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 tablesDecision 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 treesA 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 graphsDecision 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 modelsLookup 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 rulesWith 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.
AboutSparkling 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.
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.
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.
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.
Data vs. knowledge in automated decision management — Why not both?
In the tech industry, we also have our well-known “Coke vs. Pepsi”, “Avis vs. Hertz”, or “Mac vs. PC” debates. In the automated decision management category, the question that keeps coming up is “data vs. knowledge.” The aim of this blog post is to show that from a practical point of view, data and knowledge can be found in the same application. To do this, we will show it with SMARTS, Sparkling Logic’s decision management platform that allows users to combine data and knowledge without them entering the “data vs. knowledge” debate.
Origin of the debateThe “data vs. knowledge” debate dates to an old debate about whether knowledge about a subject should be hand coded or machine learned. A first camp of researchers and practitioners sought to encode this knowledge in the form of rules and an inference engine that runs on these rules to supply answers to user questions. A second camp sought to develop programs that learn from available data using statistical methods to generate models that can make predictions from unseen data. At Sparkling Logic, we support a pragmatic approach that consists in using data, knowledge, or both depending on the problem and the situation at hand.
It is all about the situationThere is no such thing as a stand-alone decision management application. It is often built with the purpose of being integrated into a larger system for loan origination, risk management, product configuration, or other similar applications. As I wrote before, there is no one single approach. It is all about the situation.
Data is everywhere, easy to collect, organize, and transform into predictive knowledge. So, if you have a lot of data, it may be better to build your decision management application around that data if the new observed data does not deviate too much from the old, learned data.
On the other hand, when you have knowledge whether in the form of rules or procedures, it is better to build your application around this valuable knowledge if it is easy to capture and code into the application.
If you have both data and knowledge, why not using the two, when you can do so in a modern decision management platform such as SMARTS, the subject of the next section.
The SMARTS waySMARTS is a decision management platform that enables creating, testing, deploying, and improving automated decisions in an integrated platform. I will not detail it here, but you can find a brief overview of SMARTS on our blog page and a full description on our resources page. Instead, I will focus the rest of this article on how to use SMARTS when you have plenty of data or domain knowledge about the application you want to develop.
You have plenty of data
For situations where you have plenty of data, SMARTS proposes two tools: RedPen and BluePen.
With RedPen, you write decisions in the form of rules using a use-case driven approach. A loaded data sample supplies the context for the rules and enables immediate execution and testing of each rule. RedPen mimics what subject-matter experts do when they flag decisions.
When you activate RedPen, you can pin an existing rule, a field of this rule, or a rule set and change it as if you were using a real pen on real paper. You can also create new rules with RedPen, SMARTS automatically turns them into executable rules.
On the other hand, BluePen lets you explore and analyze your data using your domain knowledge to find predictors. Then, using these predictors, you can generate a model in the form of legible rules and integrate them into your decision logic.
Using BluePen, you can engineer or change the models any time you need to. Without heavy investment in data analytics tools and efforts, you can evaluate BluePen models in simulations and quickly deploy them in the context of an operational decision.
You have domain knowledge
For situations where you have knowledge, SMARTS proposes two additional tools: SparkL and Pencil.
SparkL is Sparkling Logic’s language for writing rules in a natural language fashion. SparkL comes with everything you need to write rules —mathematical expressions, string manipulations, regular expressions, patterns, dates, logical manipulations, constraints, and much more. You can express any imaginable decision logic and symbolic computation, making it the choice for highly sophisticated decisioning applications where the conditions as well as the actions can take a wide variety of forms.
Pencil is our DMN compliant graphical decision design tool for uncovering, documenting, and sharing decisions with colleagues and partners. With Pencil, you just drag and drop graphical shapes to form a complete decision diagram. Then you add business logic to the graphical shapes and let SMARTS execute it.
Pencil helps you think about the ultimate decisions in a structured way, starting from the top-level decision to smaller sub-decisions. This iterative process is very friendly and amazingly easy to share with colleagues or partners working on the same project.
In addition to the above tools and language, SMARTS comes with a built-in dashboard to measure and improve business outcomes of the taken decisions.
You have both
If you are lucky enough to have both data and knowledge, you can leverage your models’ outputs by using it as the input to rules. For example, your loan management application could run a model that calculates a score and another model that calculates a risk and use that score and risk in a rule to calculate a price.
