Decision Management
Regression Testing and Business Verification
How to Ensure Your Modernization Project is a Success Part 3
In our previous post, we suggested that you establish a team early on to mine your database for edge cases and other examples that can be used for regression testing, define expectations ahead of time based on those examples, and test-as-you-go. In our final post of this series, we’ll go deeper into testing.
Why is Regression Testing So Important?
Regression testing is done to validate that an application functions as expected after a code change or other update. In decision migration projects, regressions tests ensure that the decision outcomes in your new system match your expectations. What’s important to remember is that you’re comparing decision outcomes and not the rules themselves.
Another reason that regression testing is a must-do is that it will help you identify what we call “logic band-aids.” These are those messy workarounds that you had to do outside your legacy system to make it work properly. Usually these workarounds exist because your legacy system lacked enough expressivity and/or functionality on its own. Logic band-aids can live in various places, including your code, your database, or your UI. Finding them is one of the hardest parts of the migration project. Regression tests can clue you in on where to look. And once you find them, you should replace them with easy-to-understand decision logic on the new system.
Regression Testing Best Practices
Regression testing can be time consuming, so here are ways that you can speed up the process (and your overall migration project):
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Include regression testing capabilities as part of your new technology evaluation:
As regression testing should not only be a part of your migration process but also an ongoing part of decision management, your new system should allow you to run regression tests easily. What to look for include the ability to run hundreds of thousands (if not millions) of test cases quickly, the ability to easily filter out test cases where outcomes do not meet expectations (ex. add QA flags), and the ability to report on decision outcomes (ex. custom dashboards). This short video shows you how you create dashboard reports in SMARTS to monitor and compare KPIs when you modify decision logic.
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Build your library of test cases as part of your planning process:
We already mentioned this in our previous post but want to reiterate it here. Ideally, by the time you start your migration, you should have your test cases ready, so that you can test-as-you-go. In addition, once you have your library, you can continue to use these test cases for regression testing every time you modify your decision logic moving forward.
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Augment regression testing with business verification:
Regression testing only works when your old system is error-free. So even if your decision outcomes in the new system match what’s in the old, the outcomes may not be accurate. This is where business verification comes in handy. These are additional business rules applied to your business rules to validate that your decision logic is correct. For example, you may have a rating table that segments people into different tiers where the higher the tier, the higher the rating. You can create a verification rule that checks whether Tier 1 < Tier 2 < Tier 3, etc. If Tier 3 < Tier 2, you know you have an error in your table. Focus your verification rules on higher-level patterns like this to help you catch any errors from your old system. And like with regression testing, continue to run these checks every time you modify your decision logic post migration.
Want to learn how SMARTS empowers business analysts to make smarter, faster operational decisions? Contact us today to request a custom demo.
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.
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
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
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.