Data literacy – a collection of lessons
When it comes to data literacy, we’ve been keeping our ears to the ground during the last couple of months – attending webinars, reading blog posts, following experts on Twitter, and so on. And, while we are nowhere near finished learning, we feel like we have a sufficient collection of tidbits to share with you. So, without further ado, here are some of the lessons we have picked up on our crusades.
Unsure of what data literacy is? No worries. Click here to head over to our guide that will tell you all about it.
The need to restructure and adapt your organization
Largely sourced from Jennifer Belissent’s segment during the Qlik webinar titled “Using Data Literacy to Build an Insights-Driven Culture“, let’s begin this digest with the notion of restructuring your organization to encourage a data literate environment. This stems from the fact that organizations have traditionally been overwhelmingly focused on collecting and storing large amounts of data, often forgetting that it’s the next step that is the most valuable – deriving insight. So, as Jennifer highlights, it is crucial that organizations reshuffle in order to facilitate an insight-driven culture.
While there are a number of ways to do this, employing a new type of data leadership – one that realizes the value of data innovation – is a good place to start.
Staying inquisitive is key
Next up, we’d like to touch on an idea derived from Jordan Morrow’s recent Qlik blog post – “Ask the Right Questions“. While we highly recommend heading over to the actual article itself, let’s summarize. In his piece, Jordan emphasizes the importance of staying inquisitive – never letting one answer be the end of your search for more. He writes,
“we need to ensure that after we find some answers, we iterate, formulate new questions, and continue on the journey of being data driven organizations”
This transitions nicely into the concept of data skepticism (also taken from Jordan), i.e. arguing with and challenging data. This, too, is a central part of data literacy.
Data literacy + data quality = data trust
Another lesson that we would like to bring up is the above-mentioned equation. To spell it out, this suggests that data trust does not only come from a high level of data quality but, rather, needs to be combined with literacy in order to properly constitute trust. This, again, is an idea expressed by Jennifer Belissent, and we think it’s brilliant. Because, plain and simple, how can you trust something that you don’t understand?
There are different degrees of literacy
Finally, and in some ways most importantly, we want to remind you, as Jordan reminded us, that there are different degrees of data literacy. For example, a data scientist needs to be substantially more data literate than, say, a copywriter. But that doesn’t mean that a copywriter can’t be data literate too. What it means, rather, is that literacy is defined by your ability to read, work with, analyze, and argue with data on a level that is applicable to your job and your assignments.
So, when you hear people highlight the importance of data literacy, it is important to remember that this means different things to different people. In short, don’t let the knowledge of others intimidate you. Data literacy really doesn’t have to be that hard.