In this blog post we will describe SMARTS in terms of its four capabilities: Decision Management Platform, Low-Code / No-Code Apps, Machine Learning Ops, and Real-time Decision Analytics.
Decision Management Platform
SMARTS is a decision management platform that spans the entire business decision lifecycle, from modeling to deployment. Decision Management Platform, as the core functionality of SMARTS, includes authoring, testing, simulating and deploying decisions as well as the management and governance of those decisions. Key authoring capabilities include RedPenTM, which allows business analysts to write rules via point and click without learning a special syntax, and Fluid Metaphors, which allows business analysts to choose the most appropriate decision representation based on the task at hand. The same decisions can be viewed and modified in the form of decision rules, tables, trees, or graphs.
SMARTS is a data-centric product in that business analysts can import data and immediately start writing and testing rules against that data, making the building of rating, pricing, and any calculation engine an easy task. SMARTS’ powerful processing of spreasheets allows business analysts to retrieve rates from a very set of options; fine-tune the rates to cope with exceptions and overrides; and manage these rates over different time horizons, per geography, any user-defined dimensions.
SMARTS lifecycle management capabilities include release management (read-only snapshots of projects) to track promotions and deployments. Business analysts can view the history of their decision logic and revert to earlier versions or releases ant anytime. Based on their permissions, they can publish their changes into a test environment or into production.
SMARTS decisions are typically deployed as decision services either on the cloud on on-premise. SMARTS decision services provide support for secure service invocations in an authenticated context, support for high-availability and scalability.
All of SMARTS other capabilities, Low-Code/No-Code, Model Ops, and Real-time Analytics are integrated into to the Decision Management Platform.
Business analysts can develop decisions quickly and intuitively with SMARTS’ Low-Code capability. SMARTS Low-Code features include graphical representations of decision logic such as decision flows, tables, trees and graphs. Rules are written using the SparkL (pronounced “sparkle”) user-friendly language or with the point-and-click Red Pen feature.
Thanks to this low-code capability, business analysts can develop Business Apps which provide a no-code environment for untrained business users and ‘citizen developers’ to modify, test and deploy changes.
Many decisions deal with uncertainty- such as credit card approvals and loan and mortgage approvals. These decision benefit from predictive models that help organizations make the best possible decision in the face of uncertainties. SMARTS operationalizes predictive models in three ways.
- Business analysts can import machine learning models developed by a data science team using PMML (the standard model interchange format). Once imported these models can be integrated into any decision and deployed. The models can be tested in the context of the decision to ensure they produce expected results and then can be deployed.
- SMARTS’ patented BluePen machine learning technology can be used by business analysts to generate rules from data. Models created by BluePenTM are explicit and easy to understand.
- In cases where models exist but are not available as PMML, but are available as spreadsheets, SMARTS can import them as a dashboard, regression, or tree model.
Real-Time Decision Analytics
SMARTS Real-Time Decision Analytics lets business analysts define metrics that measure the outcome of SMARTS decisions to ensure they are supporting business strategy and objectives. The SMARTS dashboard displays graphical representations of the metrics allowing the business analysts to analyze the impact of changes to decision logic and run simulations against large volumes of historical data. SMARTS decisions can also be monitored post-deployment and alerts can be defined which notify stakeholders when thresholds are exceed.
Using SMARTS Real-Time Decision Analytics capabilities, Business analysts can measure the end-to-end performance of the decisions that invoke these models – not only the quality of the model but the quality of the whole process.
We hope describing SMARTS in-terms of its powerful capabilities has given you a better understanding of the platform. Insurance companies, banks, and healthcare companies rely on decisions powered by SMARTS every day for loan origination, claims processing, credit risk assessment, as well as fraud detection and response.
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.
Colleen McClintock, is VP Products at Sparkling Logic. You can reach her at firstname.lastname@example.org.
