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Decision Management

Decision Migration Techniques & Best Practices


How to Ensure Your Modernization Project is a Success Part 2

In our previous post, we discussed why migration should be a part of your decision modernization planning process. In today’s post, we’ll cover decision migration techniques and best practices. As a reminder, while this blog series provides tips for any modernization initiative, our focus is on projects related to operational decision-making and decision management.

Decision Migration Techniques

Decision migration projects are similar to other migration projects. In general, there are three techniques with variation in-between:

  • Fully Automated
  • Rule Conversion
  • Manual Rewrite

While we believe a manual rewrite produces the best quality, this technique is also the most labor intensive. Therefore, blending the three techniques will probably be the best approach for your organization. Let’s take a closer look at each technique.

Fully Automated: automatically translate decision logic that is reliable and stable

  • How it works: extract your rules (decision logic code) as data and then apply business logic to convert that data to the decision logic representation of your new system
  • Pro and Cons: fastest technique but will transfer over any errors and ugly code from the old system — the decision logic in the new system may still be difficult for business analysts to understand and manage
  • When to use it: where your decision logic is reliable (error-free) and stable (doesn’t change)
  • What to look for when evaluating new technology: automated import capabilities

Rule Conversion: literally translate decision logic that business analysts are familiar with

  • How it works: manually translate rules from the old syntax to the new syntax
  • Pro and Cons: requires less skills/time than a manual rewrite and can incrementally improve the readability of the rules but prevents you from leveraging the richer features of the new system
  • When to use it: pockets of decision logic that business analysts were already comfortable managing in the old system (ex. a decision table)
  • What to look for when evaluating new technology: expressivity of the language

Manual Rewrite: completely rewrite ugly and outdated decision logic

  • How it works: redesign the data model, simplify logic, and address decision limitations
  • Pro and Cons: enables you to take advantage of the richer features of the new system to produce the best quality (redesign decisions and decision management for business analysts) but requires a significant upfront investment (skilled labor)
  • When to use it: crucial areas of your decision logic that currently cannot be effectively managed by business analysts
  • What to look for when evaluating new technology: verification capabilities

A way to blend manual rewrite with fully automated could be to focus the redesign on the decision flow (sequence of decision steps). Once the decision flow has been established, the rules that operate at each decision step can be fully automated from the old system. Regardless, determining your migration approach will take some time (and why migration should be a part of your decision modernization planning process).

Decision Migration Tips

To help you plan and speed up your migration approach, here are a few tips:

  • Start with the data model: Whether you decide to keep your existing data model or create a new one, you should look for opportunities to augment. In addition, establish a team early on to mine your database for edge cases and other examples that can be used for regression testing.
  • Get rid of ugly code: Where possible, remove concepts that are confusing such as null, nested logic, and string assembly. Instead, replace them with syntax (in place), business terms, calculations, and functions. In other words, the more expressive you can be, the better.
  • Define expectations ahead of time and test-as-you-go: Does an outcome on the new system have to exactly match the outcome of the old system or can there be variability? Once you establish expectations, be sure to test as you build decision logic (makes it easier to identify errors than waiting til the end to QA), using the edge cases that you identified early on.

Through proper planning and utilizing the tips we mentioned, you can avoid dragging out your migration project for months, if not years. For example, one of our bank clients was able to migrate a multi-channel credit origination system onto our decision management platform in 2 weeks! But more importantly, because they understood the importance of testing, they were able to improve the quality of their decisions from the get-go. In our final post, we’ll go deeper into regression testing and business verification during migration and beyond.

Want to learn how SMARTS empowers business analysts to make smarter, faster operational decisions? Contact us today to request a custom demo.

Decision Migration & The “Golden Rule”


How to Ensure Your Modernization Project is a Success Part 1

When it comes to modernization, migration is often overlooked in the planning process. It’s no wonder that digital transformation failure rates have been upwards of 70% according to major consulting firms. Recently, Carole-Ann Berlioz, Co-Founder and Chief Product Officer at Sparkling Logic shared her insights from client migration projects in our webinar An End-User Approach to Modernizing Operational Decision-Making. In this 3-part series, we dive deeper into the webinar key takeaways:

  • First, include migration support capabilities as part of your new technology evaluation
  • Second, pre-plan the migration project so it doesn’t take years
  • Finally, test, test, test!

While the blog series provides tips for any modernization initiative, we will focus on projects related to operational decision-making and decision management.

