What is Decision Management?
Decision Management 101 Part 1
Our Decision Management 101 series is designed for business leaders who are unfamiliar with decision management and are currently facing challenges with automation, adaptability, and scalability. In this series, we address the following:
- What is decision management?
- How can decision management address business growth challenges?
- What to look for in a decision management system (DMS)
Business processes and business rules keep your business running
Before we dive into decision management, we need to talk about business processes and rules. Processes and rules drive an organization’s day-to-day operations. Business processes are the procedures necessary to accomplish a task. Business rules are the policies that govern activity. In other words, business processes inform employees and machines what to do and business rules inform them how to do it. Together, they are the instructions for how a business gets things done. Examples of business processes and rules include:
- How to price a product or service
- How to handle customer complaints and issues
- How to process an invoice
For example, a process for pricing products for a customer may be as follows:
- Calculate cost for each item
- Apply appropriate margin
- Apply relevant discounts
And the rules may be as follows:
- Cost of Product A = $100, Cost of Product B = $200…
- Margin of Product A = 30%, Margin of Product B = 20%…
- If Total Cost > $500, apply 10% discount
When process automation prevents effective rules management
Traditionally, the goal of automation has been operational efficiency and therefore the focus has been on process. Organizations typically define and automate process workflows and then hard-code rules within those workflows. The result is an automated yet rigid system that is managed by IT rather than the subject matter experts and business analysts that are responsible for business outcomes. Over time, business rules management systems have been developed to make it easier to manage and update business rules. However, these technologies keep ownership of business rules with IT.
Decision management gives businesses more control and agility
Decision management is a discipline and set of technologies designed to bring management of business rules back to the business side of an organization. Rather than starting with business rules, decision management starts with a decision outcome and then considers all the data and business rules that must be in play to achieve that outcome. Decision management systems separate this decision logic from the applications that automate business activity and make it easy for non-coders to define, test, and deploy decision logic. As a result, the business owners responsible for a particular decision outcome can effectively manage business rules without needing to go through IT.
Decision management systems not only enable an organization to automate costly and repetitive decisions, but also enable an organization to:
- Ensure the consistency of decisions
- Rapidly adjust decisions as needed
- Improve the quality of decisions over time
Want to learn how SMARTS gives your business more control and agility? Contact us today to request a custom demo.
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 debateThe “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 situationThere 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 waySMARTS 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.
- 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.
AboutSparkling 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 email@example.com.
Our customers — who they are, what they want, and what we bring them
As an enterprise IT solution, SMARTS has different customers in the same organization who directly use it or indirectly benefit from it. This blog post aims to succinctly describe who our customers are, what they want, and what value we bring to each of them that matches their unique needs.
In our terminology, these are the customers in the organizations who design, author, deploy, and update decisions according to the company’s policies and industry’s directives.
When they looked for a decision management solution, they looked for product simplicity and rich functionality. More importantly, they wanted autonomy once the solution was in place.
After a few weeks after training on SMARTS, our business analyst customers reported that they very much liked to have data, models, and business rules in the same tool. They enjoyed how we succeeded in managing SMARTS’ evolution to have both richness and easiness in the same product. They also enjoyed being able to quickly author, test, deploy, run, monitor, and change decisions. Their experience with SMARTS was a joy as they could focus on the decisioning process and its outcomes instead of the technology to implement it.
Business users are the people who run, monitor, and manage the performance of the business. In our case, they are the internal customers of business analysts. They are the ones who use the solution daily.
They wanted to know how easy it will be for them to monitor decisions built by business analysts and make the necessary changes when the actual performance may deviate from the expected performance.
After using SMARTS, business users reported the following benefits: Quick change-test-deploy-run cycles, being able to work without coding and with no prior knowledge of machine learning or business rules, just with their knowledge of the business and using web forms and point-and-click.
By IT, we designate IT the people who install and connect the solution to the rest of the organization’s IT system. They asked for integration, performance, security, and fit with the IT global architecture and governance.
They want to have business analysts and business users to be autonomous but at the same time being able to monitor the solution as the rest of the IT infrastructure.
IT people liked all the performance, security, integration, and scalability we promised. They also appreciated SMARTS adherence to the enterprise IT architecture and governance as expected. They liked how easily they could deploy SMARTS on premises or in the cloud. Finally, they also very much liked to have no additional development or changes in the current applications.
These are the people who develop and manage models using data science libraries through languages such as Python, R, SAS, and SPSS.
They are not direct users, but they were willing to see their models fully operational into the new solution while they continue their effort on enhancing existing models and experimenting with new ones.
Thanks to SMARTS, they were able to know the performance of their models in production with real data and transactions. SMARTS was an effective demonstrator of their models.
In our case, these are the people who head organizations or verticals where decisions are at the core of their operations, throughout all the organizations activity. Their attention is “more revenue, less cost, and why not both!”