Tags: business rules • decision automation • decision management • decisioning • DMN • RPA • rule authoring • SMARTS
Today, predictive analytics is common in any data-driven business. Typically, data scientists create predictive models first, and IT staff deploy these models in a production environment. At Sparkling Logic, not only have we streamlined this process, but we’ve extended it with prescriptive decisions. Sparkling Logic SMARTS AI & ModelOps is the third built-in capability of SMARTS to cover the full spectrum of operational predictive models, from importing models and creating new ones to initiating learning tasks. But let’s start with a brief overview of the stages data has gone through.
Data: a resource, an asset, a business
Until recently, data was a resource to conduct business and as such, it was typically managed by the CIO’s organization. The organization’s mission was to build the overall data architecture, to choose a database vendor, and to design all the applications necessary to process the data from the databases to the business and functional people screens. These applications were mostly reporting, letting the business get a sense for how the business has been doing based on the collected data.
Then came the first transformation, where data went from an asset used to understand how the business has been doing to being an asset leveraged to predict how the business could potentially do in the future. Reporting was enhanced by predictive analytics. The scope of the analytics was not only what had happened, but also what was happening and what could happen. In general, these two past-focused and future-focused activities cover most of what data science is in business, with some really important use cases on marketing, sales, and customer relationship management.
However, a new transformation is underway, first in the banking, insurance, and health sectors, but will certainly penetrate other sectors. It consists of transforming analytics into automated decisions, translating predictions into prescriptions. The goal of this transformation is to create a virtuous cycle where not only data is analyzed, but this analysis is transformed into decisions and actions that generate new data, and so on. Reporting and predictive analytics are now completed by prescriptive analytics.
Anticipating this trend, the founders of Sparkling Logic designed the SMARTS decision management platform to implement this cycle of data, insights, and decisions. Sparkling Logic SMARTS comes with a built-in AI & ModelOps environment that covers the full spectrum of operationalizing predictive models, from importing models built by data scientists, to creating new ones without prior knowledge of machine learning, to launching and managing learning jobs.
Sparkling Logic SMARTS AI & ModelOps
Predictive model importBusiness analysts can import AI, machine learning, and deep learning models developed by data scientists, and leverage them in the decision logic. The models could be developed in Python, SPSS, SAS, or Project R among others. SMARTS integrates them as long as they are compliant to PMML, a standard for sharing and deploying predictive models, or are accessible as services.
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.
In cases where models exist but are only available as specification, business analysts can easily import these models and seamlessly transform them into business rules for full transparency and easy inspection.
There may be situations where it is necessary to call an external service that is available elsewhere. This external service can be a predictive model or a data source. SMARTS provides support for remote functions, which makes it possible to invoke an external service through JSON-RPC or REST services.
BluePen predictive technologyWhen time is of the essence, when models are short-lived or when expertise needs to be confronted with knowledge captured in the data, business experts can use the BluePen learning technology to quickly create a model, potentially leveraging existing models.
BluePen lets business analysts and business experts explore and analyze data using domain knowledge and expertise to identify predictors, or, alternatively, selects the predictors for them. Then, using the selected predictors, BluePen helps them to generate a model in the form of readable decision rules, tables, or trees, and integrate them into their decision logic.
Using BluePen, users can build meaningful predictive models in hours or days, rather than the months it often takes. Users can also engineer or modify the models. As a result, without heavy investment in data analytics efforts, these models can be tested, leveraged in simulations, and quickly deployed in the context of an operational business decision.
Regardless of the business analyst’s choice, he or she can operationalize a wide range of models within SMARTS. Being able to integrate models into decision logic is a central ability to test and measure the performance of the end-to-end decisioning.
Moreover, SMARTS allows the analyst to translate the insights from many different models into a decision. Typically, data-centric organizations will have many different models which each can contribute insights into what the decision should be. The orchestration of how these insights are combined is expressed in decision logic, turning multiple discrete predictions into actual prescriptive decisions.