The Case For Decision Modernization

Operational decisions are the day-to-day, repetitive, and often high-frequency decisions that keep an organization running and impact an organization’s bottom line. Examples include:

  • In Insurance, whether or not to accept an insurance claim
  • In Financial Services, how to price a credit offer for an applicant
  • In Retail, how to efficiently get your products to your retail locations
  • In Healthcare, what treatment plan to recommend to a patient

In an effort to gain more operational efficiencies, organizations have invested in automating many of these operational decisions. However, more often than not, the focus was on processes and not on the decision-making itself. As a result, the code responsible for decision-making is typically buried within various systems. Consequently, managing decisions is cumbersome and requires IT intervention. Even small changes to decision logic would require going through the system development life cycle, preventing organizations from adapting quickly to market, regulatory, and other external changes. Therefore, many decision modernization projects involve isolating the code responsible for executing decision logic and treating it as a separate asset to enable the business to:

  • Ensure the consistency of decisions
  • Rapidly adjust decision logic
  • Improve decision quality over time

Why Migration Should Be a Part of Your Decision Modernization Planning Process

From use-case specific technologies like loan origination software to universal decision management platforms like SMARTS, there are many technologies available to choose from. Regardless of whether you build or buy, migration should be front and center of your modernization planning process. That’s because how you migrate will determine how well your new system will serve the end user — the business analysts responsible for managing those operational decisions.

This brings us to what we call the “Golden Rule” of Decision Migration: think like a Business Analyst because the future of decisions lies in their hands. And in our experience, the #1 thing business analysts want to avoid is code. In our next post, we will cover decision migration best practices to minimize code.

Want to learn how SMARTS empowers business analysts to make smarter, faster operational decisions? Contact us today to request a custom demo.

Decision Management in Manufacturing


Expediting and Optimizing Design for Manufacturing and Assembly (DFMA) in Automotive

The Complexities of Design

As automakers go electric, more lithium batteries are needed to power their electric vehicles. A lithium battery is actually a battery pack, comprised of:

  • multiple batteries with
  • multiple cells
  • a battery management system to control and monitor its performance

Each of these components require multiple raw materials and manufactured parts. In the case of electric vehicles, manufacturers must customize these components to meet the specifications of the automaker for a specific make and model. Specifications typically include:

  • dimensions (length, width, height)
  • voltage and power capacity
  • quantity, expected delivery date, and price range

Therefore, designing a lithium battery is as much a supply chain, manufacturing, and assembly forecasting exercise as it is a mechanical engineering one.

Managing Complexity through Decision Management

The product engineering field has developed different methodologies to incorporate cost and time-to-market into the design, including:

  • Design for Manufacturing (DFM)
  • Design for Assembly (DFA)
  • Design for Manufacturing and Assembly (DFMA)

However, executing these methodologies is time-consuming and often sub-optimal. One of our clients, a leading custom battery manufacturer, turned to our SMARTS Data-Powered Decision Manager to expedite and optimize the design process. Through business rules and lookup models, our client is able to:

  • define the relationship between the materials, suppliers, and manufacturing processes (ex. forging, bending) of a particular cell model
  • coordinate the relationship between cells, batteries, and battery packs (ex. physical dimensions, voltage, power capacity)
  • quickly produce a design decision (includes cell model and quantity, battery pack layout, price quote) based on an automaker’s requirements

As a result, the battery manufacturer is not only delivering a better customer experience but also minimizing internal costs.

In addition, SMARTS intuitive user interface makes it easy for business analysts to organize and manage the business rules underlying their design decision. This allows the battery manufacturer to adapt quickly to market changes. Whether certain suppliers undergo price changes or no longer supply a certain material, business analysts can easily adjust business rules accordingly.

Want to learn how SMARTS can help your organization make smarter, faster operational decisions? Contact us today to request a custom demo.

On Decision Representation


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 ten most frequently asked questions about decision management


Decisions are at the core of every organization, be it a Fortune 100 company, a start-up, or a governmental agency. In this blog post, we provide answers to the ten most frequently asked questions about decision management.

1) What is decision management technology?
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. The second are normative in that they implement regulations, policies, or strategies regardless of the beliefs or preferences of those who follow the decisions. There is no single definition that differentiates the two technologies. But there is a consensus to name the second decision management. So, when you hear or read someone referring to decision management, think of technologies implementing formal laws, industry regulations, company policies, and business strategies.

2) What is the difference between business rules and decision management?
Business rules implement industry regulations, business policies, or subject matter knowledge in the form of if-then statements or conditional action procedures. To execute, a rule engine checks all the predicates/conditions and fires the statements/procedures. To select which statement to add or procedure to run, the rule engine relies on heuristics that are part of the industry regulation, business policy, or subject matter.

