Going through the assigned courses on the learning platform of the Lagos Business School (LBS), Data Analytics (DA) was one of the courses that tugged at my ignorance and I wondered about the level of statistics we might have to delve into. To my dismay, with only marginal prior knowledge, we were assigned our first individual task in this same course – various applications of probability in different areas of business.
After the initial shock, the assignment presented a good opportunity to develop a deep appreciation of the subject matter, and it emanated from the simple fact that “data in its natural form does not make much sense until it is analyzed”. That is, exploring and drawing insights from data with statistical techniques and methodologies are necessary to make informed decisions in all various fields of specialization.
It became glaring that when theories in DA are applied to data, it uncovers behavioral and economic factors, which provides a more detailed and nuanced understanding of the scenario. Data Analytics is one of the greatest tools in understanding, analyzing, modeling and predicting human behavior and making informed strategic decisions.
My economics background couldn’t help but seek the effectiveness and reliability of data analytics in explaining an interesting economic phenomenon – the Paradox of Thrift. The Paradox of Thrift specifies the importance of recognizing the interplay between individual and collective decisions, highlighting the complexity of Economics and the need to take into consideration broader consequences of individual actions in a societal context.
Specifically, the Paradox of Thrift- popularized by an influential economist of the 20th century, John Maynard Keynes, explains that especially in the context of saving and spending, individuals save more during a recession, however this is counterintuitive for the economy as a whole, as increased savings reduces aggregate demand and might lead to businesses running out of production and eventually reduced income for households. The Paradox here is that the rational and prudent decision of the individual to save may lead to undesirable economic consequences. Some of the consequences of human behavior that could further be analyzed with the help of data analytics include:
- Irrational consumer spending
- Bank runs: withdrawing deposits and holding unto cash
- Cash Hoarding
- Global Saving Glut
- Reduced Business Investments
Data Analytics is a valuable tool that provides insights and evidence about the economic impacts of this phenomenon by playing a crucial role in using analytical tools to understand, explain, quantify and assess the reactions and implications of the decisions by the various stakeholders and proffering recommendations towards achieving informed decision making on a macro –economic scale. Some of the techniques utilized amongst others include:
- Monitoring Consumer Behavior: tracking changes in consumer spending patterns
- A/B Testing and Experiments
- Simulations and Models: To forecast the impact of the decisions on different sectors of the economy
- Predictive Modelling: forecast human behavior
- Machine Learning in Decision Making: using algorithms to process vast amount of data and profer recommendations.
The highlight from all these for me is not to underestimate the power of self-thought learning, it pushes you further than you could imagine.
#MEMBA12
