National Data and Analytics Platform

The National Data and Analytics Platform is also named NDAP. This is a useful platform and has different functions. It is a platform that deals with the accession of the latest data. The accession of the data can be given from multiple government sites. The development of products is also an important part of this platform. In this study, the discussion will be done on the concept of this platform, its mission, key features, and challenges. 

What is meant by the National Data and Analytics Platform?

NDAP seems to be a one-stop platform and it is used for all published government data. In this platform, it is important to note that all the datasets can be accessed from different government websites. This platform is also important and it is a tool in which one can easily analyse and create visualisations that are based on the required data. It will take an approach that is user-centric for providing access to data. A user-friendly search engine in this field can help in giving power to the platform. A world-class user interface is very useful for backing up seamless navigation. The customisable analytics will help to provide data in a format namely machine-readable format.   

The mission of the National Data and Analytics Platform

NDAP is a platform that goes with a mission and various missions of it are described through various points. The main mission of this platform is to standardise data and it is across multiple government sources. This platform is able to provide flexible analytics and make it easily accessible. It can be accessed in formats that are helpful for research, policymaking, innovation, and public consumption. It can be seen that the vision of the NITI Aayog is released for NDAP at the present time. The main aim of the platform is to access democratised government data that is publicly available. The main mission of this platform can also be mentioned as it will host the latest dataset from different government websites. It also presents them coherently and also provides tools for visualisation and analytics.      

Key Features of the National Data and Analytics Platform

There are different characteristics or features of the NDAP. The characteristics are also the features of a modern data platform and they are-

• Democratisation of data

• Discovery of data

• The invention of cloud technology

• Use of modern tools like machine learning and artificial intelligence

It can also be said that the analysis of data also involves processing, cleaning, collecting, and analysing massive datasets for gaining useful insights. It also includes the velocity, variety, and volume of the data.  In this factor, it can be said that there are four kinds of data analytics of big data and they can be descriptive, predictive, diagnostic and prescriptive. The main features also include machine learning and the use of artificial intelligence.    

Challenges Faced by the National Data and Analytics Platform

NDAP is a platform that has to face many challenges in the implementation of data analytics. The very first challenge of this platform is to collect meaningful data for the purpose. This platform also includes the challenge of the selection of the right tool. The data that is collected and its quality are most important but sometimes the quality of data is not appropriate and it’s a big challenge for this platform. Data visualisation and data security are other challenges. The creation of data culture is an impactful challenge of this factor. The growth of data and lack of understanding of big data are considered to be the main challenge of this platform of NDAP.   

Conclusion 

The platform of NDAP is very useful and provides access to data. The study provides a search engine that is user friendly this also helps in the prediction of future outcomes of the business. The different features of this platform include the rise of cloud technology, the use of modern tools and many more. This process is also useful for the use of modern tools like artificial intelligence. The collection of data, analysing of a massive dataset, and processing are the activities of this process.