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Why Agnostic Software Is Important

Ginger Phelps June 15, 2017
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Programming has redefined itself over the years from simply writing code to now finding effective solutions to any problem related to software development, algorithm, analytics, etc. These solutions have required the help of various software tools that are not always compatible with one another.

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Differences in software tools (including programming language compilers and libraries) have become exponentially varied. These variations have led to the need for agnostic solutions.

The demand for agnostic software tools has led to a change in the production and functionality in computers and software. This change makes utilizing analytics easier, faster, and cheaper for data scientists and analysts and allows them to develop more “sophisticated” models which have more “value” (e.g. predictive power or decision accuracy) for organizations. In analytics, more data means the potential (but not guarantee) of “better” business outcomes.

Before the shift towards more agnostic software, companies were often restricted in production. If a company was standardized on a single computer programming language, they were limiting themselves to the functionality of that language. More importantly, they were limiting themselves to hiring employees who must know how to “build” and “maintain” (i.e. write computer programs) analytics in that language.  

This restriction limits companies to only being able to use software tools and products that help them to create, design, and program analytics that support that language. These limitations cut back on productivity and often lead to missed opportunities of acquiring “valuable” analytics.

The issue of computer programming languages and software not being agnostic is still persistent today. These problems have led to the growing importance of creating a software that is agnostic to multiple data science languages and different types of models, which is why we have developed FastScore.

We have seen the importance of agnostic software through the functionality of our scoring engine. Each piece within the scoring engine has its own momentum and speed, so it is necessary that every piece be agnostic into what it interfaces into.

FastScore is a cloud-ready enterprise software that executes, monitors, and manages new and existing production analytics. FastScore is designed to work with all common data science languages and any type of model, while seamlessly connecting to virtually any data source.

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Choosing an agnostic, multi-language, product or tool provides significant long-term advantages such as freedom of choice on the data science development team, while simultaneously keeping production manageable. By acquiring agnostic tools and products, companies have a more defined way to move models into production that decreases their time to first deployment. Open Data Group is leading the realization of this potential in the analytic software tool space.

 

Topics: FastScore, Open Data Group, Predictive Model Analytics, Analytic Deployment, software compatibility