Analysis

Systematic organization, interpretation and statistical presentation of the research data utilizes methodologies which are applied to address various social risks and issues.

Both quantitative and qualitative data analysis is performed to assist with research processing. This includes transcription analysis, coding and text interpretation, recursive abstraction, content analysis, discourse analysis, and grounded theory methodology. All data is organized, and processed in a variety of different ways, using proprietary tools. Some data types need to be normalized, the significance of data categorized, and voluminous data condensed into usable form.

Statistical Analysis

Data organization, interpretation and presentation of the research data, using applied methodologies to address various social issues & risks.

Data Insight

The team will look out for patterns and themes which often tell stakeholders something meaningful about research issues, a potential solution, or both.

Data Mining

Potentially useful information is extracted using methods to organize, or combine datasets. These may include machine learning, visualization methods and statistical analysis.

Deep Learning

Deep structured learning or differential programming is part of a broader family of machine learning based on artificial neural networks with machine learning.

Analysis Overview

• Establish Significance –  Data is analyzed to determine what is key to the project and the significance of the data which is categorized
• Normalize as Required – data quality control is performed, and some data types need to be normalized
• Condense – part of analysis is to locate and archive exterraneous data
• Data Transformation – conversion methodologies are applied to address data shortcomings
• Comparisons –  this includes transcription analysis, coding and text interpretation
• Charting – analysis uses charts to clarify the expression of data roll-ups

All data is organized, and processed in a variety of different ways, using proprietary tools.