We help you understand and utilise the power of your data and intelligently handle it
Robust analytics can help assess the efficiency and efficacy of social welfare schemes, thereby providing relevant insights to enable empirical policy decisions. Analytics can also assist in eliminating ghost beneficiaries, detecting identity fraud, correlating cause and effect, and sharpening policy interventions.
We present opportunities for you to advance work in analysing data for public welfare. We ease data collection, digitization, and curation, particularly around urgent priorities. We collate and integrate different data sources, even the ones that are not being used together. We create models and predictions of trends and behaviors to support existing interventions. This may help in better evaluation of existing and historical policies to understand their implications vis-a-vis enablement of newer advances.
Functions under Data Analytics
Aggregation
We make technology assist you in your data collection
We collect data from a variety of sources, as per the requirements of your organisation. After collecting, we measure the data on targeted variables through a thoroughly established system to evaluate outcomes by addressing relevant questions. Some of the different data collecting sources are collecting new data from the sources or by secondary means; using the previously collected and stored data; reusing someone else’s data.


Processing
We help you see and analyse the data patterns
The collected data is often in the bulky form, which needs to be summarized before getting conclusions from them. It may be in the form of numerical data, transcripts of interviews or descriptions. Data initially obtained must be processed or organised for analysis. Once processed and organised, the data may be incomplete, contain duplicates, or contain errors. We prevent and correct these errors from the data to avoid the problems arising from that data.
Presentation
We sift through the vast data and find meanings
We apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. The process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics, such as the average or median, may be generated to help understand the data. Finally, we examine the data in graphical format, to obtain additional insights.
