In a world where all kinds of data are being generated at a fast rate every single day, there is an increasing need to make sense out of these numerous lines of information. It is nearly impossible to be done manually by going through millions of lines of data to derive analysis out of it (every single time!).
Depicting and consuming all this data in a comprehensive way to derive meaningful analytical insights has become a form of art (which we proudly mastered). And the most artistic of the required techniques is Data Visualization.
If you are here only for the white paper, scroll all the way down. The article is great too though. 
It helps the analysts to engage with the data at a much deeper level. The visuals immediately grab the attention and point out trends and inconsistences, and thus help the humans arrive at meaningful insights. When a visual like a chart or graph is viewed, the analysts can almost immediately spot underperforming/well-performing metrics and in turn analyze related factors to identify the root cause.
As compared to trying to make sense of raw data, converting the data into visuals makes it much easier to consume large volumes of data in a meaningful way. There are various tools like Tableau, Power BI, QlikView, Qlik Sense, Looker, Excel, etc. that are commonly used to transform the data into effective visualizations.
It is a common mistake to use up “all the data” and somehow want to depict it all at once without ensuring to link the visualizations into an easy-to-follow story.
We do not always need all the data to arrive at the right insights.
Creating visualizations is not the end of the journey to reach the right decision. These visualizations need to be arranged or presented in a way that makes sense, that tells a story to the viewers.
The art of building the integrated view using the data, the visualizations, and the business question into a meaningful presentation or dashboard is what we call Storytelling with Data Visualization.
1. Who is the target audience? What is their business concern?
2. Which datasets are relevant to the business question?
3. What is the most important thing that I want to show my audience as the result?
These questions provide an effective starting point to build your story.
Before you create your story with stunning visualizations, you need to make sense of the data. Begin by defining the story that speaks to the audience. Building an effective storyboard with data is a process, that involves the following steps:
![Expert guide to Data Visualization 2020 [+White Paper] 1](https://amplifyanalytix.com/wp-content/uploads/2020/09/Amplify-background-image-1-e1600688625739.jpg "Expert guide to Data Visualization 2020 [+White Paper] 3")
1. Know your audience
Get acquainted with who might be reading or viewing your story. It will help in understanding how the dashboard will be used and what level of data should be represented in various parts of your story.
2. Understand the end goal
Ensure that your findings would answer your audience’s key questions. Focus your insights based on this end goal. For example, the end goal could be to increase sales, increase customer satisfaction scores, increase customer retention, etc.
3. Analyze with impact in mind
Ensure that you keep on track and do not move far away from the key point of focus.
4. Build a story dashboard
Create a flow and story of the dashboard or presentation using effective visuals.
5. End with an insight
Show the outcome from all the facts or patterns gathered from the data.
Presenting the data insights as a story makes it more engaging and memorable. How you share your story determines the size of your audience.
The fundamental steps to expert data visualization
Data visualization makes it easy to recognize patterns and find exceptions while interpreting the data at a faster pace.
By exploring your data, you get a better sense of what story to tell your audience. It is then time to discover which visualization best articulates your information.
Begin with these steps to create an effective visual:
- Understand the data
- What are the best techniques to display the answers? Do you need a chart (overview), a table (details), or maybe both to convey your message?
- Is it possible to highlight/distinguish specific data points to get the message across more effectively?
- How can you incorporate a summary of your message in your chart title to emphasize on your overall message?
There are mainly two types of data that we use:
- Quantitative – data that is numeric and can be measured and quantified. For example: unit price, number of units sold, profit, etc.
- Qualitative – data that is mainly categorical and non-numeric, in a logical order or not. For example: product name, product category, geographical location, etc.
Establishing the differentiation between the two data types will decipher which data group should be used in this context.
To be able to select the right visualizations, you need to understand data relationships. The main data relationships are as follows:
- Ranking: A visualization that relates two or more values with respect to a relative magnitude. For example, a company’s most sold products.
- Deviation: Examines how each data point relates to the others and, particularly, to what point its value differs from the average. For example, the line of deviation for products sold during sale/discount days versus the usual days.
- Nominal comparisons: Visualizations that compare quantitative values from different subcategories. For example, cycles rented in various locations.
- Correlation: Data with two or more variables that can demonstrate a positive or negative correlation with one another. For example, salaries based on the level of education.
- Partial and total relationships: Show a subset of data as compared with a larger total. For example, the percentage of employees that work in a department.
- Series over time: Here we can trace the changes in the values of a constant metric over the course of time. For example, monthly sales of a product over the course of two years.
- Distribution: Visualization that shows the distribution of data spatially, often around a central value. For example, the heights of players on a basketball team.
Here are some suitable visualization to use while depicting such relationships:
Highlighting or distinguishing specific data points
In this chart, notice that the color coding and the legend helps to almost instantly recognize how the KPI is performing versus the target.
Titling your visualizations
Adding the business question to the visualization helps the viewers to immediately understand the purpose of the visual and do their analysis accordingly. It eliminates any confusion about how the represented visual should be viewed and analyzed.
These are some suitable visualization to use while depicting such relationships.
When designing a data visualization, you first must clearly understand your target audience. Data visualization doesn’t follow a single fixed approach every time. Knowing your audience is the first step to ensure effective data representation.
Design your visualizations for users across multiple skill-levels. Make findings easy to understand for the ones who are not very familiar with the data. Here are some typical audiences that you will need to keep in mind:
1. The Newcomer
They do not have extensive experience with analytics. Focuses on the final goal – the solution to the challenge. Expects the data experts to lead the way to answer the business challenges using data, but lacks a deeper experience in the ways of handling and manipulating the data.
2. The Enthusiast
They have the general knowledge of the project and the available data but not enough experience with data analytics for in-depth analysis. They eagerly seek guidance, results, and reference points to understand some of the insights.
3. The Scientist
They are all about the numbers, the process, and the results. They may put less emphasis on the human aspect of the project, and concentrate on the process itself, the findings, and the potential it may offer for other projects.
4. The Master
This is the ideal audience type. They possess both interest and technical knowledge to be able to see right through the big picture. They are a supporter of your topic and are a master of the information.
Putting some thought into who will be seeing the visual to get insights will help you improve your visualization. Building the right kind of visuals for the audience will help to promote wider adoption of your visual report or dashboard.
To make it easier for you, we have written a white paper on data visualization and storytelling, which you can access by completing the form below. The white paper is divided into 8 sections and includes everything from rules and guidelines to do’s and don’ts and some pro tips. All yours, for free!
