We're on a mission to simplify the complexities of your data ecosystem using hyperautomation. With Cube, a single nontechnical user can do the work of an entire data team.
Our team has deep experience working at data analytics tech companies, where we collectively experienced the need for deeper automation across the data lifecycle.
We've seen how most organizations currently try to manage their complex data workflows through a mixed bag of manual, disconnected solutions at each step of the chain with humans exerting significant effort to connect the dots.
These manual tasks include orchestrating data consolidation from a large number of sources, rebuilding analytical workflows from scratch every time new data becomes available, building unwieldy Excel spreadsheets to handle multi-step data transformations, and hiring expensive data science talent to write ad hoc code.
To solve this problem we’ve built an easy-to-use, comprehensive solution, architected from the ground up with automation at its core.
At Cube, we believe that any person using our platform should be granted data 'superpowers' - the ability to produce 10X the results of a regular human.
Our core principles that enable this include:
Automated workflows eliminate the need for manual tasks, and improve operational efficiency by 70-95%
No-code, easy to use point and click interface for both technical and nontechnical users
We've built a full data lifecycle platform that handles collection, processing and visualization
The Cube Difference
While many companies have created simple, one-size-fits-all tools for basic data visualization or data warehousing, there are currently no tools that can successfully automate the diverse, chaotic, and complex workflows that are the ugly reality of day-to-day data analytics.
Cube is the first platform that has been architected with the deep capabilities and flexibility needed to automate these tasks. Our secret lies in Cube’s no-code intelligent building blocks, which leverage advanced configurability and responsiveness to adapt to even the most complex workflows.
Through work we've done with existing clients facing these problems, we've been able to reduce data operating costs by 70-95% across a diverse set of use cases.