Frontline Solvers has been in business for over 25 years and focused on democratizing analytics for the last five years. They identify themselves as an alternative to analytic complexity with a focus on leveraging broadly held Excel skills and a large base of trained students. They offer several products for predictive and prescriptive analytics and have sold these products to over 9,000 organizations over the years. Their customers are both commercial and academic with hundreds of thousands of students using the tool and 500,000 cloud analytics users. Their commercial customers include many very large companies, though generally they sell to several distinct business units rather than at a corporate level.
Frontline began with their work in solvers (prescriptive analytics) and have worked “backward” into predictive analytics. Their approach is very focused on avoiding analytic complexity:
- Smart small and keep it simple, with a focus on rapid ROI.
- Recognize that companies have more expertise than they think – Excel and programming skills for instance plus all the students who have used the software in MBA classes.
- “Big Data” and more complex ML/AI technologies are not essential for success – ordinary database data is often enough.
Frontline is focused on decision support today but rapidly moving into decision automation – decision management systems.
Their core products are modeling systems and solvers for optimization and simulation, used to build a prescriptive model, rather than the analysis of lots of data (though they have data mining routines too). These kinds of optimization models are often called prescriptive analytics because they recommend – prescribe – specific actions for each transaction. Prescriptive analytics can, of course, also be developed by combining predictive analytics and business rules – driving to a recommended action using the combination. Frontline recognizes this and envisions supporting business rules in their software.
Solver-based prescriptive analytic solutions generally focus on many transactions in a set not a single transaction – what Frontline call coordinated decisions. Sometimes these decisions also have no data, no history, so a human-built model is going to be required not one based on data analysis. Indeed, any kind of prescriptive analytic approach to decision-making is going to require human built models – either decision models to coordinate rules and analytics or a solver model (or both, as we have seen in some client projects).
Frontline argues Excel is the obvious place to start because Excel is so familiar. Their RASON language allows you to develop models in Excel and then deploy to REST APIs. They aim to make it easy for business domain experts to learn analytic modeling and methods, to provide easy to use tools and then make it easy to deploy. Working in Excel, they provide a lot of learning aids in the product that popup to help users. They also have an online learning platform (solver.academy) with classes and there are over 700 university MBA courses using Frontline’s software to introduce analytics methods.
The core products are:
- Analytic Solver – a point and click model builder in Excel, including the cloud-based Excel version which they have been supporting since 2013.
- RASON – modeling language that can be generated from the Excel-based product or edited directly.
- SDK – supporting models in written in code, developed in RASON and/or Excel and deployed as REST APIs.
Their base solver is built into the desktop Excel (OEMed by Microsoft). As the cloud Excel does not have this, they have built online apps for optimization, simulation and statistics that work across Excel Online and Google Sheets. The latest version of Excel Online is now ALMOST able to support the full Analytic Solver Suite and this is expected to be complete in Q1 2019, allowing them to unify the product across desktop and online Excel.
To bring data in to the solver, they use the Common Data Service as well as standard Data Sources for data access. This makes it easy to connect to data sources. They also use the Office Workbook model management tools (discovery, governance, audit) which are surprisingly robust for those with corporate licenses to Excel.
The engine has four main capabilities:
- Data mining and forecasting algorithms.
- Conventional optimization and solver.
- Monte Carlo Simulation and decision trees.
- Stochastic and robust optimization.
For very large datasets (such as those used in data mining), the software can pull a statistically valid sample from, say, a big data store. The data can be cleaned, partitioned into training and validation sets and various routines applied. The results are displayed in Excel and PMML (the standard Predict Model Markup Language) used to persist the result. Obviously the PMML is executable both in third party platforms and in their own RASON language.
RASON is a high-level modeling language that allows the definition of data mining models, constraints and objectives for optimization, and distributions and correlations for simulation. A web presence at rason.com allows this to be written in an online editor and executed through their REST API. RASON is JavaScript-like and can embed Excel formulas too. RASON can be executed by passing the whole script to the API using a JavaScript call. An on-premise version is available too for those who wish to keep execution inside the firewall.
The Solver SDK has long supported coding of models. Since 2010 the SDK has been able to load and run the Excel solver models. The RASON service came in 2015 and in 2017 they added integration with Tableau and Power BI, and this year to Microsoft Flow. These integration steps involve generating apps from inside the Excel model using simple menu commands. Behind the scenes they generate the RASON code and package that up in a JavaScript version for consumption.
You can get more information on Frontline Solvers here.
