This time, I would like to walk you through questions and answers, but first, let’s read this quote below as our reference point.
“Public organizations tend to have goals that are difficult to quantify, meaning that it is difficult to measure outcomes. The purpose of a public organization is to provide something in a ‘good way,’ in a ‘proper way,’ or in an ‘efficient way.’ but what is meant by these objectives? There is simply no uniform currency available that may be used to evaluate the objectives. Whereas, there is such a goal (profit) and such a measure (money) in private organizations, there is nothing similar in public organizations.’ quoted by Jan – Erik Lane, in Kooiman and Eliassen’s Managing Public Organizations
Here is my first question: Do you think the quote above still holds?
Partially yes, whereas a lot of exciting things are happening in this field. If you had enrolled any microeconomics class, you would probably know perfect / imperfect information. Here is the news: technology is aggressively shifting private sector from imperfect towards perfect information society, which is the bottomline of all the things I mentioned below. This transformation in private sector affects the expectations of citizens regarding the transparency of public sector. If you are not convinced, just do a quick research about how governments are panicked about shifting to e-government.
Second one: How can quantifiable measures help public sector and therefore the society?
First things first, data means politics-free, fact driven decision environment, maximized effectiveness and efficiency of entities and so on. On the other hand, as analytics enable finding relationships (or correlations) between multiple factors and the coefficient of those relationships, society can surely anticipate that data science will resolve all the cause&consequence – related problems of mankind. These arguments may sound like the key aspects of an ideal state, so you may think that it’s still an utopia from 21st century perspective, whereas latest trends show us how technology becomes fruitfully applicable in the public services.
We can split main aspects of public services into two(1):
1) Social Protection:
All technological improvements and enhancements as well as new inventions (e.g. seek for shelter, invention of weapons & fire at the stone age) are firstly and inevitably implemented on security. In that respect, one of the well known examples for the big data usage is the crime prevention (so called “predictive crime fighting” by IBM). Methodology is not rocket science: Define the crime aspects, capture the data, conduct the descriptive analysis which is then followed by a predictive one, and pin all the criminal activities into the map – both current and predicted ones. As a final step, allocate your resources accordingly which ultimately saves budget, protects society and increases welfare. Another similar example is the Richmond Police Department in Virginia: Their analysis revealed certain cool things such as the likelihood of car theft according to the seasonal differences. For instance: warmer months as citizens leave their cars running in an effort to keep them cool, car theft increases. This kind of predictions probably have positive impact on their patrol personnel workloads, such as the deployment of those patrol units.
2) Economic Vitality:
From the economics point of view, tax payment is still the main economic priority of 21st century governments as being the main income driver (excluding some of the countries like UAE). This one is more obvious because it’s easy to find similar examples from private sector. The idea is that tax collection and fraud related issues can be handled much more effectively by using certain data mining techniques.
Segmentation and personalization of citizens are the key elements behind these two aspects above. When you segment citizens, you are likely to serve them with better policies that match with their needs. As a quick example, you may not need to pitch same ideas in all of your election campaigns in your entire geographical focus, you can definitely do it smartly buy collecting some information. Even from the legal perspective, instead of pissing off a major part of the society to pass one essential law, you can customize the law for the sake of needs and behaviors of certain groups of citizens.
If you need to remember one thing and one thing only from this long article, here is the summary message: Massive treatment is out, identification of citizens is in (in all aspects of the public sector) So government officials, brace yourselves!
My third question: What are the main bottlenecks regarding the implementation? Why don’t we see cool stuff all around?
This will be the topic of my next article, and I will include my fresh professional experiences in that article. But here is a short answer that I can give you immediately:
Public sector requires people-centric approach mostly, whereas my approach in this article was from a business-centric perspective. In my opinion the main challenge of the implementation is the fact that data science has not reached the “beyond” point, the point where we have accurate understanding and prediction of the “utility”. Although human behavior is predictable to a certain extent, it still a question mark to find a strict measurement for “happiness”, “joy” and so on. To simplify, we are very complex creatures, so as our needs. And our behaviors have not been strictly linked to our needs or vica versa, yet…
The Power of Analytics for the Public Sector, IBM Global Business Services – Executive Report