Big data is , well, big thing these days. Honestly, it has been for a while. We make so much data by interacting with digital tools. Daily 2.5 Exabytes are produced every day. That is the equivalent to 5 million laptops filled to the brim with data. Imagine yourself right now attempting to find one thing in the middle of all of those computers. (You might envy the person seeking the needle in the haystack). So, even with all of these data points, I am making a radical suggestion. We don’t have enough data. Or rather, we need to diversify the types of data we collect.
Let’s take this scenario. A visitor decides to participate in a class at your organization. They use your online ticketing system. Even the worst of online ticketing system gets their name, address with zip code, their preferred class, and their method of payment. (If you don’t at least get this, make a new choice of system). Right now, you have a great deal of data about that patron. And this data is basically quantitative; you can run the numbers on them. You calculate elements that help your audience. For example, this person is 1 of X number of people from a zip code or one of X number of people who choose Mastercard.
So, let’s get back to our patron, shall we? Then this person arrives at your facility. They drive up to your institution in their shiny teal mini-cooper. They press the button for the ticket, and it doesn’t work. They press it again. Finally, a staff parking attendant comes out. He apologizes and explains that the button they are pressing says “Staff”. They need to press the larger button which says, “Please Press, Dear Visitor”. Mr Parking R. Deck hands the patron the ticket with a smile. Mollified, the patron drives into the parking lot. In this second connection point, there were other data elements playing out. Many questions come to mind immediately. How many times does the visitor try the wrong button before calling the staff? How often do staff get called to explain the buttons? Is there a correlation between age and misreading the button? Is there a correlation between height of car and misreading the button? I could go on…
Most institutions leave this type of data on the table. It remains anecdotal; the stuff of staff meetings and lunchrooms. There are several factors for this:
- The first might be the word data itself. Data has a mathematical aura that is often segregated to certain fields, like finance and technology. Going into roll call at security, you might seem incredibly radical to ask security to track their “data”. Solution: There needs to be wider understanding across the field of data and the possible sources.
- Data only exists if captured. Think about that for a minute. If you are not holding on to it & aggregated it, there is no data. Data collection takes time and resources. You need to have tools for collection. You need to train people. Solution: Institutions need to reallocate expectations to change the culture of data.
- Data use needs to start with a goal. Right now, many institutions are in the peer pressure phase of data collection; collecting because everyone is doing it. Rather than employing the scientific method that underlies so much of the work of our field, they don’t collect with a goal or thesis in mind. Without a goal, it sure is hard to make a roadmap to that endpoint. Basically, institutions are often wandering in the weed fields of data. Solution: Data literacy needs to include clear education on goal setting.
Making data a part of institutional culture might seem costly, and it would be, but think of this. There are many members of the museums staff who spend the bulk of their time with visitors, including guards, teaching artists, and visitor experience professional. Collectively, this is the sector of the museum who tracks the least amount of data. If you asked any one of them, if they’ve noticed a challenge finding the restroom, they would be able to tell you immediately. This is also one of the most transient members of our staffing. They might move up in the field or move out of it. Either way, their observations remain anecdotes—basically little data. In order words, institutions throw away what could have been the most valuable data on their clients every day.