Google Data Studio and Tableau: What is Data Visualisation?
The Purpose of Data Visualisation
The purpose of data visualisation is to understand and communicate
data to others. By visualising big data we can identify trends and patterns that
could assist and improve business decision-making. The most popular platforms used
to discover insights from vast quantities of data are Google Data Studio &
Tableau. We create data visualisations to see what happened, why it happened
and to learn what to do next. A data visualisation report usually follows these
steps in its structure from beginning to end. The heading should communicate the
most important metrics to the viewer first - key performance indicators and target
performance. The image below is part of a website traffic report created in Data
Studio. It features an analysis of visitors by channel and location and
includes key KPIs in the header. Templates like these are available on-platform
and are perfect for our readers to analyse traffic, sales or marketing
strategies for their blog or business.
The Understanding Process
To create an effective design for a data visualisation, the
3 stages of understanding must be considered to cater to the viewer of the data
visualisation. The 3 stages are perceiving, interpreting and comprehending, which
are all influenced by the viewers current knowledge. This includes their previous
knowledge, interests and the meaning of the visualisation to them.
- Perceiving - “The view is attempting to read the chart and to understand what is being shown.”
- Interpreting - “The viewer takes their perceived values from the previous phase and applies meaning to them.”
- Comprehending - “The viewer concludes how this data
visualisation matters to them, within a personal reflection. They consider how
this information changes what they had already known about the topic. There
could be an emotional impact, depending on how they relate to the topic. The
viewer’s understanding can be concluded with them taking action, such as making
a decision, or for the knowledge to be used for future action.” (Kendal, 2021)
Choosing a Data Visualisation
To prepare a data visualisation, the first step is to ask ‘What
is the nature and purpose of the visualisation?’ This is usually determined by
the type of data, qualitative or quantitative. The second step is to ask ‘Is
the idea being declared or explored?’ This is found by deciding if the idea is
being communicated or examined. If the idea is being communicated it is being
declared. If it is being examined, it is being explored. An exploratory data
visualisation would seek to answer questions about why something happened, for
example, why there was a drop in sales in the last quarter. Rather than presenting
an answer, it is seeking to answer questions. A declarative data visualisation
would present a formal statement such as quarterly sales figures using
quantitative data. The final step is to identify the tools and resources required
to make the final visualisation. The image below features declarations about
the age and page time of users in Data Studio.
(Duffy, 2021)
Google Data Studio and Tableau
The most popular platforms used to visualise big data are Google
Data Studio and Tableau. Data can be structured and unstructured. The data can consist
of names, dates, values, comments and locations. When a data set is connected in
Data Studio, it becomes a data source, which can be edited without making
changes to the original data set.
Exploratory Data Visualisations
Within exploratory data visualisations are visual discover
and visual explorations.
A visual exploration is a type of data visualisation that
aims to “explore in an open-ended way, without having to be skilled as a data
scientist or business intelligence analyst.” (Kendal,
2021) In Google Data Studio this type of report is made with their ‘Explorer’ software.
Explorer “acts as a scratchpad”, is “temporary unless you save” and “helps you
find insights by streamlining editing and viewing experience and applying
filters quickly.” (Tour the Data Studio Home page, 2021) They are often used to
test and confirm a hypothesis “when there is an assumption about what has
caused an event”. (Kendal, 2021)
A visual discovery would be
classified as when there is no preassumption about why something happened
prior to exploration, as the visualiser seeks to discover why an event happened.
This involves identifying trends and patterns by visualising data in chart form.
The image below features an exploration of the origin of new user
traffic and browsers use inside Data Studio.
(Duffy, 2021)
Sales Data
Sales data can be analysed in just as much depth as website data can. This can be very useful to identify high value customers or successful revenue streams. For example this sub-category analysis report made in Tableau analysed sales by product sub-category, and we can see phones and chairs achieved the significantly higher sales than other categories. See the full Tableau beginners guide here: Link
Storytelling
Storytelling in data visualisation is a way to add interest, provide context and add an emotional aspect to the data. It is used to “increase the impact of the data visualisation on the viewer, allowing them to understand it as well as relate it to their own experience, and increase viewers ability to make decisions based on the data presented.” (Kendal, 2021) If the viewer can connect to the story behind the data visualisation, they are more likely to be encouraged to take action, for example to take a new business direction. Similarly, revealing both negative and positive data can help the viewer to emotionally connect to the data. By including negative data, the viewer can identify the most important metrics being presented which can bring attention around the most important aspects to discuss and set possible targets for. This is also true when there is a need for change due to new challenges and threats in the market.
Author: Elizabeth Duffy
#DataVisualisation #GoogleDataStudio #Tableau #Explore
References:
Kendal, N., 2021. Digital Marketing Analytics and Metrics: Data Visualisation.
Analytics.google.com. 2021. Tour
the Data Studio Home page. [online] Available at:
<https://analytics.google.com/analytics/academy/course/10/unit/2/lesson/1>
[Accessed 24 March 2021].
Duffy, E., 2021. Marketing
Website Summary. [image] Available at:
<https://analytics.google.com/analytics/web/#/> [Accessed 24 March 2021].
Kaur, P., 2017. Tableau for Beginners – Data Visualisation made easy. [image] Available at:
<https://www.analyticsvidhya.com/blog/2017/07/data-visualisation-made-easy/>
[Accessed 25 March 2021].
Lukas at Pexels.com - "pexels-lukas-669622.jpg"
Story telling via data visualisation is one of the most effective ways to achieve your aims as it creates an emotional bond with the viewer and provides them a context. In a world where we are besieged by data but desperate for meaning, data storytelling helps connect the dots.
ReplyDeleteAuthor: Deirbhile
ReplyDeleteData can be very powerful for a business I agree that there should be an emotional aspect to data visualisation for viewers to understand and relate to it well. Storytelling while presenting data can change its impact dramatically. Using the right form of communications when presenting data can also help to give the reader or audience more context to allow them to understand and engage with it. Having an emotional element to the data can truly impact the viewer when making decisions from it. The need for data storytelling will always come back to the human need for facts in order to make a decision.
Visualizing data is a priceless tool for making sense of lots of data and for referring back to it at a glance. It is invaluable for many who can process graphical information much more effectively than it's raw form.
ReplyDeleteI am delighted to learn of the two resources, Google Data Studio and Tableau and feel inspired to make use of these thanks to your article.
Data visualization is an incredibly useful tool that should be in every business owner's pocket!
ReplyDeleteI enjoyed your overview of exploratory visualizations and believe that they offer an intriguing contrast to declarative visualizations, which are statements that need to be communicated rather than data produced for the purpose of asking questions and exploring. Declarative visualizations often come in the form of communications such as a presentation of quarterly sales data.
ReplyDeleteThe storytelling aspect of visualization is one that is often forgotten about. Developing a connection between audience and the data is important as you’ve outlined, but the creator must also strive to make the audience think as little as possible, in a similar way to how UX Design is approached (Kendal, 2021)
Reference:
• Kendal, N., 2021. Digital Marketing Analytics and Metrics: Data Visualisation.