Before the advent of the internet, instant connectivity, and big data, governments and organisations dedicated to fighting poverty didn’t have access to real-time analytics platforms to decipher their data in a meaningful way.
Today, analytics allows us to know that there are over a million people who are unemployed in Australia, with the unemployment rate surging to 7.4%; the highest since the recession of the 1990s.
Given the treasure troves of data available to us, we know what people like to eat, how they spend their time, which communities they belong to, what they watch and their opinions—statistics and data, which, at first glance, would seem completely unrelated to unemployment.
Alongside the new data science modelling techniques that are available, however, we can use even seemingly irrelevant data like this to identify and categorise similar groups of people, behavioural patterns and preferences to understand unemployment with a broader scope.
Because of the internet and nearly all people having access to it in some form or the other, it is now possible to break down human behaviour into data sets and make correlations between our preferences and behaviour.
For example, we could say that in the US, certain urban communities grow up in areas where violence is commonplace, where peers influence teenagers and young adults into crime. Once someone has a criminal record, finding gainful employment is virtually impossible.
Statistics show, however, that though there is a relationship between crime and unemployment, it is insignificant when it comes to crimes like burglary, larceny and robbery. It can also be inferred that the link between unemployment and crime becomes much weaker during recessions.
Analytics make it possible to make more sense of this data and may illuminate potential strategies governments can execute to provide vulnerable youth with gainful employment.
Even without data analytics, most people today could make an educated guess that for the most part, resources are distributed unequally between cities and rural areas—the differences in schools, universities and jobs are quite obvious.
This opportunity gap between people who are geographically, socially and economically displaced is more jarring than ever.
Analytics can help us understand to what extent and further distinguish aspects of our social framework that lead children and young adults to grow up into unemployment. It can, for instance, help us fix certain issues in the school system, rectify resource distribution issues and find ways to keep children interested in education, or if necessary, pursue specialised vocational training instead.
Data analytics is a great tool, therefore, for governments and authorities to understand and help certain communities develop in a meaningful way, not with just the goal of getting a job, but building a career.
When we consider unemployment, it’s important to have relevant real-time analytics about the number of people that enter the job market each year, their level of education, skills and the rate of new job creation, to name just a few of the most relevant factors.
Without big data and analytics, authorities can’t keep track of these statistics and find a lasting and viable solution to a problem as complex as this.
Analytics can also help gauge the success or failure of any solution that is implemented. It can help us determine whether it is a long-term or a short-term solution and whether it can be replicated over different demographics and if so, with what modifications.
Given the rich range of insights analytics presents us with today, understanding a complex and multi-faceted problem like unemployment and breaking it down to its bare bones is much easier today than at any time in the past.
If leveraged effectively, data analytics and new data modelling techniques will give governments and NGOs the tools they need to provide a lasting solution to the unemployment question.