Alteryx Analytics vs QlikView

When we \ evaluated this product, we were focussed on doing advanced analytics with our data like the predictive, spatial and descriptive analysis. In all of these categories, Alteryx was the best. It took very less time to do this analysis in our environment. So, we opted Alteryx as the best solution.

Our shortlist included Tableau, a newer version of Microsoft's SSRS and Qlikview.

We also knew what Micro Strategies and Business Objects had to offer from our experiences with them at other companies, and knew what we could afford. We eliminated them on price and the complexity of set-up.

We liked QlikView's in memory, associative model, and self-service capability.

With "in-memory", everything that gets consumed from a dashboard is loaded in memory so it is incredibly fast. Although Qlik touts its mobile distribution capabilities, that was not a huge differentiator for us, but is something we are now exploring. Microsoft and and BO do have in-memory capabilities now.

The associative model is patented by QlikView. Basically it starts to understand the associations in your data, e.g. if A=B and B=C, then A=C. It means you can build a Qlikview model very quickly. In traditional data warehousing, you work with users and understand their requirements, refine the data model, start to physicalize it, tune it, build ETLs. It's a 3 month at best delivery cycle. That doesn’t work here at this company. Our business users will not wait for you. Our business is dynamic, we are launching new products all the time. Instead of going through an arduous process, you just load data into QlikView and build an associative model. It link things up. If it doesn’t work, you can change things very quickly. You don’t have to write data definitional language. Where you get into trouble, is if you load very large data sets into QlikView, memory not as abundant. They are releasing a tool called data explorer which allows you to do a hybrid approach – load some in memory and some in database. If data is going into frequently used dashboards it goes into memory. If infrequent access is required and the data set is very large, it makes sense to leave it in the database.

We also have a company called DataRoket that builds connectors for us, e.g. load this relational data into QlikView. They have built adapters for QlikView-Hadoop integration