Hi community,
I'm working as Chief Scientist at a Marketing Services company. We're extending data analytics into regression models and I'm exploring various analytics platforms.
In your opinion, what is the easiest to use analytics platform? Thank you.
I have found both Knime and RapidMiner easy to use and powerful. That applies to producing numerical output, graphical representations, and PMML files. The latter helps if one wishes to produce diagnostic or recommender systems automatically, with no human intervention needed. I would, however, always have human oversight available and do a performance check occasionally.
Both offer ways of integrating with Python or R should you wish to do something unusual. They are quick-to-learn for beginners.
I often receive a dataset and do a quick analysis in, say, Knime before turning to SPSS for the heavier lifting.
PowerBI and Tableau are excellent BI tools.
Anaconda with Python (or AWS Sagemaker) are excellent for modeling and ML
So, most of our clients tend to like Qlik or Tableau for BI. It also depends on your data sources - if you're pulling from a newer cloud ERP there's generally more to work with in an easier fashion - but people also work wonders going straight to the data tables.
Power BI from Microsoft is a good option - mentioning regression analysis makes me think of Excel, and Power BI uses Excel as the user interface (sort of).
But depending on the ERP, you'll also want to take a gander at Host Analytics, Adaptive, Hyperion - not totally in the BI space, but may provide what you're looking for.
And between all of those is going to be a featureset that specializes in the data you're wanting to work with in the format you can access it. And how that clicks for you is the best indicator of what will end up being easiest for you to use.
Our practice for software selection is based on giving clients options between the best solutions, but it's so individual on the client side - we really don't know which software application will ring true in approach and featureset to the client.
We tend to make sure whatever choice they end up with is a proven product supported by solid, proven partners - because bad implementations of good software = junk.
Tableau is the best analytics platform
@reviewer1399407 what are the pros of Tableau vs other tools? Thanks.
There are many tools out there. You have to define the type of users and their desired user journey. Like buying a car, there are brands, performance, and driver's preferences on the feel of the vehicle to consider. I would identify the users, list out use cases, trial a short-list, and understand what drives the preferences.
For example, IT departments love Qlik and PowerBI particularly if they are on the Microsoft platform. Most of our clients end up having to outsource or build PowerBI central utility teams. The software is accessible and Agile but not particularly successful in promoting analytic freedom. Tableau on the other hand is accessible and Agile but also easier for non-experts to navigate the features.
You mentioned regression models and in this situation, I wouldn't recommend these unless you have statisticians already comfortable with these tools because they aren't focused purely on statistical applications.
Another example, engineers, academic departments, and corporate research teams might like SAS, SPSS, Matlab, Knime, and RapidMiner. These are specific software to develop low code mathematical models, and also have very powerful scripting to go beyond the basic features of the software if necessary. I would say regression is a basic feature in all that I mentioned. But they are geared toward mathematicians, statisticians, and engineers.
My personal preference, if I am looking purely on regression and the easiest to use for a non-statistician, I would say Alteryx or SPSS. If I am looking to consume insights and have some statisticians with coding skills, I would say a BI tool like Tableau and have my statisticians build R / Python scripts behind it.