Different people mean different things when they talk about data literacy. For this article, so that we are on the same page, let's use the definition of "the ability to create, manage, read, work with, and analyze data to ensure & maximize the data's accuracy, trust and value to the organization" (D. Marco, Ph.D.). Note that this definition highlights a crucial characteristic of data literacy which entails coherency and collaboration within the organization, something that often reflects a particular kind of culture. I'm referring to the data culture, which is an integral part of data strategy, merging the business objectives and plans with the data world where data becomes a kind of asset.
All that's great, but it may seem a bit abstract. However, data literacy is very hands-on, even if it's not as low-level work as analytics. It is also utterly significant for all sorts of professionals, particularly decision-makers. Many people talk about making data-driven decisions and having a data-driven approach to problem-solving. How many of them do it, though, and to what extent? Well, data literacy enables professionals to do just that and make data something they value and leverage for the benefit of the whole.
Data literacy is beneficial for other people too. For instance, when someone works in a data-literate organization, there tends to be more transparency about how decisions are made and what different pieces of data mean. So, if you have a role that involves data in some shape or form, you can be sure that it's not a black box and that you can learn from it. Naturally, this implies that you are data literate to some extent too!
Data literacy is a state of mind, a way of thinking and acting about data. As such, it has many benefits that depend on the organization and the data available to it. The fact that many companies base their entire business model on data attests to the fact that data is crucial as an asset. To unlock its value, however, you need data literacy.
Key Ideas and Concepts of Data Literacy
Let's delve deeper into this by looking at the components of data literacy. For starters, data literacy involves understanding data and how it is governed. This part of data literacy is vital since many organizations have lots of data that is essentially useless because it's in silos and inaccessible to those who need it. This problem is essentially a data governance one. Also, as much of it involves personally identifiable information (PII), it has to abide by specific regulations such as GDPR. Otherwise, this data may be a huge liability.
Data literacy also involves analytics, as it is when data is turned into information that it truly becomes useful. The latter we can understand better and reason with, especially in decision-making. Data in its original form is usually understandable only by computers. Analytics makes this transformation and enables others to benefit from the data. Usually, analytics work is handled by specialists, such as data scientists.
Data literacy also involves presenting and communicating data. This part of data literacy often entails reasoning about insights and exploring how they can apply to an organization's challenges. Otherwise, data has just ornamental value, which may not be enough to justify people working with it. Perhaps that's why today every data professional is assessed based on communication skills too, not just technical ones.
Finally, data literacy involves protecting the data and whatever information it spawned. It's usually specialized cybersecurity processes that ensure the protection aspect of data literacy, which also includes preserving the privacy of the people behind that data. In larger organizations, there may even be specialized professionals involved in this kind of work.
What does a data-literate professional look like, though? For starters, it's not like he stands out from the crowd. But when that person engages in a conversation on a business topic, it becomes clear that they know how data can be used as an evergreen asset. Such a person may also undertake responsibilities related to the use of data in decision-making, be it through a data-driven marketing initiative, a cohort analysis of the customers or users, etc. A data-literate professional can undertake numerous roles, not just those related to hands-on data work. He can be a competent team leader, a business liaison, a consultant, and even an educator, promoting a data culture in the org. As long as that person has a solid understanding of data and how the organization can put the data available into good use, that person is a data literate professional and can add value through that.
Generally, data-literate professionals are very competent in leveraging data for making decisions and driving value in the org. This aspect of data literacy often involves having a sense of data and its potential. For someone else, data may be just something abstract and interesting to data professionals only. For the data-literate professional, however, it's something as powerful as a product sometimes. At the same time, it's a pleasant challenge because just like products need work before you can trade them for money, data also requires special treatment. A data-literate professional accepts this challenge and works towards making it a reality. This special treatment may involve getting the right people in a team or leveraging the existing ones, doing some mentoring even, and turning this understanding of data into a set of processes that transform data into something of value.
When it comes to data literacy, there are several challenges most professionals change. I say most because people with a data background tend to find this whole matter intuitive and relatively easy. However, people who come from different backgrounds tend to struggle with data literacy in various ways. After all, traditional education stems from a time when data wasn't something educators knew or cared about. Their data literacy skills were rudimentary at best, while they focused on educating people about those business models and concepts that were more relevant back then. That's not to say that business acumen isn't that important. It's more important, however, when it's integrated with data acumen (as Bernard Marr eloquently illustrates in his book and courses on data strategy).
Data literacy is a journey for most professionals, and there are different levels to it. Maintaining a sense of humility about this matter and understanding there is always more to learn can go a long way. This isn't an easy task, especially for accomplished professionals who got far in their careers using traditional ways of thinking about assets and business processes. Perhaps through proper coaching, mentoring, and other educational tools, they can overcome the challenges that plague this journey toward complete data literacy.
Data literacy is crucial in today's digital economy. As data is what some refer to as the new oil, the prima materia of many products, data literacy is the equivalent of an oil-based engine. The main difference is that it doesn't pollute and there are no practical limitations on the fuel! Nevertheless, it's not trivial as some data people make it out to be. Of course, you can plug this data into some off-the-shelf model and get it to spit out some results that you can put into some slick presentation and share with the stakeholders. However, this is often not enough or relevant. Data literacy helps people see how the data relates to the business objectives, tackling specific problems and answering particular questions. Having some fancy data model may be something interesting to boast about, but if it doesn't help the organization with its pain points, it seems like an ornament rather than something of value. Going back to our metaphor, it's more like a gadget than an engine that can help us traverse the distance between where we are as an organization and where we need to be.
To be continued...
In the meantime, feel free to learn more about data literacy in the corresponding page of this site.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.