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Datavisualisation: 5 pitfalls to avoid

Collecting and storing data has become quite commonplace for companies, but are they really making the most of this data, which adds a little more to the budget every year?

Datavisualization is one way of harnessing data for business purposes, and when properly exploited, data can be a real ally for the business. This is where dataviz comes into play, improving the readability and accessibility of data. It can be integrated into corporate strategy, saving teams a considerable amount of time... if and only if the most common pitfalls are avoided.

1

Going solo

It's essential to surround yourself with the right people. It would be a shame to confine datavisualization work to a single business area. Opening up the project to other departments that could benefit from a datavisualization tool to help them manage their activity makes the implementation of the tool profitable, while adding value to the various departments. Sales administrators, content managers and sales representatives could all benefit from clear, specific data. It's not enough to simply consider the different professions; they must also be actively involved in the implementation, so that the tool is as close as possible to their needs.

In addition to end-users, it may also be useful to identify teams who are already familiar with data issues, which will enable them to be included in the project at an early stage, and move forward more quickly and serenely with the implementation.

2

No defined objectives

Datavisualization is a powerful tool, provided it addresses real business issues. Defining the objectives of each user typology is the cornerstone of dataviz success.

Let's take a concrete example and imagine an advertising agency. You might think that the main objective is to visualize sales. Admittedly, this data is fundamental, but perhaps it would be interesting for sales staff to have visibility of the percentage of advertising space sold and the selling price, all coupled with the sales targets assigned to them. Or it might be interesting to know how much each brand is investing in advertising. Each business or user profile needs to be able to highlight the KPIs that are really useful for its business and decision-making.

3

Take all the data

Selecting the data to be analyzed is eminently strategic. The easy way out, often dictated by the fear of missing something later, is to integrate all the data. If the data set is too large, it becomes difficult for end-users to find their way around. With an inordinate amount of data, tools can experience processing latencies that are a real brake on adoption and use, since one of the objectives of dataviz is precisely to save time. The other major risk is that key information may be drowned out by the flood of existing data. In the final analysis, taking into account the completeness of data can be counter-productive.

The choice of data and its analysis possibilities is decisive. The intrinsic aim of a dataviz application is to help understand the data. If this objective is not perfectly achieved, the application loses its interest. As a general rule, we quickly notice that only a few analysis axes are used. These are sometimes relatively "simple" indicators, such as temporality (year, month...), products, countries or cities, which enable the dataviz to be understood while meeting business challenges.

4

Rushing to choose the right tool

Once you've clearly established your dataviz requirements, the question of the right tool naturally arises. There are a plethora of tools available to help you with this task, but not all of them will meet your needs. When making your choice, it's important to take into account what already exists within your company. For example, if your database is managed with a Microsoft Azure tool, then it's worth considering Power BI, which facilitates interconnection. To make these decisions, we recommend you call on experts who know the tools on the market inside out, and can guide you to the technologies best suited to your needs, constraints and budget.

5

Building an endless dashboard

Speed and ease of reading are essential elements of a successful dashboard. At first glance, this may seem anecdotal, but it's far from it. The end-users of dashboards will each have different approaches and uses: managers and directors will need an aggregated view with global figures to help them make decisions, while others will need a very fine level of detail to manage their customer portfolio or the audience results of a program, for example. So everyone needs to have access to the data that concerns them, without having to make any effort. In this case, there's a very useful method called DAR (or Dashboard, Analysis, Reporting), which allows you to start from the most general to the most precise.

  • The Dashboard displays the most important information with the least possible interaction,
  • Analysis lets users explore their data interactively,
  • Reporting represents the finest granularity, with detailed data enabling users to identify a specific case.

Datavisualization is a real asset in the democratization of data. It adapts to users' needs in terms of information granularity, ease of understanding, data analysis and decision-making support. These 5 keys provide the foundations for a successful datavisualization project.

Editor: Pauline Guillet - Data Visualisation Consultant Micropole

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