The benefits of combining machine learning and automated decisioning as SMARTS does are nothing less than transforming businesses into always learning organizations where data helps identify opportunities, machine learning turns that data into insights and automated decisioning turns this information into action, closing the virtuous cycle that data promises.
- Data has moved from being a resource to assess how the business has been doing, to being an asset used to predict the future of the business, and finally to an asset used to improve automated decisions
- Sparkling Logic SMARTS comes with a built-in AI & ModelOps environment that covers the full spectrum of operationalizing predictive models, from importing models, to creating new ones, to launching and managing learning jobs
- With its AI & ModelOps capability, SMARTS helps in transforming businesses into learning organizations, closing the virtuous cycle that data promises. Data feeds analytics leading to improved decisions that generates additional data in addition to profits
Sparkling Logic is a decision management company founded in the San Francisco Bay Area to accelerate how companies leverage data, machine learning, and business rules to automate and improve the quality of enterprise-level decisions.
Carole-Ann is Co-Founder, Chief Product Officer at Sparkling Logic. You can reach her at email@example.com.
In this post, I briefly introduce SMARTS Real-Time Decision Analytics capability to manage the quality and performance of operational decisions from development, to testing, to production.
Decision performanceH. James Harrington, one of the pioneers of decision performance measurement, once said, “Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.” This statement is also true for decision performance.
Measuring decision performance is essential in any industry where a small improvement in a single decision can make a big difference, especially in risk-driven industries such as banking, insurance, and healthcare. Improving decisions in these sectors means continuously adjusting policies, rules, prices, etc. to keep them consistent with business strategy and compliant with regulations.
Decision performance management in SMARTSSMARTS helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation — thereby reducing errors and bias. To this end, we added a real-time decision analytics capability to the core decision management platform.
Currently used in financial and insurance services, it helps both business analysts and business users to define dashboards, assess alternative decision strategies, and measure the quality of performance at all stages of the lifecycle of decision management — all with the same interface without switching from one tool to another.
Development. From the start, SMARTS focuses the decision automation effort on tangible business objectives, measured by Key Performance Indicators (KPIs). Analysts and users can define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture their decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. They can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.
Testing and validation. During the testing phase, analysts and users have access to SMARTS’ built-in map-reduce-based simulation environment to measure these metrics against large samples of data. Doing so, they can estimate the KPIs for impact analysis before the actual deployment. And all of this testing work does not require IT to code these metrics, because they are transparently translated by SMARTS.
Execution. By defining a time window for these metrics, business analysts can deploy them seamlessly against production traffic. Real-time decision analytics charts display the measurements and trigger notifications and alerts when certain thresholds are crossed or certain patterns are detected. Notifications can be pushed by email, or generate a ticket in a corporate management system. Also, real-time monitoring allows organizations to react quickly when conditions suddenly change. For example, under-performing strategies can be eliminated and replaced when running a Champion/Challenger experiment.
Uses casesInsurance underwriting. Using insurance underwriting as an example, a risk analyst can look at the applicants that were approved by the rules in production and compare them to the applicants that would be approved using the rules under development. Analyzing the differences between the two sets of results drive the discovery of which rules are missing or need to be adjusted to produce better results or mitigate certain risks.
For example, he or she might discover that 25% of the differences in approval status are due to differences in risk level. This insight leads the risk analyst to focus on adding and/or modifying your risk related rules. Repeating this analyze-improve cycle reduces the time to consider and test different rules until he or she gets the best tradeoff between results and risks.
Fraud detection. An other example from a real customer case is flash fraud where decisions had to be changed and new ones rolled out in real time. In this case, the real-time decision analysis capability of SMARTS was essential so that the customer could spot deviation trends from normal situation directly in the dashboard and overcome the flood in the same user interface, all in real time.
Without this built-in capability, the time lag between the identification of fraud and the implementation of corrective actions would have been long, resulting in significant financial losses. In fact, with SMARTS Real-Time Decision Analytics, the fraud management for this client has gone from 15 days to 1 day.
