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First Look: BusinessOptics


I got a briefing from a company that’s new to the US but that I have been aware of for a while – BusinessOptics. The company was founded in South Africa and started about four years ago. The product itself was first released about two years ago and the initial customers are primarily in South Africa with a focus on financial services, telco and some manufacturing/retail.

The product is described as a prescriptive analytics platform and can be thought of as a combination business modeling and predictive analytics environment. It is cloud based (currently on AWS though moving to cloud agnostic and potentially on-premise versions) and built on Hadoop and Spark. It has a strong focus on APIs and a robust security model. The product has four layers:

  • Data from various sources.
    The data layer can handle relational and non-relational data, flat files, HDFS or APIs etc.
  • Modeling, analytics and optimization – a knowledge layer.
    The knowledge layer handles modeling, integration with machine learning and optimization, version control etc.
  • Automation and APIs.
    These support integration with mobile apps, systems and machines while also providing tools for feedback into the data layer.
  • Visualization and scenario analysis.
    Focused on managing and refining the models in the knowledge layer with charts, dashboards, maps, pivot tables etc. Dashboards can also be embedded in other applications.

From a functional point of view, data is connected to and then mapped into the modeling space. Data is read in, keys identified, discrete and continuous variables identified, tables linked etc. Data in the sources can be easily visualized and displayed.

Within the knowledge layer the user can define functions that leverage the data, creating calculated or filtered data for instance. Such functions, or ideas as they call them, can be built on, with higher level functions using lower level ones. Time based analysis, aggregation, geospatial analysis, analytic and optimization and a wide range of other built-ins can be applied. Within these functions, conditional logic can be added in a table layout for instance. The model can be managed by dividing it up into namespaces and by using tags. Tools for zooming in and out as well as navigating a model allow very large models to be managed. The knowledge layer focuses on allowing analysts to efficiently model complex problems and systems in detail.

Unlike most visual programming/business modeling environments the analytic / machine learning and optimization functions are built-ins and tightly integrated. Analytical ideas (machine learning, optimization) can be defined and hooked up to the data needed to train the models. These ideas can then be input to others and filter expressions defined to control the data flowing through the model – should training data be fed to the analytic model or should a transaction be fed into it for scoring. The vast majority of their customers use analytics as part of the solution they develop and the tool makes it easy to include a number of machine learning providers such as scikit Learn and Spark MLlib.

Views can be added to visualize the output created by these ideas. The formatting can be controlled in some detail. Dashboards can be created that combine multiple visualizations. Visualizations can be parameterized so that users can interact with the view. Dashboard elements can be linked too, so that as users interact with data in one of the visualizations it impacts the others e.g. filtering lists based on selections in another pane. The logic behind the visualized data, defined in the model, can also be viewed through the dashboard to explain the data.

Execution is logged at a very granular level, providing complete execution transparency and this data is available through an API. Changes to the models are logged and versioned. Machine learning components can be set up to automatically update or not, allowing for review and control as necessary.

Once deployed the same model can be used to drive dashboards and aggregated metrics as well as  transaction-level execution.

More information on BusinessOptics can be found here.


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