You can also do it the other way around, using the outputs of rules as inputs to your models that you would have trained with data. For example, your application might run a rule to classify a loan applicant, then run a model to calculate their risk of default and another model to calculate the price.
Whether you have data, knowledge, or both, SMARTS uses them as sources of information for the automation of your operational decisions.
- Data and knowledge do not have to be antagonistic. They can both be used as inputs to automate decisions.
- SMARTS is a modern decision management platform that enables their combination in an elegant and seamless way. For SMARTS, data and knowledge can be used as sources of information.
- When you have a lot of data, you can use RedPen to write rules without learning a special rule language or syntax, just starting with the data. You can also use BluePen to learn from data and turn it into rules.
- When you have knowledge, you can use SparkL to encode it into rules, from the simplest to the most complex rules that your application may require. You can also use Pencil when designing, documenting, and sharing your decisions are part of the requirements.
- Our mission is to enable customers to implement the most demanding decisioning requirements and to easily change and improve them over time. Whether you have data, knowledge, or both, we can help. Just contact us or request a free trial.
AboutSparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.
Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Hassan Lâasri is a data strategy consultant, now leading marketing at Sparkling Logic. You can reach him out at firstname.lastname@example.org.
Our customers — who they are, what they want, and what we bring them
As an enterprise IT solution, SMARTS has different customers in the same organization who directly use it or indirectly benefit from it. This blog post aims to succinctly describe who our customers are, what they want, and what value we bring to each of them that matches their unique needs.
In our terminology, these are the customers in the organizations who design, author, deploy, and update decisions according to the company’s policies and industry’s directives.
When they looked for a decision management solution, they looked for product simplicity and rich functionality. More importantly, they wanted autonomy once the solution was in place.
After a few weeks after training on SMARTS, our business analyst customers reported that they very much liked to have data, models, and business rules in the same tool. They enjoyed how we succeeded in managing SMARTS’ evolution to have both richness and easiness in the same product. They also enjoyed being able to quickly author, test, deploy, run, monitor, and change decisions. Their experience with SMARTS was a joy as they could focus on the decisioning process and its outcomes instead of the technology to implement it.
Business users are the people who run, monitor, and manage the performance of the business. In our case, they are the internal customers of business analysts. They are the ones who use the solution daily.
They wanted to know how easy it will be for them to monitor decisions built by business analysts and make the necessary changes when the actual performance may deviate from the expected performance.
After using SMARTS, business users reported the following benefits: Quick change-test-deploy-run cycles, being able to work without coding and with no prior knowledge of machine learning or business rules, just with their knowledge of the business and using web forms and point-and-click.
By IT, we designate IT the people who install and connect the solution to the rest of the organization’s IT system. They asked for integration, performance, security, and fit with the IT global architecture and governance.
They want to have business analysts and business users to be autonomous but at the same time being able to monitor the solution as the rest of the IT infrastructure.
IT people liked all the performance, security, integration, and scalability we promised. They also appreciated SMARTS adherence to the enterprise IT architecture and governance as expected. They liked how easily they could deploy SMARTS on premises or in the cloud. Finally, they also very much liked to have no additional development or changes in the current applications.
These are the people who develop and manage models using data science libraries through languages such as Python, R, SAS, and SPSS.
They are not direct users, but they were willing to see their models fully operational into the new solution while they continue their effort on enhancing existing models and experimenting with new ones.
Thanks to SMARTS, they were able to know the performance of their models in production with real data and transactions. SMARTS was an effective demonstrator of their models.
In our case, these are the people who head organizations or verticals where decisions are at the core of their operations, throughout all the organizations activity. Their attention is “more revenue, less cost, and why not both!”
They wanted to hear about similar successful implementations in their market, in particular the time it would take to recoup their investment in the new solution, and the strategic advantages it will provide them after one year or two in production.
To management people, we brought strategic benefits. They could operate the business under a decisioning process that implements the business strategy. Their organization could finally make informed, error-free, and unbiased decisions. And they were insured that the decisions taken were in full compliance to internal policies and industry regulations.
Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful decision management platform, accessible to business analysts and ‘citizen developers.’ Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.
Sparkling Logic SMARTSTM (SMARTS for short) is an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines business rules with predictive models to create intelligent decisioning systems.
