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Understandable Decisions with Vienna, the new version of SMARTS


Understandable Decisions with Vienna
This blog post presents the latest version of SMARTS, Vienna. Vienna expands SMARTS capabilities by strengthening the ease-of-use and clarity of implementing business decisions, allowing business analysts to more effectively manage complex, automated business decisions. The new version demonstrates continued innovation in the pursuit of Understandable Decisions, an integral part of Understandable AI.

Introduction

At Sparkling Logic, innovation never stops. Version after version, we push the boundaries of simplifying decision management technologies to make it even easier for business people to use them without heavy training in data analytics, machine learning, and business rules. The new version, Vienna, is no exception to the rule: it comes with a multitude of innovations that we group under the name of “understandable decisions.”

After Uhusia, here is Vienna

Since its creation, SMARTS has provided context to business analysts authoring their decision logic. They have been able to instantly see the impact of changes in their business strategies. With the Vienna version, Sparkling Logic has taken this approach to the next level, as their users can now better understand and explain to the team how the decisioning operates step by step. The dual objective is to ensure that the current logic complies with requirements, but also to foster conversation on ways to improve it over time.

To accelerate the implementation and understandability of decisions, Vienna comes with new enhancements and innovations for all the stakeholders.

For business analysts who build the decisioning application, Vienna further simplifies the low-code environment to easily combine data augmentation, pre- and post-data acquisition decisioning, and model operationalization. It also further simplifies the creation, visualization, testing, and debugging of decisions, and therefore reduces errors and biases in decisioning. In addition to these simplifications, Vienna adds new high-level expressions to make decision logic more compact and therefore easy to understand and modify. Decision tables, decision flows, and lookup models have all been affected by these enhancements, enabling projects, large and small, to take full advantage of these improvements.

For business users who operate the decisioning application, Vienna adds dedicated interfaces for them to not only author, but also test, promote, measure, and experiment on their decision logic. With an increasing number of automated tasks within the tool, business users’ productivity rises to higher levels. Business rules can drive the verification and processing of decision logic changes, automatically and seamlessly.

For IT people who first install SMARTS, Vienna comes with new interfaces to connect even more easily to external systems and services, whether on-premises or in the cloud. It also introduces new versions of all the REST decision service invocation SDKs, as well as for the .NET framework and . NET core decision components.
The Vienna beta-testing program has proven to be a success, as customers have provided significant input in the fine-tuning of the new capabilities. Sparkling Logic recognizes and thanks all the companies that actively participated.

Wrap-up of Vienna

  • Further simplification of the low-code environment to easily combine data augmentation, pre- and post-data acquisition decisioning, and model operationalization
  • Augmented user interface to further simplify the creation, visualization, testing, and debugging of decisions, and therefore reduce errors and biases in decisioning
  • New high-level expressions, making decision logic more compact and therefore easy to understand and modify
  • Dedicated interfaces for untrained users to author, test, promote, measure, and manage business apps
  • New interfaces to connect even more easily to external systems and services, whether on-premises or in the cloud

If you want to learn more about the new version of SMARTS, register for this webinar or contact us for a demo or 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.

SMARTS for data marketplaces


SMARTS for data marketplaces

In my earlier blog post, I explained how decision management and business rules were suitable for micro-calculations, the type of computations that businesses often codify into large spreadsheets and use to score, rate, or price items. In this post, I explain how they are also suitable to simplify data integration, aggregation, and enrichment when building and running data marketplaces.

Data marketplaces

Data marketplaces are a shift from data warehouses where the goal is not only to store large volumes of data, but to make that data be consumed as a service without resorting to IT or prior knowledge of a query language. Data marketplaces are often organized into three layers:

  • At the lowest layer, we find raw data stored in the form in which it was ingested from the data sources. Data sources can be global ERP and CRM systems, or even local MySQL databases and shared Excel spreadsheets.
  • At the middle layer, we find integrated data from multiple sources that is reconciled to resolve disparities and inconsistencies found in the original data. Often, the source systems do not have the same format for dates, names, phone numbers, and addresses. Sometimes the same object can have different attributes in different data sources.
  • At the topmost layer, we find aggregate data expressed in summarized forms, often to inform about groups rather than individuals. It is at this level that we also find data enriched by external data to make them directly consumable by the businesspeople.

