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Marketers: Good data visualisation addresses two key questions

Marketers: Good data visualisation addresses 2 key questions

The past decade has seen an explosion of new data sources, often unstructured such as social data, and increasingly owned by other people e.g. weather data and open source government data. All companies are becoming much more aware of the value of data because of the rise of the “data analytics-driven companies” such as Amazon and Uber, and the impact of data is being felt (or is being missed) across many parts of the organisation.

In marketing alone, the need to track engagement and interactions across web and social and to relate the analytics to sales and ROI creates a tremendous amount of data which requires analysis by those with domain experience.

This is where end-users are clamouring to apply easy-to-use tools which provide easy and flexible reporting of the data, including visualisation.

Data visualisation moving into the hands of user is a good thing. The risk is that many such visualisations can be more buzz than insight and in the excitement of visualisation itself, the real power is sometimes lost.

By asking two key questions, and clarifying roles, marketing managers can set themselves on track to optimise their investment in data visualisation

What should data visualisation deliver?

Marketing managers are used to seeing data visualisations, perhaps even too many. So what is the secret sauce that a data analyst might bring to a marketing manager – just more visualisations? The simple answer is that a data analyst should bring visualisations that provoke more questions, as well as telling a more compelling story.

A good data analyst releases the excitement from your data. This means exploring the data for unexpected features which tell stories that were otherwise buried.

The key approach to visualisation is to ensure that it does not miss the point of the story – if the plot misses the point then the data analyst has “missed the plot”.

Fundamentally, visualisation combines statistics and design to make meaningful plots. This guides the path from the data exploratory phase to the explanatory phase and the explanatory visualisation.

A good visualisation addresses 2 key questions:

Question 1:

The first is to understand the capacity and appetite of the audience to be able to absorb the information. With visualisation, it is easy for an enthusiastic analyst to bombard their audience with all kinds of interesting facts and patterns, but if it isn’t stitched together into a succinct story it won’t have meaning for the audience – and the value will be lost.

So the key question is “does the visualisation tell the story we want told, the questions the audience wants answered, and will the audience perceive this story in the way we expect?

The potential success of the visualisation should be judged by its power to which increase the speed of perception of the story to be told.

Question 2:

Secondly, and something often overlooked, is the issue of the choice of visualisation. The wrong choice can obscure the data’s ability to stimulate insightful questions from the audience.

A set of visualisations may address the key topics specified in advance by the audience – and look very smooth and satisfying. However, if the underlying characteristics of the data are obscured then the audience may never be provoked to ask potentially important questions.

In other words the visualisations should not only seek to tell the primary story, they should present characteristics of the data that provoke insightful (and perhaps unexpected) questions by a knowledgeable audience.

This access to the underlying data, to explore questions from the audience, is facilitated by interactive visualisations. Interactive visualisations are becoming very powerful and increasingly accessible, but that’s another article.

By paying attention to the two key questions above when interacting with their data analysts marking managers will gain much better outcomes from their data visualisation investments.

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