Back in the past we authored a series of posts about data literacy (see below for list of posts). We suggested that data literacy isn't something you have or don't have, either individually or organizationally, but rather is better understood as a set of related competencies, each of which is more like a slider than an on/off switch. In our experience, for example, most if not all employees have a deep understanding of what it means for data to be confidential, and what sorts of limitations on sharing and publishing are in place. Some of them can articulate compelling cases for why they themselves should have greater access to certain confidential data, and others can mount similarly compelling arguments in the other direction.
A more advanced competency here might involve not only understanding and following rules around confidential data, but also identifying data that hasn't been flagged as confidential, and then suggesting that classification. Or, similarly, we'd recognize some data literacy competency in people who can view a data product and notice when individually innocuous data elements combine to form something that should be handled more carefully.
Once you conceive of something as a competency, rather than a body of knowledge, or even a set of skills, your playbook opens up a bit (if we may borrow a sports metaphor).
If data literacy is a body of knowledge, then instruction, study, and testing are the way to develop it, and all it takes is the time and effort of student and teacher. If it's a skill, or a group of skills, then data literacy is more task-oriented: perform this calculation, build that visualization, interpret this statistical analysis, etc.
But a competency is broader, and more pliable, isn't it? Competencies include knowledge and skills, but they also include behaviors, attitudes, and habits. The way to build competency thus involves building habits, changing attitudes, and adopting new or better behaviors.
We have suggested in prior posts, such as this one, as well as in our webinars and videos, that our pragmatic approach to data governance and data intelligence will help people develop necessary and appropriate data literacy competencies. What we think is that the process of discussing, defining, verifying, curating, and challenging data will improve data literacy almost as a by-product.
Our clients and prospective clients continue to report they'd like to see improved data literacy among their employees, and a few have even pioneered some innovative training programs. What we like about these is that they do not start from the premise that employees lack literacy or data competency; rather, they start from the premise that employees are curious, and that they want to utilize data more fully and more frequently in their work. From this premise, many more topics, techniques, and approaches are available.
Increasing data literacy competencies among employees, while useful in the abstract, is probably not really an end in itself. What ends does it serve? It can be a way to help generate better conversations about data, or to streamline the flow of useful and accurate data across departments and units. Surely having stronger and broader data literacy competencies across your organization can assist in identifying data quality issues sooner and resolving them more efficiently. And of course it's a key component of actually using data to improve operations and make better decisions.
Don't better data conversations start with employees who know more about what data is collected, and why, and how to access it? Isn't trustworthy data facilitated with some level of curation of data stores, and some kind of certification of data products? Doesn't recognizing data quality issues require knowledge of data in context, and regular exposure to it in various locations and formats?
None of these are really possible in a consistent, reliable, ongoing manner without a data governance framework and related practices. And we all know from long and occasionally painful experience that without governed data, data-informed decisions are all but impossible.
Now, we believe that a framework for data governance can be very informal and still be successful, as long as it provides points of engagement to people all along the data lifecycle. These points of engagement include searching for existing answers, discovering useful data and data products, requesting information or assistance or additional data, and of course certifying or validating data.
No matter how a person attempts to engage with data, we think the best data governance practices are the ones that bring people together to share their knowledge, whether that's a data catalog, or a robust data requirements gathering process, or data stewardship meet-ups, or continuing training. We suspect it is not much of a coincidence that these are exactly the same kinds of activities that allow employees to learn from their colleagues, to extend the organization's data intelligence, and to develop data literacy competencies in a supportive, casual environment.
Here are previous blog posts regarding data literacy:- Looking Back in Order to Look Ahead
- Managing Data Sprawl
- The Nexus of Data Literacy and Data Governance
- Some Thoughts on Data Literacy and Data Quality
- Making Better Decisions by Better Managing Data
- Still More Notes on Practical Data Literacy
- More Notes on Practical Data Literacy
- A Practical Understanding of Data Literacy
There is also a "Developing Data Literacy for a New Employee" video available.
Our data governance consulting services and our data intelligence solution -- the Data Cookbook -- have been carefully designed over the years to identify, expand, and solidify these points and methods of data engagement, and they lend themselves to both formal and informal data literacy initiatives. We'd love to chat further about putting them to work for you! Hope you found this blog post beneficial. To access other resources (blog posts, videos, and recorded webinars) about data governance feel free to check out our data governance resources page.
Feel free to contact us and let us know how we can assist.
(Image Credit: StockSnap_LWUD4HMYXY_bulidingconstruction_dataliteracy_BP #B1285)