To learn more about data marketplaces, I recommend the Eckerson Group white paper: The Rise of the Data Marketplace – Data as a Service by Dave Wells.

Easy to define, hard to construct

Defining a data marketplace as we have just done is simple, its construction is complicated for two reasons:

1) Data heterogeneity. It is not enough to bring together all the company’s data in a data marketplace for the data to be transformed into knowledge, forecasts, and decisions. Indeed, all data does not have the same age, the same structure, the same format, the same quantity, the same quality, and above all the same utility. If an attribute is important for a business line, it is not automatically important for another business line, yet within the same company.

Each business line has its vision of the product, of the customer, and of any entity managed by the various actors of the company. In the luxury sector for example, a dress, a bag, or a piece of jewelry, although it is a unique object, is seen through different attributes according to the databases where this same object is stored. Looking to exploit all the data available in a company to extract predictions and then decisions not only require integration but also transformation, unification, harmonization, and enrichment.

2) Data reorganization. A data marketplace would work better if data, information, and needs were always stable. But in a dynamic and rapidly changing business world, groups are reorganizing, and companies are acquired, merged or separated. For example, to simplify finance reporting, a country can change the region it was in a year ago, and it’s a safe bet that it will change yet again if a new boss is appointed, or a region is split or added. To be successful, data marketplaces must be implemented as change-tolerant projects.

These two reasons are representative of situations where decision management technologies are used: piecing together things that move independently to each other. Under the name of decision management, we group all the technologies that help organizations to automate all those simple but plentiful granular decisions and calculations that businesses often codify into decision tables, decision trees, or business rules.

Decisioning technologies to the rescue

Decisioning technologies can be used here. But one can use a database programming language or a general-purpose scripting language to automate the loading of data from the data sources into the raw data layer if the original format is kept. There is no real value using a business rules engine to do this straightforward job. The value of using a decisioning technology starts at the integration data layer, where attributes of objects are grouped together to form an updated version of an existing attribute or a new attribute.

Take the example of customer data from two different databases. Suppose that customers have their home address and business address in a first source database, and that they only have their home address in a second source database. Suppose also that the format of the addresses is not the same in the two databases. What should the integration data layer hold? An address? So, which one? And what format? Two addresses? So, what professional address to put for these customers who are only present in the second database with the home address? These questions can be easily answered through decision rules.

Now, suppose at the aggregate data layer, we want to add an average turnover with a client that buys from two business units. Here again, calculation rules can be easily used. One can use SMARTS’ look-model engine to automate such calculations.

SMARTS

SMARTS is our all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of micro-decisions and micro-calculations described in this article. SMARTS comes in the form of one product, four capabilities:

SMARTS has been extensively used for decisions and calculations in finance, insurance, healthcare, retail, IoT, and utility sectors. To learn more about the product or our references, just contact us or request a free trial.

Wrap-up

  • Data marketplaces promise to change the way data is consumed by businesses. Contrary to data warehouses, they are more complex to build and therefore deliver on their promises.
  • Decision management technologies simplify data integration, aggregation, and enrichment when building and running data marketplaces. They make decisions and calculations explicit and therefore easy to change whenever situations change.
  • SMARTS supplies multiple graphical representations* and engines to make such transformations, integrations, and enrichment easy to design, implement, test, deploy, and change according to situation changes.

* The following table, tree, and graph show three different representations of the same decision logic so that developers can use one that they are familiar with or that best fits the task at hand.

DecisionDecision treeDecision graph

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 SMARTS 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 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.

SMARTS in credit and risk management


SMARTS in credit and risk management

If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, or a product configurator, our SMARTS decision management platform can help you. Discover it here through a selected list of use cases we consider to be representative of decision management applications in modern credit and risk management, based on data, models, and automation.

