Economic Survey Of Unorganised Sector
The technology platform required for this study shall utilize a number of apparatuses that talk to each other through a common database and the use of APIs wherever required. The apparatuses that shall be utilized are as follows
A mobile application that surveyors can use to collect data and push it for analytics in real time, thus allowing better audit and higher quality of data collection.
The app also has an optional section to allow secondary research data to help fill the primary data.
React Native is used for building the mobile application that works on Android.
SAMPLING AND EXTRAPOLATION TOOLS
Combination of mathematical models that are used to extrapolate data in order to get collective information across various parameters, particularly for the purpose of knowing overall awareness and gaps.
These tools are built using Python.
STATISTICAL DATA ANALYTICS TOOLS
Tools that uses various hybrid econometric and statistical models to find critical variables that impact the related variables and help us identify key problem areas with respect to the publicity of the schemes, as well as key drivers that lead to low awareness of schemes.
All analytics tools are built on Python.
DATA NORMALIZATION ALGORITHMS
Data cleaning and normalization shall be needed to improve the quality of the collected data and such normalization is a function of various secondary and primary variables, thus being able to weed out false data and correct certain data.
Most of these algorithms are already in use for various surveys and are built on Python. Some of these algorithms are also available in Macro on Excel.
Various outputs can be combined to create various measurement indices or scores that can be used to understand the RoIe of schemes as well as the level of quality of benefits across demographics and regions. Such tools need to be developed specifically for the purpose of this study and can be built upon previous tools that have been used to measure scores such as financial status, social status, and socio-economic satisfaction status.
All of them are built in Python or R or Excel as the need be.
Recommendations for course corrections for various schemes can be provided through a recommendation engine that follows the following steps
DATA ANALYTICS DASHBOARD
A dashboard that provides filters of various demographics, regions, and urban/rural divisions and can display processed and analyzed reports across the entire state, using intelligent interfaces of graphs and diagrams, along with Heat Maps.
A dashboard that provides filters of various demographics, regions, and urban/rural divisions and can display processed and analyzed
reports across the entire state, using intelligent interfaces of graphs and diagrams, along with Heat Maps.
All licenses of the above-mentioned tools, including the sub-tools such as the GIS Map of the entire state belongs to Sapio Analytics, our partner in the project.
Moreover, the data architecture used by us is designed in a way that it can scale up to a much larger dataset in future keeping in mind the possibilities of going beyond sample data set for seeing the impact of this study later.