June 2, 2014

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Intelligent Data Visualization

Looking inside the data and proposing the visualization model based on best practices and recommendations can change the way we visualize and understand the data.

Data Visualization is a technology for visualizing huge data into meaningful business visualizations, giving a deeper insight into what the data is or how to make best use of the data for different business purposes. Like:

  • Forecasting
  • Decision making
  • Summary / status
  • Uncover trends and manage processes
  • For gaining an up-to-date ‘360-degree view’ of critical business functions.

Presently there are many applications, providing rich data elements like charts and dashboards combined with animations which looks very appealing. Still these charts and dashboards need to be created manually by selecting the type of chart and querying the data required to plot on it.

This works fine in cases where a business user is well aware of the data and knows which chart will be suitable for viewing the data and there is an analyst who can understand, analyze the data and come up with the required data queries and chart for displaying the business information.

This makes visualization a challenge for the end business user without any analyst or technical skills. Many times even an analyst / technical user fails to identify the right data and chart to visualize the business data which is actually of use.

Intelligent data visualization is about looking inside the data and proposing the visualization model based on best practices and recommendations. It analyzes the business data in different views and domains and tries to identify the best fit model that can give maximum information as per region, business needs and other parameters.

How it works

Intelligent data visualization takes a snapshot of the data by reading random data sets; these sets represent some business information at any point of time, which is mapped with available data patterns to find a chart that can best represent the business information.

The data patterns are either predefined or the system builds them gradually by analyzing the information gathered from different sources. These sources may include user inputs, online patterns and self-analysis.

A global repository will always be available and updated with the data patterns of different users and reporting applications as per the geographies, domain types and other parameters.

The system can take decisions based on the data analysis and knowledge to auto generate different reports. It would present user with the options to select the most useful visualization for a particular business scenario or can point to the portion of the data which is important.

Technical Understanding

The diagram below gives a brief idea about the technical understanding of IDV; the system acts like a knowledge gathering and decision system by gathering knowledge of business data with the available patterns from various pattern sources. This knowledge can be combined with different applications like reporting for analysis and report generation.

Future applicability

The system has the potential to change the way business data is visualized. This can eliminate the need for Complex and lengthy data analysis process.  Useful business information can be visualized without the need of any analytical skills or big business software. Thus, bringing analysis and visualization in reach of small organizations and individuals.

References

[1] http://en.wikipedia.org/wiki/Data_extraction

[2] http://en.wikipedia.org/wiki/Data_mapping

[3] http://en.wikipedia.org/wiki/Private_class_data_pattern