Data science, as Deepinder Dhingra recently blogged, “is essentially an intersection of math and technology skills.” Individuals with these skills have been labeled data scientists and organizations are competing to hire them. “But what organizations need,” Dhingra explained, “are individuals who, in addition to math and technology, can bring in
Data-driven journalism has driven some of my recent posts. I blogged about turning anecdote into data and how being data-driven means being question-driven. The latter noted the similarity between interviewing people and interviewing data. In this post I want to examine interviewing people about data, especially the data used by people to drive
There’s little doubt that basic, static pie charts and even infographics can tell a story. But, as I write in my new book, Visual Organizations understand that contemporary dataviz tools are just plain better. They allow for a high degree of interactivity, motion and animation. So, what does this mean?
At the Journalism Interactive 2014 conference, Derek Willis spoke about interviewing data, his advice for becoming a data-driven journalist. “The bulk of the skills involved in interviewing people and interviewing data are actually pretty similar,” Willis explained. “We want to get to know it a little bit. We want to figure
Data matters more than ever. Progressive organizations such as Netflix, the University of Texas System and others are using contemporary data visualization tools to find the signal in the noise that is big data. Dataphobes won't be able to hide for much longer. These facts were very much on my mind as
My previous post made the point that it’s not a matter of whether it is good for you to use samples, but how good the sample you are using is. The comments on that post raised two different, and valid, perspectives about sampling. These viewpoints reflected two different use cases for data,
We live in an era in which it's not terribly difficult for companies to ape many of their competitors' products and services, especially digital ones. For relatively small amounts of money (compared to years past), an organization can more or less mimic another's raison d'être and even specific functionality. As for design,
In my previous post, I discussed sampling error (i.e., when a randomly chosen sample doesn’t reflect the underlying population, aka margin of error) and sampling bias (i.e., when the sample isn’t randomly chosen at all), both of which big data advocates often claim can, and should, be overcome by using all the data. In this
We have entered the era of big data, but many questions remain unanswered. For instance, who owns all of this information, anyway? If you take a photo and post it on Facebook or Twitter, does it still belong to you? If you create a presentation with Google Docs, does Google
In his recent Financial Times article, Tim Harford explained the big data that interests many companies is what we might call found data – the digital exhaust from our web searches, our status updates on social networks, our credit card purchases and our mobile devices pinging the nearest cellular or WiFi network.
