While reading the L03 material, one of the Gartner research papers discussed data literacy. This term caught my attention, not because I heard it for the first time, but because of how it encapsulated the state of many enterprises’ workforce. And the day-to-day struggle of those tasked with maintaining, curating, and, most importantly, cleaning the data. In my own experience, I have encountered many datasets with invalid values and sometimes incorrect formatting. And periodically, spending countless hours cleaning up those dataset(s).
As enterprises evolve and legacy applications are scaffolded with new functionalities, the data inconsistencies can compound unless the solution is architected correctly. And this is where the term data literacy comes to the fore. In the same article, Gartner describes data literacy as the ability to read, write, and communicate data in context, including understanding data sources and constructs, analytical methods and techniques applied, and the ability to describe the use-case application and resulting value.
The question then arises: how do you increase data literacy in your team, business unit, or enterprise? One of the most effective Gratner’s suggestions involves establishing a communication platform that includes, for instance, a data dictionary and a business glossary to raise your organization’s data literacy level. According to Atlan, a data dictionary is a collection of metadata such as object name, data type, size, classification, and relationships with other data assets. A data dictionary acts as a reference guide on a dataset.
Establishing an enterprise-wide data dictionary can alleviate the overall data literacy of an enterprise. A centralized reference guide can also gradually improve data entry errors, which, according to HBR, can take up to 80% of data scientists’ time to clean up. With that being said, what is your enterprise’s data literacy like, share your thoughts below. I will leave you with this very apt meme shared by one of my coworkers during one of the projects we worked on.
Interesting blog Ali, I really could related to the data literacy challenge many organizations are facing. I believe that data literacy is an ongoing process that requires a dedicated team to continuously aim to improve the data literacy maturity and not just a once a year effort to be achieved.
Again great post.
Thank you for the comment
Hey Ali,
I couldn’t agree more with your thoughts on data literacy. It’s like having all the puzzle pieces but not knowing how to fit them together. We’ve got this goldmine of data, but its true value shines only when we really understand it.
On that note, have you delved into the concept of a Data Fabric? This integrated framework seamlessly connects an organization’s data tools and sources. One standout feature is the Data Catalog. Imagine it bridging the data dictionary and business glossary you highlighted. It merges the technical details with the business context, giving a comprehensive view of your data landscape.
Additionally, it leverages AI to activate this metadata using knowledge graphs behind the scenes to help bring out relationships, classifications, and other critical insights about data. This simplifies the data discovery process and empowers individuals to make data-driven decisions confidently. It’s like having a personal data assistant that guides you, ensuring you understand the nuances and intricacies of the data. This proactive approach significantly boosts data literacy, as users are continuously learning and adapting with the help of AI.
Thank you for the comment. No, I have not yet read up on data fabric, however, that does seem like an interesting concept. But I will definitely do now, thank you for the tip.