From a company finding the majority of its customers in the industrial sector, one might expect a customer interaction similar to how other Tech Giants behave. Nothing distinguishes RapidMiner more than any other company or application I used over more the last decades.
As a clinician (anesthesiologist and emergency physician), once an absolute rookie in data science I tested several platforms able to solve my data related problems. And problems, I had many of them from extracting unstructured data from electronic health records (EHRs) to model deployment and monitoring of disease specific datasets from critical patients with stroke, sepsis, covid-19, etc.
I could never have accomplished these tasks without the close relationship I developed over the years with amazingly interesting and clever people developing for or working with RapidMiner technology. A few things I experienced during that journey:
No matter which sector you work in, listening to questions other data geeks are facing, reading the solutions proposed by experienced people in a certain subdomain always gives you a step forward to solve your own problem. I never noticed such a vivid community as the one I visit almost daily with almost 700K members and over 15K discussions and answers. As most other companies have only a customer service, RapidMiner always valued the direct feedback from any customer or platform user. This definitely is the reason RapidMiner is a very successful application as it keeps the finger on the pulse. The company always knows which direction customers wanted to get their platform to grow.
Besides the passive inflow of information from the customers, RapidMiner is also actively interacting with its customers. National and international meetings and webinars are organized to get the latest trends and insights in the developments the company is working and additionally provides lots of time and space to have customers present their accomplishments.
As many data science applications evolve in the direction of monolithic black boxes, RapidMiner used a completely different approach. As much as developing new technologies within their platform, they offered solutions to integrate existing code (R, Python, etc) within their processes which saved many hours of coding as several tools are available e.g. in Python and provide links to databases, websites, web services, feeds, social media, etc.
As a clinician and data scientist, I need to spend a lot of time explaining the steps I took between raw data and the final model I developed. This is so often overlooked by many companies and from my point of view a significant reason the AI/ML revolution could have positioned itself at a higher level in many fields of directly aiding citizens to become healthier and more efficient.
The most striking argument I have to stick to RapidMiner is the fact that I only need one application to get most of my work done from ETL to data visualization which makes the amount of coding required to achieve my goals to a minimum. RapidMiner evolved in such a way that people with almost no experience in data science can cluster and classify their data, make predictive models, deploy models and monitor any drift in a way large industrial processes are dealing with this.
Additionally, the way the user interface is built, any process can be built starting with tangible building blocks which can be understood by any audience which will at one moment in time require insights in which magics you have created.
Although RapidMiner has most of its customers positioned in different industries more than in research departments, RapidMiner always showed lots of interest and provided me with valuable advice.
It may be very subjective but the word RapidMiner family is how I see this community and company.
As life is an inflammatory pathway, generating interesting data, RapidMiner will always be one my everyday carry essentials.
Sven Van Poucke, MD, PhD
https://www.researchgate.net/profile/Sven_Van_Poucke/research Review collected by and hosted on G2.com.