You might have come to believe that data-oriented solutions are not only crucial to private entities but also to the public sector. However, this is far from the truth. Data-oriented solutions are just as important to the government sector as well. Imagine how big volumes of data are supposed to be handled and analyzed by our governments every single day. Now, imagine the assistance that data solutions can provide and the magnitude of ease it can add to the functioning of the government.

slider      Let's delve a little into some challenges a certain state government of India was facing. As each state has numerous districts, the administrative divisions often need to operate on and analyse the data district wise for statistical reports and otherwise. The data exchange under the state government has the data about the beneficiaries of the Ration Card scheme and various policies run by the government and NGOs. The ultimate end-users are the citizens of the state in this case. Apart from that, the district heads i.e, the commissioners and other involved administrators have the expected data usability. On daily basis, the administrative bodies need to prepare the reports and do data visualization via the tableau. To exemplify, let's assume the certain state referred here has an approximate population of 40. 9 million with an average population growth of 14.5%. Different reports such as the amount of ration distributed or the number of beneficiaries benefitted under the ration card scheme separately for each day or the number of families with average monthly income above 130 dollars in a specific area and so on are required all the time.

      Apart from the already mentioned concern, an additional major problem in this information age is the mere ethic of data privacy. The privacy of data of citizens can't be compromised under any circumstance. It makes it essential for governments to ensure the protection of information in data management. This translates to the fact that only the respective districts should be able to access the data of their governed districts. The detailed sensitive information on KYC of the citizens being collected on the application has made privacy issues even more critical lately. The framework to be implemented is needed to minimize the privacy risks and safeguard the data from exposure involved by having well regulated quick data protection capabilities.

      We, at Nubax, successfully set up quick analytical dashboards for each district's database to extract data and process queries effectively. The dashboard was implemented using effectual data practices like data feasibility and data viability. Back then, the application architecture could not handle multiple queries at the same time resulting in a dysfunction of the application in the middle of processing the query. Also, the exposure of data resting in data lakes to the other districts was altogether curbed with this methodical strategy of separating data district-wise making privacy breaches as minimal as possible. The improvised dashboard with the data integration layer as a middle block solved that along with other challenges the state government was facing.

      In the previous data exchange architecture framework, the data reports and queries were run directly on the data in the application architecture. In an attempt to solve this, our team added a data ingestion layer also referred to as a data warehouse wherein the data was segmented into smaller chunks i.e, data marts referred to as data buckets that could be assessed by the designated authorities, for instance, the respective district commissioners without any undue exposure of database. Now the real-time interactive queries are run on the data marts instead of entire data records in the application directly. Quicker seamless processing and protection of data security with unique authentication were the two major benefits of the same.

      With the above-mentioned issues already into the bargain, an added complication that cannot be overlooked was the scalability and ever-increasing quantum of data. The data in the ingestion layer consisting of the unstructured public data and streaming data was segmented into data marts. The segmentation of data from the primary warehouse into marts simplified the handling and storing of data at its disposal. The data lake could handle millions of data sorting the problem of data scalability or the application getting stuck on the processing due to the pressure of many queries at once solving the issue of ever incrementing pace of data growth.

      The data in the data lake is further catalogued via airflow readying it for efficient data processing. We would be elaborating on it another time for a better understanding.