Decision management is more than that. Behind the terminology of decision management, we would find multiple technologies. The simplest are decision tables, trees, and graphs. The most sophisticated combine business 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.

3) What types of decisions do decision management technologies automate?
Like any technology, decision management technologies 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 used.

Decision management technologies 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 a decision management product for the operational and day-to-day decisions that companies make in the thousands and sometimes millions in a single day.

4) In which industries are decision management technologies primarily used?
Decision management technologies are primarily used by companies 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. But you can find them in other industries such as telecommunications for network, service, or customer management, in retail for product recommendation, and even in media for content personalization.

5) For what applications are decision management technologies used?
Although not dedicated to finance, insurance, and healthcare, decision management technologies are widely used 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.

Decision management technologies can also be used for data transformation as a better alternative to scripting languages to move, unify, and enrich data from one layer to another of a data platform or marketplace. In fact, decision management technologies can be used to automate every complex non-linear process such as the ones we find in product configuration or condition-based diagnosis.

6) Who are the users of decision management technologies?
The users of modern decision management systems are not IT people but businesspeople. 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.

7) How is decision management related to data analytics?
There are three types of data analytics. First, descriptive analytics that allows companies to get a status of how they are performing against their goals. Then, predictive analytics whose scope of analysis is no longer just on what had happened in the past months and years, but on what might happen if there are no significant changes in industry regulations, market dynamics, and company strategy. Then, prescriptive analytics that transforms insights from both descriptive and predictive analytics into decisions and actions with the support of decision management technologies. In this sense, decision management technologies operationalize data analytics.

8) Is decision management data-based or knowledge-based?
The “data vs. knowledge” debate is 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.

9) What are explainable or understandable decisions?
As more and more of our personal, professional, and social activities are managed by data and algorithms, bias and discrimination become a big concern for companies, particularly those operating in highly regulated industries such as credit, insurance, and healthcare. Decisions, whether for eligibility, pricing, recommendation, or personalization must be understood by all the stakeholders —not only by the business, credit, or risk analyst, but also by the customer-facing businesspeople and the customer. SMARTS’ latest version, Vienna, implements understandable decisions through additional visual features that go beyond intuitive business rules writing. For instance, users can use the play-by-play feature to watch the decision happen before their eyes while refining the expected behavior.

10) How do decision management technologies avoid bias?
In our vision, one of the best ways to reduce biases, is to make decisions explicit (like the rules of laws) so that those who implement the decisions can test them out, one at a time or in groups, and visualize their outcomes in dashboards. Business rules with dashboards help to detect the consequences of decisions before putting them into production. Decisions should be kept separate from the rest of the system calling those decisions — the CRM, the loan origination system, the credit risk management platform, etc.

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.

If you envision modernizing or developing a decision management application, we can help. Just contact us or request a free trial.

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.

The SMARTS Way for Personalization


SMARTS 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.

What our customers say about SMARTS


What our customers say about SMARTS

You are about to modernize or develop a new automatic decision-making application, based on data, knowledge, or a combination of both. You wonder whether our SMARTS platform meets your business needs and technical specifications. Nothing is better proof of a product’s superiority than the testimonials of customers. So, we have selected some customers here so you can see why they chose SMARTS as their decision management solution.

Equifax, consumer credit reporting

Equifax is a global data, analytics, and technology company that serves financial institutions, corporations, government agencies, and individuals with enriched data and executable insights. These insights are typically derived from many data sources including financial, telecommunications and utility payments, employment, and income data.

Equifax chose SMARTS as a core part of the Equifax InterConnect platform. Credit and risk analysts, both at Equifax and their customers, can seamlessly import data, capture decision logic, A/B test and analyze how the decisions apply to each transaction, and measure the collective impact of making changes to decisions. Business users and business analysts, both at Equifax and their customers, can autonomously make changes to policy rules.

Testimonial
“SMARTS is at the heart of our InterConnect platform. It has helped us quickly enter an era where the credit process must be smooth, automated and optimized for lenders and consumers. SMARTS’ all-in-one approach to authoring, testing and deploying business rules into a sophisticated yet simple product appealed to us from the start. Since then, we have been very satisfied with the use we make of it on a daily basis” — Deepesh Mohandas, Vice President, Global Product Management, Decisioning Platforms.

Learn more about SMARTS at Equifax.

