have more, feel free to read our blog, sign up for our webinars, or contact us. We would be happy to get back to you very quickly.
About
Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful decision management 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, decision technology 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 hlaasri@sparklinglogic.com.
Decision Requirements and Modeling with DMN 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.
Basics
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.
About
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 mlerman@sparklinglogic.com.
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.
Authoring Business Rules with Data, Standards, and Apps in SMARTS
Nowadays, business rules automate hundreds, thousands, and sometimes millions of operational decisions that some organizations make every day. The most representative examples of such organizations are financial, insurance, and healthcare sectors. All these organizations make automated decisions with several combinations of terms and conditions, legal constraints, eligibility criteria, risk levels, and price ranges. In this blog, I explain how business analysts and ‘citizen developers’ author decisions with rules, data, standards, and apps in Sparkling Logic SMARTSTM.
Business rules
Business rules are not new; but until recently they were encoded in the rule syntax as “IF THIS THEN DO THAT” statements. As such, they needed detailed specifications from business analysts and skilled developers to code these business rules. And once the business rules were coded, they were complicated for business analysts to understand or control.Authoring with data
Gone are the days when business rule creation started with lengthly interviews where IT professionals asked business experts how they made decisions in line with company policies, industry regulations, and market dynamics. Starting with data, transactions, and use cases is now the new way. Fully in line with this new approach, SMARTS provides RedPenTM, SparkL, and Pencil. These are three independent but complementary technologies that business analysts can use to import data, and start authoring rules.RedPen is Sparkling Logic’s patented technology for authoring decisions through point-and-clicks. Using RedPen, business analysts write business rules using a use case approach. The loaded sample data provides the context to create, test, and run rules without prior knowledge of a special rule language and syntax. RedPen mimics what business experts do on paper when they flag decisions with a red pen. When business analysts activate RedPen, they can pin an existing rule, a field of this rule, or a rule set and modify it as if they were using a pen on a paper. They can also create new rules with RedPen, SMARTS will automatically turn them into executable rules. For cases where advanced logical, mathematical, and symbolic manipulations are required, business analysts can use SparkL.
SparkL (pronounced “sparkle”) is Sparkling Logic’s language for writing rules in a natural language format. SparkL can be used by business analysts with no formal technical background in rules syntax while still benefiting from mathematical expressions, string manipulations, regular expressions, patterns, dates, logical manipulations, constraints, and much more. They can express any imaginable decision logic and symbolic computation, making it the choice for highly sophisticated decisioning applications where the conditions as well as the actions can take a great variety of forms.
Other cases where the decisioning projects necessitate formal requirements and decision modeling, the standards development organization (OMG) offers a standard called Decision Model and Notation (DMN). Sparking Logic has adopted this standard and developed Pencil to operationalize DMN.
Authoring in the context of DMN standard
Pencil is a tool for users to model business decisions by dragging and dropping graphical icons to form a decision process. Pencil models comply with the DMN standard. Using an intuitive graphical interface, business analysts can immediately start capturing data requirements, decision models, and business rules, while collaborating to achieve the best explicit description of the decisions required for systems and applications. Pencil’s glossary can be used across decisions to achieve consistent use of terminology related to decisions. Business analysts can create or import data and then execute, test and continue to refine and improve decisions. Once decision modeling is done, Pencil provides a direct path to an executable decision.With SMARTS, a user has not to adapt to the tool, but the reverse, it is the tool that adapts to the user. The business analysts select the appropriate way for the task at hand. In the same project, they may choose Pencil to model decisions, RedPen for the major part of the application, and SparkL for the rest of the application. At any time, they can choose to display the rule sets as a group of rules, a decision table, a decision tree, or a decision graph. Moreover, they can switch from one representation to another and vice versa.
Orchestrating business apps
As intuitive as a decision management tool can be, it may never meet the needs of a real business person. The bells and whistles that business analysts need can be overwhelming for the credit manager or insurance underwriter who needs access to decision logic. This person is certainly more inclined to exploit decision-making logic than interested in learning how to create it, and even less in training on a rules authoring tool.For untrained business users, SMARTS sets the bar higher towards more simplification, and still within the same interface. They have full control over the configuration, management, and assembly of the decision applications that business analysts have developed, and they can do it all through web forms and point-and-clicks. With this added level of abstraction, untrained business users, business experts, and ‘citizen developers’ can adapt to industry regulations, company policies, and market dynamics, without IT intervention beyond the first installation.
Takeaways
- Business rules have moved from coding rules in “IF THIS THEN THAT” statements to authoring them with data, standards, and apps
- SMARTS implements this new way via RedPen, SparkL, and Pencil, three independent but complementary authoring tools that business analysts can use to express their decision logic
- Business users need business applications, not authoring business rules or developing machine learning models
- SMARTS gives business owners full control of business apps through web forms and point-clicks
- Today change is the rule, with SMARTS, automated decisioning is flexible to accommodate ever-changing regulations, company policies, and market dynamics
If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, or a product configurator, 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. Just email us or request a free trial.
About
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 SMARTS 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 hlaasri@sparklinglogic.com.
Tags: business rules • decision automation • decision management • decisioning • DMN • RPA • rule authoring • SMARTS
Low-Code No-Code Applied to Decision Management
Low-code no-code is not a new concept to Sparkling Logic. From the beginning, the founders wanted to deliver a powerful yet simple product, so that a business analyst could start with data and build decision logic with built-in predictive data analytics and execution decision analytics.
Version after version, they have achieved this vision through SMARTS, an end-to-end decision management platform that features low-code no-code for business analysts and business users to develop and manage decision logic through point-and-click operations.
Low-code development
For business analysts, SMARTS offers a low-code development environment in which users can express decision logic through a point-and-click user interface to connect data, experiment with decisions, monitor execution without switching between different tools to get the job done. Depending on the nature of the decision logic at hand and user preferences, business analysts can choose on the fly the most appropriate representation to capture or update their decision logic. The resulting decision logic is seamlessly deployed as a decision service without IT intervention.
To push the simplification even further, Sparkling Logic founders turned to their customers for inspiration on their needs and developed three complementary technologies:
- RedPen, a patented point-and-click technology that accelerates rule authoring without a need to know rule syntax or involve IT to author the rules
- BluePen, another patented point-and-click technology to quickly create or leverage a data model and put it into production without involving data scientists or IT
- A dynamic questionnaire to produce intelligent front-ends that reduces the number of unnecessary or redundant questions
No-code apps
In addition to low-code development capability for business analysts, SMARTS also elevates the decision logic to a simple web form-based interface for untrained business users. They can configure their decision strategies, test the updated decision logic, and promote the vetted changes to the next staging environment — without learning rules syntax.
These business apps offer a business abstraction for most tasks available in SMARTS related to configuration, testing and simulation, invocation and deployment management, as well as administration.
For example, credit risk specialists can configure loans, credit cards, and other banking products, and pricing specialists can control pricing tables, through a custom business app specific to their industry. The no-code business app enables business users to cope with environment changes whether they are related to internal policies, competition pressure, or industry regulation.
Furthermore, SMARTS tasks can also be automated through orchestration scripts. Business users can trigger these scripts through the click of a button, or schedule them to be performed automatically and seamlessly.
About
Sparkling Logic is a decision management company founded in the Bay Area to accelerate how companies leverage internal and external data and models to automate and improve the quality of enterprise-level decisions.
Sparkling Logic SMARTS is an end-to-end, low-code no-code decision management platform that spans the entire business decision lifecycle, from data import to decision modeling to application production.
Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at hlaasri@sparklinglogic.com.