Data Journalism : Do we trust the numbers ?

-
Data journalism can be rephrased as data-aided journalism. The ability to extract information from various sources is at the heart of the big data process called Data Mining. The current trend in journalism is to rely more on the data mining techniques to gather data that allows for meaningful exploitation of facts and insights that would be otherwise difficult to analyze manually. A lot of news stations have started investing in big data over the past few years but this has led to another problem.
         How can we trust the analysis performed by the new channels ? Not only does the technique of big data create a myth that data analysis serves as conclusive evidence, but also disengages the audience from conducting its own inquiry. I’m not implying here the curation of viewers minds, rather the false trust generated by statistical analysis in general. Statistical analysis which is at the heart of big data is only used to indicate there presence of trends in data. The problem arises when we claim certain properties hold in a system in general only based on trends. In some cases, this is a valid approach.
     Take the example of historical meteorological data. It’s not out of the world to assume that if it rained yesterday and that if the humidity is sufficiently high that it will rain again today. But this kind of predictive analysis cannot be used out of the box to predict the number of crimes that will happen and certainly not to decide whether a person living in a particular area is more likely to commit a crime than other. While there is some truth to it, this decision cannot be made without sufficient analysis of the situation and evidence.
Journalism is "the profession of the skeptic” and should stay that way. It is one of those few usages of big data where too many cooks spoil the broth.
I hope you liked reading about this topic. This writeup is entirely mine and is inspired from the following two web sources

Comments

Post a Comment

Popular posts from this blog

Why computers aren't amazingly smart

-
Artificial intelligence is the field of computer science with the goal of making computer more useful than they already are by making them receptive to their environment, giving them a novel way to store, process and expand their knowledge base and letting them make informed decisions based on the accumulated knowledge. As you can already see from the above definition that the field has a variety of sub-fields or places where one can contribute. Let's begin by talking about what motivates artificial intelligence. How can the task of making decisions in machines ( which is mostly inspired by how humans think and act ) be understood as a process ? The best way to draw a parallel is to keep in mind that what the machine is essentially trying to do is act like humans albeit without a perfect resemblance. The state of art thinking machines are not even as good as a teenager. In fact, most would not be able to do simple tasks like differentiating a girl from a boy as fast and as accur...
Read more

Making Machines : Introduction

-
Machines are now ubiquitous but once they were just thoughts in a crazy man's mind. Well actually, many men but for our understanding there were three people who set the ball rolling. 1. Alan Turing 2. Alonzo Church 3. Kurt Godel All these men were, in their own ways, trying to figure out a mathematical model for a machine. For all those readers who lost me here should think of a mathematical model as the way of describing anything around you with pen and paper. It's almost like taking notes but mathematics is the art of taking notes about your notes and doing all that till you make sense of what is possible by twisting what you know in ways you couldn't have directly imagined. Math is just a language and it's not necessary for all of us to think in that language. My blog here is another effort among many to explain what "computation" means and how is it different and similar to the word "computer" we use every day now. Think about what yo...
Read more

Data Mining and R/Rattle : First Experiment

-
Data mining is the activity of harnessing useful insights from vast amount of data based on a certain model. Instead of one particular model or way analysis, data mining is context-based and may potentially employ multiple models and can work on a variety of sources of data including text, audio, video, images etc. Data mining is an ongoing process and there are scenarios where the development of the data model stagnates are relatively few. This is because the mining insights act as feedback for a better model and hence, generally the process is much like agile software development. The role of a data miner in an organization begins with an understanding of the domain and data itself. This process is crucial to defining and refining the model that is the heart of the mining process. A model is constructed and critiqued by domain experts and data experts. This completes one cycle of data mining and the cycle continues in the same way every time using the insights generated from th...
Read more