NICE Actimize, suspicious financial activity monitoring

NICE Actimize is the largest and broadest provider of financial crime, risk, and compliance solutions for regional and global financial institutions, as well as government regulators. Consistently ranked as number one in the space, NICE Actimize experts apply innovative technology to protect institutions and safeguard consumers’ and investors’ assets by identifying financial crime, preventing fraud, and providing regulatory compliance.

Integrated with NICE Actimize’s platform, SMARTS supports financial institution customers by making more rapid decisions on financial crime strategies and providing the ability to view the overall impact across all NICE Actimize analytics. This capability allows X-Sight customers to make more informed decisions while maximizing financial crime coverage and controlling costs.  

Testimonial
“This technology partnership delivers our X-Sight customers more rapid development and deployment of financial crime management strategies across the broader NICE Actimize analytics ecosystem” — Craig Costigan, CEO.

Learn more about SMARTS at NICE Actimize.

Percayso Inform, insurance intelligence

Percayso Inform is an insurance intelligence provider whose services go beyond traditional data enrichment, providing unique, real-time solutions at all stages of the insurance lifecycle and delivers unrivaled insight into insurance customers, risk, and fraud.

Powered by SMARTS, Percayso Inform Manager allows insurance providers of all shapes and sizes from start-ups to global insurance businesses across personal and commercial lines to build, adapt and optimize their own data enrichment, rating, and intelligence strategies dynamically and intuitively. It enables high volume data ingestion, rules configuration, operational and strategic decision management as well as a string of valuable features such as reporting, dashboards, champion challenger, data manipulation and more.

Testimonial
“Sparkling Logic stood out from the crowd. The functionality, configurability and ease of use of SMARTS ticked all our boxes but what really impressed us was the genuine desire by their team to create a true partnership and build a foundation from which we could both grow together” — Richard Tomlinson, Managing Director.

Learn more about SMARTS at Percayso Inform.

Enova Decisions, data analytics and decision management

Enova Decisions is an analytics and decision management technology company that was formed in 2016 to enable businesses to automate and optimize operational decisions through data, AI, and the cloud- in real-time and at scale.

Testimonial
“Leveraging technologies like Sparkling Logic in our cloud service allows our clients to oversee the fine details of the decision algorithm without feeling overburdened by the complexity of what they’re designing” — Sean Naismith, Head of Analytics Services at the time.

Learn more about SMARTS at Enova Decisions.

LTCG, insurance business processing

LTCG is a leading provider of business process outsourcing for the insurance industry. The largest insurers rely on our unparalleled expertise to help manage their complex long-term care portfolios and maximize financial performance.

LTCG evaluated tool and platform options and selected SMARTS as the decision engine for their claims adjudication system. They developed and implemented the new system and have since extended their use of SMARTS to support additional business processes.

Testimonial
“Thanks to SMARTS, we were able to discover, test, and deploy automated claims decision logic in under six months” — Kyle Korzenowski, CIO at the time.

Learn more about SMARTS at LTCG.

ABT, power management

ABT Power Management, now part of Concentric, furnishes, engineers, installs, and services industrial batteries and charging systems and is a recognized industry leader in material handling power management.

Testimonial
“Now, using SMARTS, we have achieved near real time analysis of this [sensor] data and are able to respond immediately to conditions that need attention. SMARTS’ interface is easy and intuitive enough to allow our engineering staff to create and maintain the rules themselves” — Mike Shemancik, CIO at the time.

Learn more about SMARTS at ABT.

First Rate, wealth management

First Rate is the UK’s largest supplier of foreign currency and a top 5 currency wholesaler globally. They are one of the foremost FX experts in the industry, with a multi-billion-pound wholesale business and over 10 years’ trusted experience providing tailor-made travel money solutions for companies in the finance, travel, and retail sectors.

Testimonial
“We chose SMARTS for its comprehensive decision management environment with out-of-the-box integration of business rules, and predictive analytics, and focus on decision improvement” — Nick Collins, Head of Business Solutions at the time.

Learn more about SMARTS at First Rate.

Onlife Health, patient-centric care management

Onlife Health, a GuideWell company, brings simplicity to population health and wellness, connecting and integrating people, technology, and benefit design through a user-friendly engagement platform, guiding members on the “next right thing to do” in their healthcare journey.

Testimonial
“We are able to easily change our decisions as business needs dictate and deploy these changes without going through the full software change process.” — David Jarmoluk, Vice President of Enterprise Solutions at the time.

Learn more about SMARTS at Onlife Health.

Do you want to learn more or test SMARTS yourself? 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.


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