Marketing campaign. The two above examples are taken from financial services but SMARTS real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules, such as in loan origination, product pricing, or marketing promotion.
In the latter case, SMARTS can help optimize promotion in real-time. Let’s say you construct a series of rules for a marketing couponing using SMARTS Champion/Challenger capability. Based on rules you determine, certain customers will get a discount. Some get 15% off (the current offering — the champion), while others get 20% (a test offering — the challenger). And you wonder if the extra 5% discount leads to more coupons used and more sales generated. With SMARTS real-time decision analytics environment, you find out the answer as the day progresses. By testing alternatives, you converge to the best coupon strategy with real data and on the fly.
ConclusionAs part of the decision lifecycle, business analysts obviously start by authoring their decision logic. As they progress, testing rapidly comes to the forefront. To this end, SMARTS integrates predictive data analytics with real-time decision analytics, enabling business analysts and business users to define dashboards and seamlessly associate metrics with the execution environment — using the same tool, the same interface, and just point and click.
- SMARTS comes with built-in decision analytics — no additional or third-party tool is required
- You can define metrics on decision results so you can measure and understand how each decision contributes to your organization’s business objectives
- Decision metrics enable you to assess alternative decision strategies to see which should be kept and which rejected
- SMARTS add-on for real-time decision analytics lets you monitor the decisions being made and make adjustments on the fly
- SMARTS’ real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules
AboutSparkling Logic is a decision management company founded in the San Francisco Bay Area to accelerate how companies leverage data, machine learning, and business rules to automate and improve the quality of enterprise-level decisions.
Sparkling Logic SMARTS is an end-to-end, low-code no-code decision management platform that spans the entire business decision lifecycle, from data import to decision modeling to application production.
Carlos Serrano is Co-Founder, Chief Technology Officer at Sparkling Logic. You can reach him at firstname.lastname@example.org.
Low-code no-code is not a new concept to Sparkling Logic. From the beginning, the founders wanted to deliver a powerful yet simple product, so that a business analyst could start with data and build decision logic with built-in predictive data analytics and execution decision analytics.
Version after version, they have achieved this vision through SMARTS, an end-to-end decision management platform that features low-code no-code for business analysts and business users to develop and manage decision logic through point-and-click operations.
For business analysts, SMARTS offers a low-code development environment in which users can express decision logic through a point-and-click user interface to connect data, experiment with decisions, monitor execution without switching between different tools to get the job done. Depending on the nature of the decision logic at hand and user preferences, business analysts can choose on the fly the most appropriate representation to capture or update their decision logic. The resulting decision logic is seamlessly deployed as a decision service without IT intervention.
To push the simplification even further, Sparkling Logic founders turned to their customers for inspiration on their needs and developed three complementary technologies:
- RedPen, a patented point-and-click technology that accelerates rule authoring without a need to know rule syntax or involve IT to author the rules
- BluePen, another patented point-and-click technology to quickly create or leverage a data model and put it into production without involving data scientists or IT
- A dynamic questionnaire to produce intelligent front-ends that reduces the number of unnecessary or redundant questions
In addition to low-code development capability for business analysts, SMARTS also elevates the decision logic to a simple web form-based interface for untrained business users. They can configure their decision strategies, test the updated decision logic, and promote the vetted changes to the next staging environment — without learning rules syntax.
These business apps offer a business abstraction for most tasks available in SMARTS related to configuration, testing and simulation, invocation and deployment management, as well as administration.
For example, credit risk specialists can configure loans, credit cards, and other banking products, and pricing specialists can control pricing tables, through a custom business app specific to their industry. The no-code business app enables business users to cope with environment changes whether they are related to internal policies, competition pressure, or industry regulation.
Furthermore, SMARTS tasks can also be automated through orchestration scripts. Business users can trigger these scripts through the click of a button, or schedule them to be performed automatically and seamlessly.
