IBM Watson, Tableau, Si.Sense, and Microstrategy are but a handful of the data visualization spinoffs that the industrialization of analytics is producing. The data supply chain is indeed in full maturation stages. Behemoths IBM and Oracle were previously in the business of selling hardware (servers) for the mass storage and aggregation of information.
However, today they are realizing that the actual resource they were storing (data) is worth much more than the servers themselves. An analogy we can liken to the industrial revolution and the surgent use of oil derived petrols to power everything from automobiles to aircraft and everything in between.
Hence, the silicone revolution has produced this unlimited resource we now label “data”. Previously only accessible by green flashing C:, and subsequently by infamous world renowned Microsoft Excel in columnar databases; the data revolution is changing. Spinning ones and zeroes into easily digestible coherent colorful graphics is an art in and of itself.
Programmers and savvy computer scientists were previously the norm for deriving user interface products for the limited sets of eyes of C-Suite execs looking to advance their causes. Today, however, data is so profuse, that other, easy-to-use (relatively speaking) interfaces are popping up like weeds in the springtime.
The fundamentals of using data visualization tools as a means of communication comes down to three essential elements. Nothing complex, very common sense, but one must understand these three keys:
1. Who are you speaking to? Who is your audience?
As with all communication basics, we cannot convey messages to children as we do to full blown educated experienced adults. It just wouldn’t work, and that, both ways. So, rule number one, when setting up your data visualization report, is to establish your audience parameters. Remember to be answering questions that may arise from your viewers. Use both creativity and logic to brainstorm before even beginning to search for the RAW data sets. The audience you are addressing should set the stage for the two next questions to be answered.
2. Why are you communicating this information?
They say you learn a lot more by listening than by speaking. So be it for data visualization too. Intent listening is the key to providing the correct visualization story. Listen to all involved stakeholders, and zone in on the key stakeholder’s needs to establish why you need to get from spreadsheet to story. In other words, what message are you trying to convey? eg. A sales dashboard developed for store managers (in retail) will not need to distribute the same message as a data visualization for shareholders.
3. Where is the data (the resource) coming from?
With the proliferation of data and the ability to store it growing exponentially, we now have more than we need in most cases. Before you start even thinking of producing a visual for your stakeholders, make certain that your data is being collected and sourced from the correct medium. Erroneous information at source could gravely impact the final storyboard you are trying to create.
A Case In Point
We recently created a visualization with a data set downloaded from The US Department of Transportation Bureau. The RAW data contained a multitude of elements describing late aircraft arrivals with US regional flights. This visualization may have been created in numerous ways, depending on our audience and the message we were trying to convey. In our particular instance we wanted to help travelers book flights that have the highest success rate of on-time arrival.
Hence, the two key filter metrics on our dashboard are “Which month are you planning to travel?” and “Which city are you flying into?” Answering these two questions results in an INDEX and RANK of all the airlines serving your time of travel and destination.
To view our BLOG post and Dashboard Follow this LINK.
If, for example we were using the underlying RAW data to present on time competitor metrics to C-Suite airline execs, the dashboard would take on a whole new look, although using the same underlying data.
The Takeaway
Once you have the answers to these three fundamental questions, only then can you begin sketching out the visualization that you need to produce. Yes, the first draft of your visual should be a pencil and paper sketch of the message you will be communicating. Do you have the correct data sets to produce that visual? If yes, then proceed. If not, then answer the three questions to get what you need, and only then can you begin the process of spinning your raw data into meaningful storyline dashboards.
Please forward any questions to [email protected]
