If you are a business professional, the chances are high that you have either read a report or created a report to highlight the progress of the business growth and challenges. If yes, then spending 10 minutes reading the thoughts below will benefit you.
‘Story telling’ in data is very vital; it enables users to interpret and act appropriately. We all know how to add numbers to an excel sheet or how to make a chart but we do not know is how to tell a story with those charts and numbers. Storytelling in visual reports empowers you to correctly communicate with your target audience and help them understand not only what the graph or table represents but also highlight the key parameters and future trends. This paves the way for a healthier and nourishing discussion on the outcomes and next steps. So, when you finish your hourly goals and progress meeting, all the stakeholders have a clear understanding of the next steps.
Most of the time in reporting, we put a lot of focus on plotting numbers and creating charts trends but lose a very important aspect of data storytelling. We tend to try different visualization of static or interactive data to understand data behavior faster. Often, I realized that making trendy charts has left the key information unnoticed.
So, what approach should we adopt while implementing data visualization? Here are my thoughts …
Start with ‘empathize,’ the word that is aligned and comes with every expert in every field. If you can empathize with how the user is feeling - interpreting the data and acting on it is easy, you have won half the battle already. Empathize helps you define a clear problem statement from a customer perspective.
For users who are still reading, here are some detailed insights.
I recommend the three steps approach to deliver the best data experience to end customers.
First, focus on understanding customers and key business questions that they would like to answer through this report or dashboard. This exercise will help to contextualize the overall requirement or expectations.
Let us take the example of building an insight dashboard for a product manager. The product manager is keen to understand how the product is doing.
- Information about users who are showing interest in the product
- The experience of people who are really using the product
- Is a product able to create enough interest, and more people are expected to buy?
- Reach out to unhappy customers and collect their voice to plan the improvements
First, focus on understanding every pointer in detail and then map these to metrics and data.
Pick a small set of data that is available and spend some time understanding the data. Is there something interesting or different that I need to accommodate during the visualization? Are there any extremely high or low numbers? Also, think about categories and the level of complexity, and how different data points are connected to each other? Often times you will struggle in creating visualization if data is not very good, so as a best practice, work on data literacy.
If you do not study the data and start working on the visualization directly, you might end up with complex visualizations with no meaning.
Once you have clarity on the above question, you have empathized and defined the problem; we can now move ahead to the next level of design thinking approach, which is IDEATE.
During the ideation phase, we must analyze how people consume these data for decision making; this exercise will help in selecting the right visualization for the report or dashboards.
Pick a dataset and stitch the frames in journey maps with the help of the data you have collected in the empathy phase. Create a journey of a user on what elements of the data they would need to answer in each business question.
This will help in the sight and memory that will act to frame up the importance of pre-attentive attributes.
There are various ways you can make the visualization look effortless and give a lighter look irrespective of a lot of information. In data visualization, not every piece of data will fit in every visualization. For instance, if you have 10 numbers, you cannot make a complex visualization. See if you want to come with the new visual language.
- There are a lot of concepts for usage of color, visual and perception, and graph design that suggests how to depict data efficiently
- Once you are ready with the charts, bring the actual data so that you can test
- Not every piece of data will fit in every data visualization. If you have fewer numbers, your choice of the chart will be accordingly. See if that engages you as a user. Test with users
There are times you will feel the data you are getting is incredibly good, but when you reach visualization, you realize you cannot do as much as you wanted. It is not convincing and interesting enough. Also, it is too late to go back. That is the reason we analyze the data before Ideation.
Final step: Evaluate
To evaluate visualizations that communicate or present information, we must validate the visualization with users to check on how effectively a message is acquired by the users:
- What are they interpreting with data?
- Are they getting the answers to business questions faster or better?
- Was it memorable, or did they forget it a few minutes or hours or weeks? We might ask whether the data story encouraged any personal insights, which they were not finding or thinking earlier, and to what level users were engaged.
- Engaged could mean playing with filters, sorting, drilling down, clicks, and how often charts were touched
We should ask if users felt they understood the context of the data and if they felt confident in their interpretation of the story: Did they feel they could make an informed decision after seeing the visualization? Reliability being an important attribute, we might wonder whether some data stories are more trustworthy than others or some visualizations are more persuasive.
If you want to grow in data visualization, work on your data literacy, see the pattern in data. You do not need to be a data expert/analyst/scientist, but be comfortable with the dataset and know how to draw a conclusion and not mislead the observers.
A structured approach of empathy, ideate, and evaluate can be a very effective strategy to effectively meet users' expectations.