DATA VISUALIZATION CHALLENGES AND OPPORTUNITIES
We use maps to orient ourselves, document and communicate more broadly. For example, the plan of subway lines creates a useful representation that is completely naturalized as we know how to relate it to our concrete area.
Today, thanks to a simple interaction, this representation of the metro makes it possible to examine the relations with population, incomes, unemployment by region.
However, this tool does not allow for any correlation, even though it offers simpler cross-reference of data than static tables. However, comparisons and correlations between data are the power of data.
Another important factor of interaction has to do with their real-time feeds. Other RATP mobile services thus make it possible to instantly visualize metro faults and incidents.
These indicators, infused into these representations, continually enrich a map that now and increasingly merges with regions. Data acts as a mediator between a reality and its representation, between the data and the information obtained in return.
Problem of Understanding
Information visualization or analytical visualization (visual analytics) transforms large amounts of complex input data into synthetic and simple information at the output.
Through scientific modeling work, there is a decisive, exploratory intermediate dimension to the discovery of knowledge, especially when it comes to complex data.
Interacting with data opens up new possibilities for manipulating, selecting, zooming in, zooming out, researching.
The data visualization then helps you gradually get to the heart of the case. It becomes a heuristic tool that allows you to access details that are not visible at first glance in the processed data pile. This mode of exploration shifts the methodological dividing lines from an explanatory logic to a more intuitive, comprehensive logic of phenomena.
The pharmaceutical field aims to improve the development of drugs through more specific molecule selection. Modeling data through visual interfaces to improve factors of populations at risk.
In the future, this exploratory data application makes data tangible with data tools. It will increasingly impact software solutions for enterprises and the general public, perhaps even by embodying it through forced feedback.
Decisions
Analytical sciences have always used descriptive statistics for decision making purposes. The innovation lies in their predictive value. For example, PredPol, a crime prediction system, cross-checks all files at the scale of a city like Atlanta or Los Angeles for prediction purposes to reduce crime.
For organizations, visual data processing is becoming an important tool in decision making. It makes it possible to expose unused data. It also makes it possible to build a cumulative knowledge base to predict actions.
From an economic perspective, business intelligence already makes it possible to provide decision-making information.
The latest generation of data visualization tools, directly integrated into software solutions such as SAS or Tableau public, are more comprehensively designed.
strength and weakness of data visualization
disadvantages of data visualization
what is the purpose of adding a challenge to a visualization
data visualization mistakes examples
when not to use data visualization
reasons why a visualization might not be needed
charts in data visualization
what is data visualization dashboard
For non-computer users with common cognitive and visual abilities, it helps decision makers and managers focus on the essentials.
Data visualization makes existing information accessible and improves control and governance more broadly.
As a result, visual design, functionalities, and visual communication issues are included in data visualizations. It is at its core, creating typical difficulties in terms of accessibility and sometimes interpretation difficulties.
Yaşam Ayavefe
