Data Culture in the Library

If I asked somebody what a data culture is, they would probably look at me with a disinterested, glazed expression, followed closely by a mumbled, “I don’t know.”

And they may not really care either.

I feel like most people tend to see the word “data” as a very scary and intimidating word, and this prevents them from learning about just how useful and informative data can really be. Most people also tend to think that data is strictly quantitative information, such as numerical statistics. However, data can be text as well (such as emails, interview notes and media reports), which is qualitative data.

When analyzed, data provides ideas and solutions to various problems and situations. This ties into data culture and the benefits it can bring to a company or organization.

A data culture is an organization that uses data to make decisions to improve themselves. Some business leaders may prefer to make decisions based on gut instinct, ignoring data because their way just “makes more sense” (at least to them it does). A data-driven organization follows the path that makes the most sense in correlation with the data, even if it doesn’t necessarily make sense from a conventional standpoint. This has proven to be far more strategic and efficient. Erik Brynjolfsson et al. al. from MIT’s Sloan School of Management conducted a study that showed data-driven organizations to have a five to six percent higher output and productivity than other systems in which organizations tend to go with what they “feel” is the best path.

The Data Culture Project provides a great method for learning how data can be useful and how to connect with others through detailed analysis. The Data Process described below is a step-by-step system from the Data Culture Project that details how to bring about a more data-oriented workplace. The OSU Library is piloting the Data Culture Project this semester and plans to offer it in the future for those who are interested.

The Data Process:

#1: Warmups
Anytime you get a group together for a team-building exercise, warmups are a great way to get things started. Quick and creative activities that help to establish a feeling of comfort and informality allow participants to be invested and confident, leading to more engagement, which leads to more results in the long run.

#2: Asking Questions
This is the first step in analyzing data. When we look at a data set, there are plenty of questions to be asked, and there are plenty of questions to be answered. Colleagues are divided into small groups to analyze a data set and develop insightful observations and questions. Now, this exercise isn’t particularly about answering any questions that are asked, but it is instead about thinking creatively to generate stimulating questions.

#3: Gathering Data
This step goes hand-in-hand with the previous step. Data is difficult to interpret sometimes, even more so when the data is wrong or unorganized. You might need to combine multiple data sets as well, because a single data set may not be able to answer every question.

#4: Analyzing Data
Oh no, here comes the hard stuff!
Not really, but this is how many people feel when it comes to analyzing data. There are many different ways to analyze data because there are many different types of data. If you’re having a hard time trying to figure out a data set, maybe you just aren’t using the right method for you!  Analyzing data with colleagues leads to a combination of different ideas to maintain positive trends.

#5: Telling Your Story
This is the fun part of the process (as if you weren’t having fun before). This allows you to be creative while explaining a data set to colleagues. Various types of displays such as sketches and word webs communicate the context of the data through a narrative. This also allows abstract ideas and numbers to be represented by a concrete visual display.

#6: Try It Out
The final step is simple: just try it out! This answers many questions you may have about your developed story and the data backing it. Is the data understandable? Does the story make sense? Is it compelling? The idea of narrating your data is to influence your audience, informing and propelling them into action to improve the data.

A data-driven culture isn’t something that immediately happens, and it definitely isn’t something that has immediate results. It requires cooperation and understanding among colleagues, and the entire organization needs access to data in order to practice analyzing and interpreting it. In addition, data literacy must be encouraged so that each member of the organization can effectively contribute to improving and developing a collective network to help make important decisions.

Posted by Braiden Ellis, student staff member, Research and Learning Services