Is Tableau the glue Holding the Data Science World together?
Being a Data Professional can be broken down into 3 major categories:
- Data Engineer
- Data Scientist
- Daya Analyst
There are obviously other titles out there, as well as combinations of those titles in order to form what a company or organization designates for their employee. In order to make this simple I will just stick with those.
My hypothesis is this — Tableau is the glue, or one of the essential bonding agents that holds, even connects all three of the above positions together. I mean except for data… So let’s look into each one and figure out where Tableau sits in the lifecycle and how it may be useful to understand some of the basics of Tableau in order to be a better professional in the industry.
First: Data Engineer. A Data Engineer is the position I like to consider the base of the pyramid. Data Engineers are in charge of the architecture, collection, and lifecycle of data. That means setting up the databases, warehousing and lakes used for all other organizations in the company and all other professionals that work with data (Scientists and Analysts).
One of the great parts of Tableau is Tableau Server. If a Data Engineer is going to be responsible for organizing the architecture, the structure and movement of massive amounts of data. It must be able to understood by others outside of the Data Engineering organization. That is where Tableau shines, an easy way to visualize streaming data from all sources in the business using some of the handy data tools that have actually been optimized to interact with technologies such as Hadoop and Redshift.
Second: Data Scientist. A Data Scientist is a position that often focuses on the future — Trying to get an understanding of the data in order to make future decisions. This is where some of the positions overlap a touch, although for this article let us think the majority of Data Science is based on creating machine learning applications and running analysis for the future.
Tableau if often used as a first and last pass look into the data. Being a very easy tool to quickly look at data in many different dimensions, a Data Scientist can move through the Exploratory Data Analysis portion of an experiment very quickly. Yes this is also possible in a notebook, but the ease of sharing those visualizations has been optimized with Tableau in dashboards and interactive visualizations.
Finally once an experiment is complete and we need to convince the management that we are going to 10x the business! Well maybe not 10x all the time but by some degree, change the trajectory through predictive modeling and a deep discovery that is not easily discernible without visualizations.
Third: Data Analyst. More often than not this position is the main consumer of Tableau. This position focuses on the past. “Those whom do not learn from the past are doomed to repeat it”.
A Data Analyst uses historical data to report back on how a certain test, campaign, or initiative resulted. This is incredibly important when thinking about how the company functions, or takes steps towards a more profitable future.
Anyone care to guess the tool used to make sure all those pretty graphs, charts, and dashboards are all in the same place.