If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, a product configurator, a condition-based maintenance application, or such applications, SMARTS can help. Just contact us or request a free trial.
Noise reduction in digital decisioning with Sparkling Logic SMARTS
In this post, we present how to deal with the problem of noise, which is both a source of errors and biases in digital decision-making in organizations, through explicit decision rules, dashboards, and analytics. To illustrate our point, we use the example of the Sparkling Logic SMARTS decision management platform.
Noise in organizations’ decisioning and what to do about itIn an interview with McKinsey, Olivier Sibony, one of the renowned experts in decisioning, recommends algorithms, rules, or artificial intelligence to solve the problem of noise, a generator of errors and biases in decisioning in organizations. This recommendation resonates with our vision of automating decisioning — not all of the decisioning but the operational decisions that organizations make by thousands and sometimes millions per day. Think credit origination, claim processing, fraud detection, emergency routing, and so on.
In our vision, one of the best ways to reduce noise, and therefore errors and 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. Designers must be able to analyze the consequences of decisions on the organization before putting them into production.
Noise reduction with explicit decision rules, dashboards, and analyticsOur SMARTS decisioning platform helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation, reducing biases that could be a failure to comply with industry regulations, a deviation from organizational policies, or a source of an applicant disqualification. The consequences of biases could be high in terms of image or fees, and even tremendous for certain sensitive industries such as financial, insurance, and healthcare services.
In SMARTS, business users (credit analysts, underwriters, call center professionals, fraud specialists, product marketers, etc.) express decisions in the form of business rules, decision trees, decision tables, decision flows, lookup models, and other intuitive representations that make decisioning self-explainable so that they can test decisions individually as well as collectively. So, at any time, they can check potential noise, errors, and biases before they translate into harmful consequences for the organization.
In addition to making development of decisioning explicit, SMARTS also comes with built-in dashboards to assess alternative decision strategies and measure the quality of performance at all stages of the lifecycle of decisions. By design, SMARTS focuses the decision automation effort on tangible objectives, measured by Key Performance Indicators (KPIs). Users define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. Moreover, they can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.
During the testing phase, the users have access to SMARTS’ built-in map-reduce-based simulation capability to measure these metrics against large samples of data and transactions. 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.
And once the decisioning application is deployed, the users have access to SMARTS’ real-time decision analytics, a kind of cockpit for them to monitor the application, make the necessary changes, without stopping the decisioning application. SMARTS platform automatically displays KPI metrics over time or in a time window. The platform also generates notifications and alerts when some of the thresholds users have defined are crossed or certain patterns are detected. Notifications and alerts can be pushed by email, SMS, or generate a ticket in the organization’s incident management system.
Rather than being a blackbox, SMARTS makes decisioning explicit so that the users who developed it can easily explain it to those who will operate it. Moreover, the latter can adjust the decision making so that biases can be quickly detected and corrected, without putting the organization at risk for violating legal constraints, eligibility criteria, or consumer rights.
So, if you are planning to build a noise-free, error-free, and bias-free decisioning application, SMARTS can help. The Sparkling Logic team enjoys nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have them satisfied, but also proud of the system they build. We helped companies to build flaw-proof, data-tested, and scalable applications for loan origination, claims processing, credit risk assessment, or even fraud detection and response. So dare to give us a challenge, and we will solve it for you in days, not weeks, or months. Just email us or request a free trial.
AboutSparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful digital decisioning platform, accessible to business analysts and ‘citizen developers’. Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.
Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.
Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at email@example.com.
Authoring Business Rules with Data, Standards, and Apps in SMARTS
Tags: business rules • decision automation • decision management • decisioning • DMN • RPA • rule authoring • SMARTS
Sparkling Logic turns data-driven businesses into learning organizations
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 firstname.lastname@example.org.
SMARTS Real-Time Decision Analytics
In this post, I briefly introduce SMARTS Real-Time Decision Analytics capability to manage the quality and performance of operational decisions from development, to testing, to production.
Decision performanceH. James Harrington, one of the pioneers of decision performance measurement, once said, “Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.” This statement is also true for decision performance.
Measuring decision performance is essential in any industry where a small improvement in a single decision can make a big difference, especially in risk-driven industries such as banking, insurance, and healthcare. Improving decisions in these sectors means continuously adjusting policies, rules, prices, etc. to keep them consistent with business strategy and compliant with regulations.
