5 tips for effective dashboard design for quantitative marketing

Before start exploring the deep cliffs of the quantitative marketing analysis, I would just like to share my personal set of best practices I always use for an effective dashboard design process. Altough these are focused to a typical marketing use case, these are totally generic as they provide a set of analytical starting points and some cooking recipes on how to approach the task of designing a visual reporting environment for your company.

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Getting started with Twitter data analysis using Talend Open Studio

In this step-by-step tutorial I’ll show you how to use Talend Open Studio and the Twitter Components Pack to connect to Twitter, do a simple REST query and build a trivial relevance report on top on it. There’re tons of similar Talend tutorials out there, but no one is focused on my Twitter components pack, which let you do queries and result parsing without writing a single line of custom code. So let’s go into this 101 crash course on how to download tweets and build a real-world analysis on it.

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How to build a predictive model using Talend Open Studio and R

How to build a predictive model using Talend Open Studio and R

There are plenty of scenarios when one would benefit to do a cross-over between Talend Open Studio and R. The first is perfect for even complex ETL tasks, which by their very basic nature involves massive data I/O, manipulation, federation and governance, but it completely lacks any kind of serious statistical tool.

On the other hands, R is an absolute standard for statisticians, with a huge amount of external packages for practically any possible kind of analysis one could imagine, but even simple data operations must be hand-coded. R language is a very expressive and extensible data language, but one perhaps would prefer to spend time reasoning on the predictive model, rather than writing code to get the data out from the database. This is particularly true in data exploitation scenarios, but also in rapid prototyping and, generally speaking, in the whole business world.

If it’s not enough, R is basically a data language plus a command line executor. This is historically common for statistical software (just think to SAS) so it’s not a flaw on its own. But in real life Business Intelligence life-cycle, you probably have a corporate standard, a service bus, a protocol for data transfer and so on. A better interface with R is really advisable.

This is possible using a custom optional component made by me for Talend. In this tutorial I’ll show you how to use R to build a simple predictive model with data coming from Talend and how to get results back to Talend himself, for all your ETL good habits.

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