Data Science Workbench
Manual data preparation and extraction is expensive. And so are data scientists.
Yet, they spend 60% of their time cleaning and organizing data.
Why? The answer is simple. Information is obscured by non-standard and inconsistent data formats.
It’s logical to assume that machine-created information is also machine-readable, but it’s not. There would have to be one universal, standardized data structure for all organizations to follow.
If you solved the obscurity problem, you would reduce the costs to advance analytics and improve decision-making and problem solving organization-wide.
Deliver Valuable Business Insight Faster
Augment data science activities like data preparation, pattern searching, and building machine learning models. Resources such as Python, NumPy, and TensorFlow are transformative, but have frustrating limitations for large document sets. While not open source, Grooper’s open cockpit design provides transparency and fine-tune control over settings.
Grooper combines the power of open source tools with native data and document processing tasks to function as a highly efficient data science workbench.
Featured Case Studies
Thousands of companies choose BIS to enrich products and services with unique data-centric solutions. Here are some of their stories.