Monthly Archives

September 2018

The Language of Infographics

By | Graphics, Infographics

A highly skilled data analyst once said to me:

“I don’t get infographics, I don’t see the point, why are we using them?”

I can understand why she felt like that, she knew that data inside out, could identify unusual trends, and was so fluent in the norm she was able to recognise outliers and exceptions. Her audience were also familiar with the information, they had been reporting and reviewing it for years. For her, nothing else was needed. I however, had been ask to contemplate how we would deliver the story to a wider audience, one not familiar with the subject.

Trying to explain, I can refer back to a situation that happened to me a few months ago. Stood in front of a ticket machine in Frankfurt train station, on my way to get my flight home, I had the machine set to English as my German is not strong, but I realised that this didn’t change the station names. In a rush and a bit of a panic I tried to remember what the German for airport was so that I could type it into the search. Snatching a look at the departures board I see my salvation, not the name but that little plane next to it, announcing clearly the name of my sought after destination.

Not being fluent in the language, I needed the graphic.

Just like it’s difficult to teach someone how to do a job you do every day, understanding how to explain a data set you are fluent in, to someone who isn’t, can be challenging.

You need to step outside of your knowledge to breakdown the story for a different audience, sometimes an unknown audience. You have to see it from their perspective.

Just like Denzel Washington in the film Philadelphia saying,

“Explain this to me like I’m a two year old ok, ‘cause there’s an element to this thing that I just can’t get through my thick head”

His character (a lawyer) knows that his client and his client’s bosses (also lawyers) need to give evidence in a way that a jury will understand, cutting out the “lawyerspeak”. He makes them think about how they communicate the information.

Interestingly, speaking to a lawyer the other day, I found out that one of the most useful tools during trials are infographics (trial or litigation graphics). They are used not just to present the information but to tell the story, engaging people and making them, not just see the data but care about it.

This is why we use the language of infographics.

Cracking Data

By | Data Analysis, Infographics

This week I was watching a news report looking at how the Serious Fraud Office (SFO) were using Artificial Intelligence to analyse documents in complex cases. AI cuts down the huge amount of time it would normally take the SFO to analyse the vast amount of information collected for cases. What struck me was their demonstrations of the output. After the system had worked through the data (sometimes 10’s of millions of documents), the interface officers have to review the findings is an infographic. A stringed bubble comparison in fact, which is then used to identify interesting relationships and patterns in communication.

Obviously that in itself makes me happy, that the best way to analyse, make sense of, “see” the data is an infographic. That actually the work the AI is doing is essentially presenting the data in a different way so that we understand it better. Technology now means that infographics are no longer an end product, a presentation tool to show findings, but also the tool we use to actually analyse the data. We can manipulate the graphics to pull out key findings and use them to answer questions.

Going further, what was going to be a YAY INFORGRAPHICS! blog has developed into something even better…

It was obvious to me that the human input to the situation was essential. Initially telling the system what was interesting and needed to be highlighted and then to analyse the output, using the graphic to identify what was pertinent to the case. It was the officers knowledge and understanding of the subject which allowed them to make sense of all that information, however it was presented. You cannot get anyone or anything to answer a ‘so what’ question if they don’t have an understanding of the situation. Data Science is not just crunching numbers, its about knowing what evidence to layer up together, what extra information to have and trying to anticipate what questions will need answering.

I think collaboration is the key. Knowing who the specialists are, communicating and working with them to collect and paint as detailed pictures as possible.

Data is its own peculiar language and analysts, what ever their titles, are interpreters.