The first week in the Data School has definitely been fun, with lots of new faces to meet, places to see, and pieces of software to learn. However, as with any new job, it has also been a daunting process too, with a plethora of information to take in, and a number of tough Alteryx and Tableau Prep tasks to undertake. It is therefore of little surprise then that the completion of these tasks has subsequently allowed me to recognise an initial area within data visualisation that I needed to better comprehend, namely: understanding the importance of data prep.
What is data preparation?
So what is data prep? Put simply, data preparation concerns the manipulation of raw data to facilitate more expeditious and precise data analysis. This can be achieved through a multitude of approaches (e.g., data cleansing, data transformation, data blending), using varying pieces of software to expedite the process (e.g., Altair, Trifacta). However, in the context of the DS, data preparation is largely undertaken within either Alteryx or Tableau Prep. Having said that, it is important to note that although both pieces of software can be used in conjunction with the other, potential clients are likely to prefer you use just one; primarily to reduce the administrative load of the stakeholders, and to avoid any potential errors that may arise from using differing operating systems.
My initial take on Alteryx and Tableau Prep
How did I feel about data prep? To be completely honest, both Alteryx and Tableau Prep were a complete mystery to me before joining the data school, not aided at all by my minimal experience within the realm of data preparation. Nonetheless, after an introduction to both pieces of software during the first couple of days last week, I have definitely been able to form some initial opinions on the two of them.
Alteryx: Looking first at Alteryx, it was fairly hard to get used to to begin with, largely due to the unfamiliar format and the scores of tools available to use within the software (there are over 100 tools in total! Granted, we have only learnt around 10 of them so far though). As well as this, it really emphasised to me how important attention to detail is within this type of work; for example, if one does not select the correct data type for a specific field (e.g., ‘Unit Cost’ as a V_String instead of a Double), or they misplace a punctuation mark, then the whole workflow could be disrupted and one would have to subsequently ‘retrace their steps’ to troubleshoot the problem. Ultimately, however, it became clear how useful the software is within data visualisation, allowing one to efficiently clean and transform data to suit a variety of needs, whilst also streamlining the process through the utilisation of a ‘drag-and-drop’ type interface.
Tableau Prep: Looking now at Tableau Prep, the configuration of this piece of software was much easier to comprehend, taking on a much more simplistic form; this is further emphasised by the aesthetically pleasing nature of the software, encompassing the same ‘drag-and-drop’ format as Alteryx, whilst making use of a cleaner looking colour palette and interface. It’s this simplicity, however, that forms the root of its downfall in my opinion, causing me to question myself at every step and wonder if what I'm doing is correct. As well as this, Tableau Prep does little to aid the user in exploring the uses and applications of each tool, seemingly sacrificing this level of detail to preserve its austereness.
All in all, it’s clearly down to personal preference which piece of software an individual chooses to use, however at this early stage in my data visualisation journey, I am far more drawn to Alteryx and its more logic based interface. Give it two months and I may have a change of heart, but I will definitely need further practice with both tools to allow me to say for certain.