Big Data and Economic Analysis

A website called TechTarget defines big data as ‘Big data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modelling, and other advanced analytics applications.

The definition does seem complex, scientific, and full of jargon but yes the words like Machine learning, AI, Modelling, Economics, and Data Analytics are associated with the term Big data.

Big data is literally ‘Big data, a large amount of data collected via software (apps) and hardware (sensors) and stored as a huge sample space for analysis. The best example of software-based Big data is the data of users collected from social media apps such as Instagram and Facebook. These apps are still relevant in today’s world because they have a huge amount of user data to analyze and constantly update their model. Big companies and governments are constantly using Big data to create products and frame policies. But let me tell you a little secret that these firms do not tell you, that is, all the user data they have includes literally everything from the photos you like, the person you mostly talk to, the time you spend on individual posts, your opinions, your private information such as date of birth and gender, they know everything. Companies use Artificial Intelligence and Machine learning to analyze big data sets and update their products. If we talk about hardware-based Big data collection then your fitness band is the best example. The data is sent to the brand which sells that data to other firms which use that data to create new products and services.

Uses of Big data analysis – From policy making to handling a pandemic and from creating profitable products to advertising, big data has numerous uses. It helps researchers to take a multidisciplinary approach to research and help them to provide better conclusions. It helps in getting insider information about firms and markets by providing useful statistics on the economic behaviour of firms, people, and consumers. It also helps develop fields of economics such as behavioural economics, transport economics, education economics, and health economics. Big data can be used in financial analysis as in the fundamental and technical analysis of stocks, forecasting, and predicting market movements. With Artificial Intelligence and Machine Learning, Big data analysis can be made simple, and data can be properly organized and segregated.

Big data is stored in Hadoop clusters, cloud service storage, and other big data storage platforms. Big data systems store data in raw forms, filter it, organize it, and then provide it to analysts as per the requirement. In most cases, it's pre-processed using data mining tools and data preparation software so it's ready for applications that are run regularly.

Positives of Big Data:

• Larger sample space for analysis – Data is gold for analysis. The biggest problem for economic analysis is sample space which traditionally cannot be the whole population but with the use of Big data, the coverage can be increased.

• Huge chunks of Data and a variety of data to analyze – Big data is not just one type of data like name, it’s multiple types and kinds of Data which could include preferences, hobbies, choices, addresses, goals, reviews, etc. Data can also be chunked together, and a macroscopic analysis can be done.

• Better at predicting Economic Phenomena

• With the use of machine learning and artificial intelligence more and more data can be analyzed which can go beyond traditional analysis.

Challenges ahead of Big Data:

• Privacy Concerns – A discussion around the world has been going on regarding how much data should be collected and can be collected. In most free and freemium models, data is collected without users’ permission and shared between corporates. Data has become the new oil when it comes to capitalism and economics. Big Data has the potential to create a new sub-field of economics dealing with the data flow. Facebook has faced many lawsuits regarding unethical data mining and data sharing but even today it’s collecting user data and it knows what you are doing, what will you be doing, and what will be done. Privacy concerns over big data need more discussions and rationalist and empiricist views.

• Cost – Big Data is expensive to store. The computational power needed for Big data Analysis is one of the largest which can be impacted due to rising chipset and semiconductor prices. Other costs include education costs, maintenance costs, and labour costs. Labour cost in the sense because Big data requires highly skilled individuals. The development of Artificial Intelligence and Machine learning algorithms is also expensive which is going to become even more expensive in the future.

• Access – With an oligopolistic business environment and collusive business access to Big Data is made difficult. Sharing of Data between such firms cannot be monitored or audited by external authorities and governments. Due to this research freedom can be limited and firms can have a selection bias towards what type of data should be generated. Plus, most of the data won’t be open source for the public and typical access is also difficult. Thus, firms will monopolize the data and may mislead the public with the unethical model of business.

• Storage – Day by day mobile technology is requiring more and more storage and computational power. Take Blockchain and cryptocurrency, for example, day by day is becoming complex and difficult to manage. With the chipset crisis and rising component prices, we can forecast rising storage prices and difficulty in establishing servers. Big Data analysis also brings out some environmental concerns which are related to its need for computational power and electricity.

Conclusion:

Big Data surely has many positives around it when it comes to economic analysis. Like Big Data has the potential to change economics and revolutionize behavioural analysis. But the challenges ahead of Big Data analysis are what we ought to look for. With capitalism and misleading politics, Big Data can prove to be disastrous, or else with philanthropic capitalism and welfare economics, it can be taken as a blessing for Humanity. Big Data can change the way of doing business and functioning, as a result, economic analysis will change the way of humanity yet again.

References:

Bridget Botelho, Big-Data, Tech Target, https://www.techtarget.com/searchdatamanagement/definition/big-data

Rishab Krishnamurthy, Big Data in Economics and Policy, https://www.analyticssteps.com/blogs/big-data-economics-and-policy.