They wanted to hear about similar successful implementations in their market, in particular the time it would take to recoup their investment in the new solution, and the strategic advantages it will provide them after one year or two in production.
To management people, we brought strategic benefits. They could operate the business under a decisioning process that implements the business strategy. Their organization could finally make informed, error-free, and unbiased decisions. And they were insured that the decisions taken were in full compliance to internal policies and industry regulations.
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 an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines business rules with predictive models to create intelligent decisioning systems.
If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, a product configurator, a condition-based maintenance application, or such applications, SMARTS can help. Just contact us or request a free trial.
Decision Management Systems: What Are They and When to Use Them?
The purpose of this blog post is to succinctly introduce decision management systems for those who are new to the field. First, I present what decision management is, then the technologies that make it up. I also present where they are used and what they bring to the companies that use them.
What is decision management?There are two types of decision-making technologies. The first are descriptive in that they implement how people make choices among alternatives based on their beliefs and preferences. Think of a doctor deciding a treatment following a diagnosis or a trader buying an asset following a predictive model. The second are normative in that they implement regulations, policies, or strategies regardless of the beliefs or preferences of those who follow the decisions. Think of a loan officer deciding based on an applicant’s repayment history or an insurer calculating the premium an applicant should pay based on the applicant’s medical condition.
There is no single definition that differentiates the two technologies. You have heard of knowledge-based systems, expert systems or reinforcement learning for the former technologies and decision tables, decision trees or business rules for the latter. But there is a consensus to name the second type of technologies decision management systems. So, when you hear or read someone referring to decision management, think of prescriptive methods, technologies, products, and systems implementing formal laws, industry regulations, company policies, and business strategies.
What are the underlying technologies?Often decision management systems are confused with business rule engines, but they are more than that. Behind the terminology of decision management systems hide multiple technologies. The simplest are decision tables, trees, and graphs. The most sophisticated combine rules and predictive models. If we take the example of SMARTS, it integrates eight decision engines into the same platform. Depending on the problem at hand, one may choose one or the other, or even combine them in the same set-up.
Also, the users of modern decision management systems are not IT people anymore but businesspeople instead. So, they come with features that allow non-specialists to use them without IT intervention. With modern decision management systems, IT only takes care of the first installations and configurations, the systems come with everything necessary to ensure the governance and security of the applications developed as well as the ease of integrating them into the corporate IT architecture. If we take the example of SMARTS again, it comes with an easy-to-use graphical authoring interface, pre-deployment rule testing, rule repository with version control and rollback, large-scale simulation, real-time decision performance monitoring, and much more.
Where decision management systems are most used?Like any technology, decision management systems are not a one-size-fits-all solution for every decision problem. They are not suitable for long-term or midterm slow decisions that companies make once a year or a quarter. In these cases, optimization technologies are more used. They are not also suitable for cases with uncertainty and where probabilistic technologies such as probabilistic graphical models are more used.
Decision management systems are best suited when there is a substantial number of decisions and calculations that are often nested, often invoked, and likely to change often. Therefore, one must consider decision management systems for the operational and day-to-day decisions that companies make in the thousands and sometimes millions in a single day. We find these cases in banking for credit risk assessment, in insurance for premium calculation and even in retail for product configuration.
Although not dedicated to finance, insurance and healthcare, customers of SMARTS have widely used it for loan origination, risk management, fraud detection and money laundering prevention. These are typically cases where organizations make decisions and calculations thousands and sometimes million times a day and may change based on the market dynamics or global economy, or updates to regulations or business strategy.
What are the key benefits of using decision management systems?Decision management systems come with two critical benefits. As decisions are explicit, they make it possible to understand and explain the decisions implemented so that one can change them more easily when a new situation requires it. They also reduce the risk of errors and biases met in customer-facing applications, such as recommendation, credit, or insurance systems based solely on machine learning.
- There are two sets of decision-making technologies. The first is descriptive. The second is prescriptive. Decision management systems are descriptive in that they implement formal laws, industry regulations, company policies, or business strategies.
- Decision management systems are best suited when there are thousands or millions of decisions and calculations that are often nested, often invoked, and likely to change often.
- The power of modern decision management systems lies less in the engines but more in the features surrounding them to enable businesspeople take full control of the decisioning process without heavy IT intervention beyond first installation and default configuration.
- Decision management systems have two key benefits. They ease the integration of changes in regulations, policies, and strategies. They also reduce errors and biases in customer-facing applications.
Where to look for further information?We write regularly about decision management systems and SMARTS. You can find quite a bit of information on our blog and webinars. You can also download our white paper, request a demo of SMARTS, or try it.
AboutSparkling 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 an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines business rules with predictive models to create intelligent decisioning systems.
Hassan Lâasri is a data consultant and interim executive, now leading marketing for Sparkling Logic. You can reach him at firstname.lastname@example.org.