Decision performance management in SMARTSSMARTS helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation — thereby reducing errors and bias. To this end, we added a real-time decision analytics capability to the core decision management platform.
Currently used in financial and insurance services, it helps both business analysts and business users to define dashboards, assess alternative decision strategies, and measure the quality of performance at all stages of the lifecycle of decision management — all with the same interface without switching from one tool to another.
Development. From the start, SMARTS focuses the decision automation effort on tangible business objectives, measured by Key Performance Indicators (KPIs). Analysts and users can define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture their decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. They can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.
Testing and validation. During the testing phase, analysts and users have access to SMARTS’ built-in map-reduce-based simulation environment to measure these metrics against large samples of data. Doing so, they can estimate the KPIs for impact analysis before the actual deployment. And all of this testing work does not require IT to code these metrics, because they are transparently translated by SMARTS.
Execution. By defining a time window for these metrics, business analysts can deploy them seamlessly against production traffic. Real-time decision analytics charts display the measurements and trigger notifications and alerts when certain thresholds are crossed or certain patterns are detected. Notifications can be pushed by email, or generate a ticket in a corporate management system. Also, real-time monitoring allows organizations to react quickly when conditions suddenly change. For example, under-performing strategies can be eliminated and replaced when running a Champion/Challenger experiment.
Uses casesInsurance underwriting. Using insurance underwriting as an example, a risk analyst can look at the applicants that were approved by the rules in production and compare them to the applicants that would be approved using the rules under development. Analyzing the differences between the two sets of results drive the discovery of which rules are missing or need to be adjusted to produce better results or mitigate certain risks.
For example, he or she might discover that 25% of the differences in approval status are due to differences in risk level. This insight leads the risk analyst to focus on adding and/or modifying your risk related rules. Repeating this analyze-improve cycle reduces the time to consider and test different rules until he or she gets the best tradeoff between results and risks.
Fraud detection. An other example from a real customer case is flash fraud where decisions had to be changed and new ones rolled out in real time. In this case, the real-time decision analysis capability of SMARTS was essential so that the customer could spot deviation trends from normal situation directly in the dashboard and overcome the flood in the same user interface, all in real time.
Without this built-in capability, the time lag between the identification of fraud and the implementation of corrective actions would have been long, resulting in significant financial losses. In fact, with SMARTS Real-Time Decision Analytics, the fraud management for this client has gone from 15 days to 1 day.
Marketing campaign. The two above examples are taken from financial services but SMARTS real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules, such as in loan origination, product pricing, or marketing promotion.
In the latter case, SMARTS can help optimize promotion in real-time. Let’s say you construct a series of rules for a marketing couponing using SMARTS Champion/Challenger capability. Based on rules you determine, certain customers will get a discount. Some get 15% off (the current offering — the champion), while others get 20% (a test offering — the challenger). And you wonder if the extra 5% discount leads to more coupons used and more sales generated. With SMARTS real-time decision analytics environment, you find out the answer as the day progresses. By testing alternatives, you converge to the best coupon strategy with real data and on the fly.
ConclusionAs part of the decision lifecycle, business analysts obviously start by authoring their decision logic. As they progress, testing rapidly comes to the forefront. To this end, SMARTS integrates predictive data analytics with real-time decision analytics, enabling business analysts and business users to define dashboards and seamlessly associate metrics with the execution environment — using the same tool, the same interface, and just point and click.
- SMARTS comes with built-in decision analytics — no additional or third-party tool is required
- You can define metrics on decision results so you can measure and understand how each decision contributes to your organization’s business objectives
- Decision metrics enable you to assess alternative decision strategies to see which should be kept and which rejected
- SMARTS add-on for real-time decision analytics lets you monitor the decisions being made and make adjustments on the fly
- SMARTS’ real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules
AboutSparkling Logic is a decision management company founded in the San Francisco Bay Area to accelerate how companies leverage data, machine learning, and business rules to automate and improve the quality of enterprise-level decisions.
Sparkling Logic SMARTS is an end-to-end, low-code no-code decision management platform that spans the entire business decision lifecycle, from data import to decision modeling to application production.
Carlos Serrano is Co-Founder, Chief Technology Officer at Sparkling Logic. You can reach him at email@example.com.