Data Science Workbench
Manual data prep and extraction are expensive. So are data scientists.
Yet, they spend 60% of their time cleaning and organizing data.
Why? The answer is simple. Information is many times tough to access due to non-standard and different data formats.
It’s logical to assume that machine-created data is also machine-readable, but it’s not. There would have to be one universal, standard data structure for all organizations to follow.
If you solved the accessibility problem and were able to use your data, you would cut the costs to advance big data analytics. As a result, you would improve decision-making and problem solving all through your organization.
Deliver Valuable Business Insight and Deploy Models Faster through Grooper’s Data Science Workbench Tools
Augment data science tasks like:
Tools such as Python, NumPy, Apache Spark, and TensorFlow can transform the way you work with data, but have frustrating limits at extracting large document sets. While it is 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.
Check out how we are enabling faster and more effective data science tasks every day:
Featured Case Studies
Thousands of companies choose BIS to enrich products and services with unique data-centric solutions. Here are some of their stories.