Data Visualization

Data visualization is intended to provide data consumers with a qualitative understanding of the information contents in an appealing graphical presentation.  Data Visualization involves understanding historical data like relationships, patterns and trends, and providing visual information to the decision maker.

For instance scientist may be able to interpret very high volumes of laboratory or simulation data but for common people it may be hard to understand them with just the letters and digits. Also, in the field of data mining which is very much a part of a data warehousing system, there maybe many abstract concepts mined from disparate or unrelated data that can only be understood with data visualization.

Data visualization may involve manipulation of graphical entities such shapes, images, text, points and lines) and attributes such as shape, size, color and position). With data visualization, understanding of information based on aggregated data may involve detection, measurement, and comparison, and is enhanced through interactive techniques and providing the information from multiple views and with multiple techniques. Information displayed for data visualization includes all sorts of data, processes, relations, or concepts.

In the field of business enterprises, data visualization is typically used in providing business intelligence. Interactive Digital dashboard which are also known as an executive dashboard, enterprise dashboard or BI dashboard, are used for displaying performance metrics and Key Performance Indicators (KPI). These software applications are a great help for business executives in monitoring the status of the business as well as all of its events and activities.

There are many techniques for data visualization. Charts display data using either bar chart of pie chart. Graphs can be line graphs, bar graphs, dot graphs and may be good for visualizing data structure and relationships. Maps could be said to be one of the most effective ways to do data visualization in many respects.

Used with a software application like Geographic Information Systems (GIS), maps can visualize data in an interactive manner. Other techniques for data visualization include Plots (1- to n-dimensional), D surfaces and solids, isosurfaces/slices, translucency, stereopsis and animation.

Whatever data visualization technique is being used, there is only one basic process in data visualization which is the mapping of data to graphics. The process involves many stages but the general stages are as follows: examining of data for cardinality of dimension with detectible variations in graphics, using scaling and offset to fit in range, using derived values (residuals, logs) to emphasize changes, using derived values (residuals, logs) to emphasize changes, using projections, other combinations, to compress information, get statistics, using random jiggling to separate overlaps, using multiple views to handle hidden relations, high dimensions and using Use effective grids, keys and labels to aid understanding.

Many modern software applications are available in the market. These applications offer interactive features with data presentation. Data visualization through mapping offers dynamic adjustments of maps, data tour with varying views, data labeling, dynamic deletion or elimination of clusters, highlighting to see correspondence in multiple views, zooming in and out and panning for panoramic views.

As for all other technique for data visualization, the gauge for knowing a good product are:

  • effectiveness (if the view easy interprets and understand the data),
  • accuracy (if the data is correctly represented and value is not changed),
  • efficiency (when there is minimal data-ink ratio and chart-junk, show data, maximized data-ink ratio, brase non-data-ink, brase redundant data-ink),
  • aesthetic quality (when the colors and presentation are pleasing to the eyes) and
  • adaptability (the software can server multiple needs and purposes).

Editorial Team at Geekinterview is a team of HR and Career Advice members led by Chandra Vennapoosa.

Editorial Team – who has written posts on Online Learning.

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