If your project is different, just contact us or request a free trial. 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 you satisfied, but also proud of the system you will build.

Selected use cases

In credit and risk management, SMARTS has been used in applications where many data-driven decisions were frequently invoked and decision logic often updated, in response to changes in industry regulations, market dynamics, and business strategy. For this blog post, we select some applications that our customers have built with SMARTS.

Credit origination
An American rating agency has integrated SMARTS into its origination platform to help its corporate clients manage their credit risks, from screening to closing. With SMARTS, the agency manages credit risk for 4 of top 5 telcos and 30 of top 40 banks.

Credit risk management
A Chinese financial services provider uses SMARTS as the engine for its credit risk management from customer registration and identity identification to credit scoring and amount calculation to loan approval and money transfer. With SMARTS operational, the fintech company increased loan volumes to over 38 million lending transactions with greater control over its business risk.

Deposit risk management
A consortium of US banks specializing in deposit risk management measured SMARTS simulations of 1 billion transactions on 4 cores in less than 42 minutes, enabling the consortium to execute their decisions and compute complex business metrics beyond the traditional statistical means, variances, and deviations.

Flash fraud detection
A global online payment platform used BluePen for fraud detection. Since the deployment of the model, the detection time of a fraudulent transaction has been reduced from two weeks to less than a day, and the saving amounts to $10M’s per ongoing flash fraud.

Insurance claims adjudication
A major US-based third-party administrator for long term care insurance products uses SMARTS as the decision management engine for the company’s claims adjudication system, which processes 90,000 claim decisions per month over 1.3 million policies. Development and deployment took less than 6 months.

Healthcare insurance
A global risk platform company has used SMARTS to create, test, validate and put into production COVID-19 conditions for its drug prescriptions for more than 500,000 policyholders, located in more than 10 countries. Full development from specification to production took less than 12 months.

Life insurance underwriting
A Chinese life insurance company uses SMARTS so that all the underwriting rules and nearly 70% of the claims rules are managed by business experts, without calling on the IT department to update the rules. This allowed IT to focus on the reliability and availability of the system. Additionally, updating rules now takes no more than an hour from development to production.

Benefits

As reported by our customers, credit and risk analysts were able to leverage data and scoring models to intuitively build credit and risk management applications that can easily evolve with the business activity, internal policies, and industry regulations.

They also benefited from SMARTS agility and flexibility, giving them the ability to configure and refine decision logic, test, simulate decision services, experiment, choose decision strategies, and finally publish and manage deployment. Credit and risk analysts were able to participate in the entire solution lifecycle through web forms and point-and-click interfaces, without the sole reliance on IT.

On the other hand, IT had all the required performance, security, integration, and scalability capabilities to fit their enterprise architecture and governance without additional development or changes in the current applications. SMARTS was delivered in the form of a containerized product ready to install, deploy, and run as part of an interactive system, a service to invoke in a service-oriented environment, a program to call in a message-oriented environment, or a batch processing application.

To explore more, we invite you to visit our blog, webinar, resources, and demo pages where you can learn about SMARTS capabilities, features, and tools that make it an all-in-one low-code platform for building smart decisioning applications without a heavy involvement from IT beyond first installation.

Further reading

Sparkling Logic SMARTS in 10 Questions and Answers, a recent blog post that presents SMARTS all-in-one decision management platform through the 10 most asked questions and their responses.

Sparkling Logic: Decision Making Rendered Simple and Holistic, a “30,000-foot view” of SMARTS, Sparkling Logic, Inc’s low-code digital decision-making platform by CIOReview magazine.

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 SMARTS 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.

Sparkling Logic SMARTSTM in 10 Questions and Answers


Sparkling Logic SMARTS 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.

Software industry trends behind the digital transformation revolution


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

Introduction

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

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

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

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

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

DevOps

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

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

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

Low code / no code automation

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

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

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

Vertical integration with digital decisioning

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

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

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

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

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

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

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

About

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

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.


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