You need data to drive transformational improvements in mission critical systems. We can work specifically on data analytics or data engineering, but we find that one often leads to the other. After all, for your data to do its job, your systems need to repeatedly gather and analyze inputs and deliver results—whether that requires a machine learning data pipeline, cloud-native API architecture or something else entirely.
Say “ergonomics” and most people think of chairs and mousepads. In fact, developer ergonomics serve a similar purpose, but for the brain—and for your team.
We write and use open source code to make sure developers on our team and yours can work efficiently and quickly, rather than trying to remember 80 abstract steps while writing tons of complicated code from scratch. For example, we look for opportunities to encapsulate the functionality required to perform your most important analyses.
Working this way ensures that best practice metrics from machine learning models are built into your software—and a practical understanding of your software plans can inform your models.
Case in Point:
One of the most important tools coauthored by Enplus's founder, in collaboration with Williams College faculty, is Bootstrap Effect Sizes (bootES), a free, open-source software package for R, which is a language and environment for statistical computing. BootES computes both unstandardized and standardized effect sizes (such as Cohen’s d, Hedges’s g and Pearson’s r) and—for the first time—makes the computation of their bootstrap confidence intervals (CIs) easily available.