10 powerful data storytelling tricks

10 powerful data storytelling tricks

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10 powerful data storytelling tricks

Want to know the little secrets of the best data storytellers? Just apply the 10 Data Storytelling tricks that I will present in this article. Apply these methods, and you’ll know how to adapt your communication around data, and especially how to help your audience understand your data analysis simply and effectively.

Let’s go.

Data storytelling, or the art of relating a story around data. Data Story… what?

Data Storytelling is the art of communicating efficiently around data and giving the proper arguments in order to help in taking  decisions.

If you want to communicate around your data, keep the interest of your audience in your analysis, expand the use of data, mastering Data Storytelling techniques is a must and will make all the difference.

In a nutshell: Data Storytelling is the art of sharing data analysis to your audience.

These data vizualization practices are used in all types of use cases:

  • financial reporting;
  • Activity reporting;
  • activity management;

Why you should ABSOLUTELY apply these data storytelling tricks?

Are you familiar with the great moments of solitude in front of a dashboard ?

Don’t let your audience remain confused in front of a dashboard full of charts, grids, kPIs that only a specialist can interpret.

You’ll lose them, they won’t benefit from the information you give.

To be brief, it’s a disaster…

Either you decide to continue to push tasteless and pointless analyses, or you decide to make the most of your analyses to fully engage your audiences.

The checklist of data storytelling tricks for 5-stars data sharing

1/ Know your audience

To satisfy your users you need to know them. 

Am I talking to an internal or external audience ? Are they managers or operational staff, customers or suppliers, partners?

What is their level of knowledge of the matter?

Do they want to have consolidated elements or do they want to have details ?

All these questions are essential to define the direction of the analyses that we will share.

2/ Over-personalization

We must not be afraid to adapt each analysis interface to its targets. We could use the analogy of the performance of emailing, which works on the personalization of its messages to increase engagement. It is quite similar here, the more the interface fits to the expectations of the target users, the more captive it will be. In our case, we can monitor the adoption rate.

The customization will concern different aspects such as

  • the scope of the data 
  • the depth of analysis: i.e. the level of data aggregation
  • the structure of the analyses
  • The layout of the presentation

3/ Simplify the reading process

The target here is to limit the number of manipulations as much as possible. Too often it is necessary to learn the interface before being able to access the analyses. Where is my data ? Where can I find a particular analysis? 

For this purpose, formats such as Infographics or Single Page are good practices. On a single page you gather all the analyses you want to share. By this way, users only have to scroll down the analysis page, which avoids losing them in the multi-tab and multi-page navigation.

4/ Organise data analysis by theme

Often, in the dashboards, the analyses are placed side by side even though they address different domains. It is necessary to understand the axis of analysis of each graph, which requires a perfect understanding of the subject.

The ideal solution is to group the analyses by subject, by theme, in order to offer a homogeneous and coherent framework for reading and understanding. Here we take up the principle of the book which is organized by chapter. Each chapter deals with a specific pat of the story and builds a structure to read the story. This is a good practice that we will also apply to the Data Storytelling within the Single Page.

5/ Comment the analyses

Let’s not leave the users alone in front of analysis and hope that they all have the same reading and the same expected understanding. This never happens…

So what better way to explain an analysis than with a comment!

The comment areas can be used to summarize or synthesize the analyses or to highlight the key elements of an analysis to facilitate understanding.

The value behind this is that it helps to align all users with a common understanding.

3,2,1 … action.

6/ Contextualizing your analyses

What is ” contextualization “? It is the way in which the analyses given are linked to the business or to the issue concerned. The analyses are carried out to address a business issue, so it is important to bring these two aspects together in order to make the analyses more easily intelligible.

The idea is to integrate external business elements such as images, videos or business documents in pdf format alongside the data analyses. These elements therefore contextualise the analyses and make them easier to understand.

Let’s take the example of a marketing department that wants to analyse and share the performance indicators of a TV advertising campaign. It would be relevant to also include the video of the advert broadcast, as well as the communication strategy document in .pdf format which underpins this advertising campaign. In this way, all the elements are put together to contextualize the shared analyses.

7/ Putting the audience in the context

Users must feel a deep concern for the personalization or even the personification of the analysis interface. It must be made to feel as if it is totally dedicated to them.

It is recommended that the interface be redesigned to reflect the organisation’s image, by integrating the logo, images, etc. The appearance of the analyses should also be refined by associating the organisation’s specific color palette.

Users must be immersed in a framework that they know.

8/ Giving voice

If the target of this approach is to facilitate the understanding of the data, it would be relevant to associate to the interface an area to exchange and discuss that would enable the users to bring their individual knowledge in the explanation of the data.

Perhaps some elements of a chart generate questions. “Why do we have this result on this criteria ? “Ideally, users should be able to get answers from each other.

9/ Quick access to analyses

Gartner is already predicting the end of the too complex dashboards, to be replaced by Data Storytelling interfaces that require less manipulation to access insights. This is also the key. Data can seem “rough” and we have to make it easier to access and to analyse. The interface must be simplified, and the manipulations and action buttons must be kept to a minimum.

If we have seen that the Single Page format is the most suitable, it has also to be natively Responsive to allow usage from any mobile device.

10/ And finally, let’s not forget the essential … the relevance of the charts

The simplest charts are often the most effective.

Of course, it is sometimes tempting to integrate a “dependency wheel” or a “Sankey diagram” to look “modern”, but let’s not loose the sight on the goal: the audience must be comfortable with the analyses and spend as little time as possible decoding them.

A chart must be useful, i.e. it must quickly and clearly show the message to be delivered.

You will find many good practices on this matter with our colleagues at Storytelling With Data.