Sparkling Logic is a decision management company founded in the Bay Area to accelerate how companies leverage internal and external data and models to automate and improve the quality of enterprise-level decisions.
Sparkling Logic SMARTS is an end-to-end, low-code no-code decision management platform that spans the entire business decision lifecycle, from data import to decision modeling to application production.
Integration with data is key to a successful decision application: Decision Management Systems (DMS) benefit from leveraging data to develop, test and optimize high value decisions.
This blog post focuses on the usage of data by the DMS for the development, testing and optimization of automated decisions.
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In our last post, we looked at how predictive models are used in automated decisions. A key take away from that post is that a prediction is not a decision. Rather predictive models provide us with key insights based on historical data so we can make more informed decisions.
For example, a predictive model can identify customers that are likely to churn, transactions that are suspicious, and offerings and ads that are likely to have the most appeal. But, based on these predictions, we still need to decide the best response or course of action. A decision combines one or more predictions with business knowledge and expertise to define the appropriate actions.
From Predictions to Decisions
Determining how to take action based on predictions is not trivial . Most likely, there are multiple business options for the actions an organization could take based on a prediction. Consider for example charges that are identified as potentially fraudulent, a card issuer could report the case to the fraud team for further investigation, shut down the card to prohibit further charges, or text the cardholder to verify the charge.
Or in the case of customers who have been identified as having a high probability of switching to a competitor, a company may decide to contact them with a special incentive or renewal offer. But the company still needs to decide exactly how many customers will receive the offer and how much to offer. The company could target a flat percentage of customers, or could focus only on those with the highest projected CLTV (customer lifetime value). Targeting too many customers with too large off an offer might be too expensive to be worthwhile. The possible actions an organization can take based on predictions have different costs and benefits that need to be evaluated to determine the optimal decision. This is where decision simulation is applicable. Decision simulations help you identify the best decision strategy from amongst a set of alternatives.
Measure Your Decision Quality with KPIs and Metrics
The “best” decision strategy means the one that most closely meets your organization’s objectives. By defining KPIs and metrics that measure the quality of the decision in relation to these objectives, we have a basis to compare alternative decision approaches. Ideally these KPIs were identified early on, when you first decided to automate the decision.
Decision KPIs give us a clear understanding of how decision performance is related to business performance. They provide the basis for evaluating decision alternatives. To compare alternatives, you can run simulations using historical data. Using simulations you can compare one decision strategy to another, or you can compare how a given strategy performs on each of your customer segments as represented in your data.
Returning to the above customer churn example, we may decide we want to target customers who have an 80% or greater probability of churn based on our predictive model. One option would be to offer them a special 25% discount to attempt to re-engage and keep them as a customer. We can run a simulation against our historical data to learn how many customers fall into this bucket. From there, we can evaluate how much the discount offer would likely cost us. We can run multiple simulations using different thresholds, offers, and combinations until we find the best decision approach to deploy.
Decision Simulation Helps You Evaluate Alternative Decision Strategies
Decision simulations help us evaluate alternative decision strategies to narrow to the best approach. Modern decision management technologies, like SMARTS, make it easy to set up and run these simulations, even on very large data samples. Of course, the ultimate quality of the selected decision approach is related to its success once deployed- how many customers do we manage to retain and at what cost?
Once we deploy a decision we can monitor and track the KPIs but we have no way of knowing whether customers who did not accept our offer would have instead accepted a different offer. Or whether customers who did accept would also have accepted a 20% rather than a 25% discount. To answer these questions we need to use Champion / Challenger experiments. We’ll cover how Champion / Challenger works with decision management in an upcoming post.
Agility is a key focus and benefit in the discipline of decision management. Agility, in the decision management context, means being able to rapidly adjust and respond to business and market-driven changes. Decision management technologies allow you to separate the business logic from your systems and applications. Business analysts then manage and make changes to the business logic a separate environment. And they can deploy their changes with minimal IT involvement and without a full software development cycle. With decision management, changes can be implemented in a fraction of the time required to change traditional applications. This ability to address frequently changing and new requirements that impact key automated decisions makes your business more agile.
