What is our primary use case?
The PoC we did was for the oil and gas field mostly, as well as the aerospace field, to optimize supply chains. We wanted to see what level of information we could gather from using this tool and how it would help us. We were looking to become a reseller for Darwin and to provide services through it to our clients.
We wanted to pitch it to our clients, but our PoC indicated it was not feasible.
How has it helped my organization?
It's a good tool for a beginner business analyst. I don't think it's good for advanced analytics.
When I created a model, versus when Darwin created a model, my model seemed to perform better, simply because I have more knowledge about the actual business policies and problems that we have. Darwin was just giving me a beginner, data science-type assessment.
It might have improved our ability to tackle more complex problems by making data science more approachable and operational but not by much. It improved that by about 1 or 2 percent.
It didn't increase efficiency or productivity on our team because we had to spend more time trying to figure out how it worked rather than actually working with it.
It did help us to convert data into knowledge. It did provide a little bit of value.
What is most valuable?
In regards to removing null values and the like, it did do so but that's a pretty basic task. I don't need an entire tool to do something like that. I can do it very quickly in Python or R.
In terms of streamlining a lot of the low-level data science work, it does a few things there.
What needs improvement?
The solution's ability to capture complex relationships over time and the resulting accuracy of its predictions could be improved.
They could also improve customer relations with education on how to use the tool. It took us quite a while to figure out how things were put together so that we could get things to work and provide proper feedback to our leadership. The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition. These things should be a priority for them, in my opinion. Knowing how to use the tool would have given us more time to play with it instead of just trying to figure it out. They should "game-ify" it more.
Darwin could do a few other things better such as automating determination of whether certain values need not be removed and that certain parameters should not be removed. And their UI could use some speeding up.
For how long have I used the solution?
I used it for three months to do a PoC.
What do I think about the stability of the solution?
Darwin is stable, but it just doesn't provide the functionality that an analyst would need.
How are customer service and technical support?
We asked questions and there were a couple of qualified people we spoke to at SparkCognition.
Which solution did I use previously and why did I switch?
We were previously using open-source software such as Python and R. We wanted something simpler and easier and faster to use. That's why we looked into SparkCognition.
How was the initial setup?
It took about two months to deploy it and we had one month for testing it out. Once we had direction, the setup was pretty straightforward. But before that, it was pretty complicated because we weren't certain about where to go or what to do.
Our strategy was to see how we could use the tool as a business analyst/low-level data-planning tool to and to see if we could improve processes by using it, even if we did have Python and R. But it was quicker to just Google around and find things online to fix stuff, rather than for a client of ours having to have to pay for this tool to make that happen.
What about the implementation team?
We did it all internally.
What other advice do I have?
The biggest lesson I learned from using Darwin, honestly, was that they should interface with their clients much quicker and much more easily. They should make that process seamless to make sure clients are up and running ASAP so they can get their feet wet instead of wasting about a month of work.
We don't have any plans to use it right now but we're open to using in the future. We're telling them this stuff because we want them to improve this product because we did see value in it. We did see the idea behind it, but the execution was not done very well, especially when it comes to tools to get people up and running on it quickly instead of spending weeks on end going back and forth to figure out logistics.