Data is the new black. Over 90% of the world’s data has been created in the past two years; and on average, people consume nearly 30 GB of data per day. From entrepreneurs to CMO’s of Fortune 500 companies, people simply can’t get enough of it. But often times, organizations hit a standstill because they don’t understand how to properly leverage the data to drive actionable results.
In some cases, it’s simply because they’re not looking at data through the right lens. John Johnson is a trained statistician, data consultant, expert witness and founder of Edgeworth Economics, based out of Washington, D.C. Author of the newly released book Every Data, he explains how some businesses neglect to properly harness this information and what they can do to begin collecting purpose driven data.
“Statistics and data can be powerful, but very misleading. As a statistician, I think about the world from a data driven perspective. But what I’ve noticed is that averages are just like a snapshot,” Johnson says, “It just explains one frame and sometimes neglects to tell the whole story. This can lead to terrible decision making.” A good example to illustrate this point is to compare the average salary of a mayor across America (around $60,000) with the salary of a deputy mayor (around $80,000).
At an initial glance, it may seem confusing that a deputy major earns less than an actual mayor. However, these data points fail to consider that only larger cities like New York and Philadelphia have deputy mayors, while every small town and metropolis have mayors. “What you’re averaging can dramatically skew the results. Thinking deeper about data will help [business owners] make more sense of it,” he says.
Speaking of “average”, John is the exact opposite. Back in 2010, he developed a company of “wiz kids”, which wasn’t necessarily the norm. “In a typical firm like ours, you see older people working with their much young apprentices,” he explains. By refusing to settle for the average, he has since expanded his firm to 80 employees in three offices.
For companies looking to improve their data collection or analyzation methods, John suggests the following:
1. Make sure the data you’re reviewing is correct: The first step in analyzing your data is to make sure it’s the correct data set. When the temperature control company NEST was bought by Google, the ticker jumped by 1900% in one day. Unfortunately, that ticker wasn’t for NEST – it was for a similarly named penny stock that had gone bankrupt. Slowing down and taking time to review which data you’re looking at is essential to the success of your data analyzation efforts.
2. Determine what question the data is trying to solve: While some people can be data hoarders, this leads to massive efforts for data mining that isn’t even useful or applicable. Take the time to sit down and decide which problem you’d like for your data to solve, prior to gathering several data points.
Have you ever fallen victim to bad data practices? What did you do to fix it? Let us know by sending us a tweet at @WiseNapkin.