Data visualization: 3 Mistakes to Avoid

Data visualization 3 Mistakes to Avoid

Data visualization 3 Mistakes to Avoid

Various studies show that the brain processes images sixty thousand times faster than the text. Therefore, and for many other reasons, the data display is e l final step in the work of big data analysis. The timing of representation of visual knowledge gained from his analysis implies, with regard to working with large volumes of data:

  • Support to knowledge.
  • Accelerate the understanding of information.
  • Promote creative exploration data.

Facing the organization and its processes:

  • It helps to provide a single view.
  • Help share information.
  • It is instrumental in improving the quality of the reporting step.

Encourages increased efficiency in user interaction with the data.

With regard to relations with the outside of the enterprise, data visualization is also a source of benefits as:

  • It allows you to answer any questions more quickly.
  • It facilitates a better understanding of the customer and improves relations with him.
  • It helps maximize the ROI of any big data strategy.
  • Improves the competitiveness of the company in the market.

Data visualization: the failures that you should avoid

Clarify objectives and get better results should be the goals of data visualization . Anything not to fall within this perspective of data visualization certainly not facilitate the task of deepening the information, so that distances the use of good business opportunities.

The data display is an extraordinary way to make and share a vision, but many teams of big data analysis is carried out in the wrong way. Some of the most common errors are:

A / Show all data

Customers and internal users want precise, specific answers and want to get as soon as possible. They do not mind the amount of data that can be processed daily or storage volume that have at their disposal. Their priority is to minimize the effort to find solutions when looking at the information and display all available data does not facilitate this task. With respect to display data:

Irrelevant data make finding critical information becomes more difficult, so you have to avoid exposing.

It is especially important to choose what you want to show and what not, tools like dashboards, where they observed a tendency to try to condense all available information without prior selection.

A successful approach is to show only what is interesting or important. Prioritize what is truly relevant, the unexpected and what is actionable. We must dare to downplay everything else.

Generalities can be included in some reports if deemed appropriate, but should not be part of what is collected in a scorecard.

B / not meet the objectives

To make a good interpretation of the information necessary to specify the terms of the data visualization. Show subsets of information can be very useful, as long as the relationship between the data presented are relevant. To succeed we must consider how the data for decision-making is used.

Some good practices of data visualization in this regard are:

Graphics display several closely related and complementary information which, in this way the result is enriched.

Striking the balance between the amount of information displayed on each graph and in particular on the set of all.

Opt for clean and clear graphics, which are always more effective than a visualization of complex data.

C / misuse of available resources.

The data visualization offers endless possibilities to the user, which can be lost between different chart types, colors and available funds, to apply formats … go with the momentum not drive better results, but away from the goals. We must have clear goals and get the display is the perfect place to reach optimally vehicle.

Even when it comes to the correct data, excessive and lacking in coherence graphic representation may not meet the viewing needs. Therefore, it is preferable to consider that:

Proper selection of bar charts and line graphs, scatter charts or pie charts show increases the chances of key relationships between data fields.

The preparatory work for the establishment of categories or data distribution in terms of temporary records, magnitude or significance improved interpretation.

The colors and effects can be used to draw attention selectively and respecting priorities. Glitter, labels or other forms of content should never highlight subtracting attention from the main issue to be shown in the display of data.

Good design is always the product of thinking what you want to achieve and to make a consequential advance planning. To avoid mistakes is highly recommended to focus on goals and not having qualms about trying different approaches, try a little, change whatever is necessary, improve what can be done better and repeat again. A well-posed user-oriented design approach ensures data visualizations effective, efficient and useful.

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