Understanding UX for Enterprise Applications

Enterprise applications must be ready for use in 8-16 weeks.

Here's why:

For B2B applications, time-to-market is a big factor contributing to user experience.

In earlier days, projects were measured in years.

Today, you have 8 to 16 weeks max to build an application that
- integrates fast in the entire project team
- processes any data

If this expectation is not met, your application will be discarded.

It’s similar to you downloading an app from the Apple store.

You expect it to be intuitive and easy to use, and you delete it when it’s not the case.

That's why, at Ferris Labs, we help our clients build applications at an enterprise level and go to market within 8-12 weeks.

_________________

Who we are.

Although we’re a new company, we’re experienced technology experts and entrepreneurs with a palmarès of successful product and company launches. Our passion lies within DeepTech and Data Management.

_________________
What makes us tick.

We believe that every analytics and data professional has the right to a positive user experience. It is our passion to make that experience for everyone efficient, transparent, secure – and easy to use. That’s the road we’ve taken, and we will not stop until ferris is synonymous with the best possible analytics and data management suite available.

We have built the complete and most integrated enterprise analytics platform, with numerous pre-built data sourcing, ingestion and transformation frameworks. Including a Data Science Workbench and integration points to your favourite BI tools. All built on fully virtualized data clusters and equally suited to run on the cloud or on your in-house infrastructure.

ferris is guiding, supporting and automating analytics and data science on every step of the way. From sourcing new data to analyzing it, building AI models, and automating front-to-back jobs on an enterprise level. With ferris we offer a tried and tested analytics and data platform, ready for your centralized data science, data engineering and not to forget data operations – enterprise enabled and service based.

Homepage:

#bigdata #ux #linkedin