The SMARTS way for micro-calculations
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.
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.
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 email@example.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 historyThe 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.
BasicsDMN 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 blocksIn 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 variablesInput 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 ExpressionsA 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 AdoptionWith 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.
AboutSparkling 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 firstname.lastname@example.org.
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.
SMARTS Real-Time Decision Analytics
In this post, I briefly introduce SMARTS Real-Time Decision Analytics capability to manage the quality and performance of operational decisions from development, to testing, to production.
Decision performanceH. James Harrington, one of the pioneers of decision performance measurement, once said, “Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.” This statement is also true for decision performance.
Measuring decision performance is essential in any industry where a small improvement in a single decision can make a big difference, especially in risk-driven industries such as banking, insurance, and healthcare. Improving decisions in these sectors means continuously adjusting policies, rules, prices, etc. to keep them consistent with business strategy and compliant with regulations.
Decision performance management in SMARTSSMARTS helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation — thereby reducing errors and bias. To this end, we added a real-time decision analytics capability to the core decision management platform.
Currently used in financial and insurance services, it helps both business analysts and business users to define dashboards, assess alternative decision strategies, and measure the quality of performance at all stages of the lifecycle of decision management — all with the same interface without switching from one tool to another.
Development. From the start, SMARTS focuses the decision automation effort on tangible business objectives, measured by Key Performance Indicators (KPIs). Analysts and users can define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture their decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. They can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.
Testing and validation. During the testing phase, analysts and users have access to SMARTS’ built-in map-reduce-based simulation environment to measure these metrics against large samples of data. 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.
Execution. By defining a time window for these metrics, business analysts can deploy them seamlessly against production traffic. Real-time decision analytics charts display the measurements and trigger notifications and alerts when certain thresholds are crossed or certain patterns are detected. Notifications can be pushed by email, or generate a ticket in a corporate management system. Also, real-time monitoring allows organizations to react quickly when conditions suddenly change. For example, under-performing strategies can be eliminated and replaced when running a Champion/Challenger experiment.
Uses casesInsurance underwriting. Using insurance underwriting as an example, a risk analyst can look at the applicants that were approved by the rules in production and compare them to the applicants that would be approved using the rules under development. Analyzing the differences between the two sets of results drive the discovery of which rules are missing or need to be adjusted to produce better results or mitigate certain risks.
For example, he or she might discover that 25% of the differences in approval status are due to differences in risk level. This insight leads the risk analyst to focus on adding and/or modifying your risk related rules. Repeating this analyze-improve cycle reduces the time to consider and test different rules until he or she gets the best tradeoff between results and risks.
Fraud detection. An other example from a real customer case is flash fraud where decisions had to be changed and new ones rolled out in real time. In this case, the real-time decision analysis capability of SMARTS was essential so that the customer could spot deviation trends from normal situation directly in the dashboard and overcome the flood in the same user interface, all in real time.
Without this built-in capability, the time lag between the identification of fraud and the implementation of corrective actions would have been long, resulting in significant financial losses. In fact, with SMARTS Real-Time Decision Analytics, the fraud management for this client has gone from 15 days to 1 day.
Marketing campaign. The two above examples are taken from financial services but SMARTS real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules, such as in loan origination, product pricing, or marketing promotion.
In the latter case, SMARTS can help optimize promotion in real-time. Let’s say you construct a series of rules for a marketing couponing using SMARTS Champion/Challenger capability. Based on rules you determine, certain customers will get a discount. Some get 15% off (the current offering — the champion), while others get 20% (a test offering — the challenger). And you wonder if the extra 5% discount leads to more coupons used and more sales generated. With SMARTS real-time decision analytics environment, you find out the answer as the day progresses. By testing alternatives, you converge to the best coupon strategy with real data and on the fly.
ConclusionAs part of the decision lifecycle, business analysts obviously start by authoring their decision logic. As they progress, testing rapidly comes to the forefront. To this end, SMARTS integrates predictive data analytics with real-time decision analytics, enabling business analysts and business users to define dashboards and seamlessly associate metrics with the execution environment — using the same tool, the same interface, and just point and click.
- SMARTS comes with built-in decision analytics — no additional or third-party tool is required
- You can define metrics on decision results so you can measure and understand how each decision contributes to your organization’s business objectives
- Decision metrics enable you to assess alternative decision strategies to see which should be kept and which rejected
- SMARTS add-on for real-time decision analytics lets you monitor the decisions being made and make adjustments on the fly
- SMARTS’ real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules
AboutSparkling Logic is a decision management company founded in the San Francisco Bay Area to accelerate how companies leverage data, machine learning, and business rules 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.
Carlos Serrano is Co-Founder, Chief Technology Officer at Sparkling Logic. You can reach him at email@example.com.