Which Tool Is Easy To Handle Complex Data?
Any advice for the below query will be highly appreciated.
Would it be easy for someone to learn Anaplan if they have a Salesforce marketing cloud certification with the given skills set? If not, in how much time he can learn the basics of Anaplan? Recently, one of my friends was assigned a task to work on complex data, and suggested he use any data tool.
Thanks
Answers

@raavikant So my opinion, someone who has used excel formulas a lot would likely find it easier to learn and apply Anaplan as it uses the LOOKUP and SUM function a lot which is equivalent to Excel's VLOOKUP and SUM formulas. For someone who is more accustomed to structured programming languages such as Javascript, VB Net, etc .. it would really depend on the individual and their ability to adapt their mindset. I've seen very good structured language programmers struggle with Anaplan but have also seen average structured language programmers do very well picking up Anaplan.
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@raavikant Just realised I didn't really answer the fundamental question you had which is whether Anaplan is a good tool for handling complex data. As an analytical tool for pivoting data, it is awesome. It can handle millions/billions of data permutations and generate an output within seconds which would take hours if not days if you performed them on Excel. As an ETL tool, I would highly NOT recommend using Anaplan for performing data transformations, specially complex ones. For example, if you require transformations that can only be performed using loop logic (while, for next) it would be a convoluted exercise to do it in Anaplan. Though note there are some transformations that may require loop logic in structured languages like Java, VB NET, C that Anaplan can execute via a simple formula, in particular logic that involves Time dimensions. Anaplan is good at simplifying rule implementations that involves looping through time. So the approach I would recommend is to perform data transformations outside of Anaplan and then use Anaplan to ingest the transformed data for data analytics purposes.
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