Being able to rapidly make and deploy changes is important. But how do you know what changes to make? Some changes, like those defined by regulations and contracts, are straightforward. If you implement the regulations or contract provisions accurately, the automated decision will produce the required results and therefore, make good decisions. However, many decisions don’t have such a direct and obvious solution.
When Agility Isn’t Enough
Frequently decisions depend on customer behavior, market dynamics, environmental influences or other external factors. As a result, these decisions involve some degree of uncertainty. For example, in a credit risk decision, you’re typically determining whether or not to approve a credit application and where to set the credit limit and interest rate. How do organizations determine the best decisions to help them gain customers while minimizing risk? The same applies to marketing decisions like making upsell and cross-sell offers. Which potential offer would the customer most likely accept?
Predictive Models Provide Data Insight
This is where predictive models help. Predictive models combine vast amounts of data and sophisticated analytic techniques to make predictions about the future. They help us reduce uncertainty and make better decisions. They do this by identifying patterns in historical data that lead to specific outcomes and detecting those same patterns in future transactions and customer interactions.
Predictive models guide many decisions that impact our daily lives. Your credit card issuer has likely contacted you on one or more occasions asking you to confirm recent transactions that were outside of your normal spending patterns. When you shop online, retailers suggest products you might want to purchase based on your past purchases or the items in your shopping cart. And you probably notice familiar ads displayed on websites you visit. These ads are directly related to sites you previously visited to encourage you to return and complete your purchase. All of these are based on predictive models that are used in the context of specific decisions.
How Predictive Models Are Built
Predictive modeling involves creating a model that mathematically represents the underlying associations between attributes in historical data. The attributes selected are those that influence results and can be used to create a prediction. For example, to predict the likelihood of a future sale, useful predictors might be the customer’s age, location, gender, and purchase history. Or to predict customer churn we might consider customer behavior data such as the number of complaints in the last 6 months, the number of support tickets over the last month, and the number of months the person has been a customer, as well as demographic data such as the customer’s age, location, and gender.
Assuming we have a sufficient amount of historical data available that includes the actual results (whether or not a customer actually purchased in the first example, or churned in the second) we can use this data to create a predictive model that maps the input data elements (predictors) to the output data element (target) to make a prediction about our future customers.
Typically data scientists build predictive models through an iterative process that involves:
- Collecting and preparing the data (and addressing data quality issues)
- Exploring and Analyzing the data to detect anomalies and outliers and identify meaningful trends and patterns
- Building the model using machine learning algorithms and statistical techniques like regression analysis
- Testing and validating the model to determine its accuracy
Once the model is built and validated it can be deployed and used in real-time to inform automated decisions.
Deploying Predictive Models in Automated Decisions
While predictive models can give us sound predictions and scores, we still need to decide how to act on them. Modern decision management platforms like SMARTS Decision Manager let you combine predictive models that inform your decisions with business rules that translate those decisions into concrete actions. SMARTS includes built-in predictive analytics capabilities and also lets you use models built using other analytics tools such as SAS, SPSS and R.
The use of predictive models is rapidly expanding and changing the way we do business. But it’s important to understand that predictions aren’t decisions! Real world business decisions often include more than one predictive model. For example, a fraud decision might include a predictive model that determines the likelihood that a transaction originated from an account that was taken over. It might also include a model that determines the likelihood that a transaction went into an account that was compromised. A loan origination decision will include credit scoring models and fraud scoring models. It may also include other models to predict the likelihood the customer will pay back early, or the likelihood they will purchase additional products and services (up-sell). Business rules are used to leverage the scores from these models in a decision that seeks to maximize return while minimizing risk.
In our next post, we’ll look at how modern decision management platforms, like SMARTS, help you evaluate alternative decision strategies. We’ll explore how you can use decision simulation to find the best course of action.