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Sparkling Logic turns data-driven businesses into learning organizations

Written by: Carole-Ann BerliozPublished on: Jul 26, 2021No comments

Sparkling Logic SMARTS AI & ModelOps

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 import

Business 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 technology

When 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

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SMARTS Decision ManagerSparkling Logic SMARTS is a decision management platform that empowers business analysts to define decisions using business rules and predictive models and deploy those decisions into an operational environment. SMARTS includes dashboard reporting that allows organizations to measure the quality of decisions both during development and post deployment. Learn more about how SMARTS can help your organization improve decisions.
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