Data journalism won’t be consequence free

Serendipity of a much-less-romantic variety than befell John Cusack descended on me last week: data journalism popped up everywhere.

In the class I teach, we schooled the students on Excel calculations and analyzing survey research. Then, Charles Lewis presented an intriguing talk about holding the government accountable to real-time truths by examining the data.

I compare data journalism to like a treasure hunt in a foreign language. The first step is finding the prize – the numbers that will shock, astound or confirm. The second step is translating – giving the context to why that number shocks, astounds or confirms.

When you can translate from a government-generated spreadsheet into a 40-inch story that leads to a change in society, Bruno Mars has a song to sing to you.

Some of my first data journalism came in the usual way: school district budgets.

But, things got more advanced – and more exciting – when I started down the path of a complicated municipal interest finance deal called a swaption. With the filings of a FOIA, I got some pretty crazy amounts of data. And, stories.

Having that data didn’t mean endless charts and graphs. But, it did let me hold to the fire the feet of some state lawmakers who under pressure from firms had changed the law to allow districts to sign-up for these interest deals.

That’s exactly the type action Lewis advocates. Pulling out the raw data gives stories legs to run, an anchor, a hard-to-argue-away sticking point. Sometimes a Pulitzer.

But, my fear is that a government badgered constantly by the press for data will stop making data – or at least believable data. While President Obama promised a transparent government, the types of information found at is hardly self-incriminating kind.

For example, even now we don’t know how many people are killed in this country are killed by law enforcement agents.

Why, if we constantly are calling for the data agencies know would put them in a bad light, wouldn’t that “We don’t collect that data” virus spread faster than the flu the day after the Super Bowl?

The federal government, after all, shines brightly at using data for political purposes. Something as basic and constitutionally mandated as the census, for example, has been so highly politicized that Congress argues about it every 10 years, i.e. each time we do it.

Everyone knows how to lie with statistics. But, sometimes we forget you can lie with numbers nearly as easily.

Numbers without context, without methodologically sound collection, without a fair cleaning process aren’t facts anymore than the average opinion. But, they can be more dangerous because they look like holy truths that can’t be challenged and debated the way opinions are.

Numbers aren’t holy truths, though. When we don’t like the numbers, we can change how the question is asked, how answers are recorded, or if those numbers see the light of day.

If next up the federal government decides it would rather not like Americans to know the number of deaths by wallaby trampling, the simplest thing would be to change the category to “marsupial deaths” on the form coroners file. (And, maybe, simultaneously start a smear campaign against kangaroos.)

By redefining how the category, the government could avoid the embarrassment of not knowing and instead say it can’t be determined from what we know.

And, even in this age of Big Data, NSA collections, and data investigative journalism, we need to remember that human decisions always play a part in the answers to our questions.

How else does “Serendipity” end up second on a list of all-time best rom-coms?

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