Living on the Data Fringes: Making Sense of Competencies

Posted by liaison on January 27th, 2021 Posted in: All Members, Blog, Data Science

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Organizations are striving to become data driven as they realize that data is becoming a mission critical topic. Finding, wrangling, analyzing, and managing data can require advanced computation skill sets. However, not everyone wants to be a data scientist, data analyst, or what the EDC Oceans of Data Institute calls a ‘data practitioner’. All members in an organization, no matter what their job, should have an appreciation of the scope of data, an awareness of how data is used in the organization, as well as, some basic data literacy skills. Striving to become data savvy should not be limited to those with science jobs who work with data on a daily basis. It is a competency that is also found in areas such as business, the social sciences, and even the humanities. We all need to understand how data is integrated into our lives. It would be helpful for someone diagnosed with a serious disease and researching at a public library to know how to interpret statistics about treatment and care options. Students in k-12 and up to the college level need to know how to find and interpret data as they conduct research in their classes. Humanities researchers might need to know how to do text mining to analyze a text corpus. But how do we know if we are data literate or competent in data skills? One way is to look at data competency models and find areas that you can relate to your daily work situation. What do you already know and/or do already? What competency areas do you need or want to learn more about?

Let’s start with the definition of a competency. A competency is a collection of knowledge, skills and behaviors that together demonstrate effective work performance in a particular area. It is a visible application of knowledge and behaviors. For example there may be managerial/leadership competencies, technical competencies, or functional competencies related to particular disciplines or job tasks. A competency does not equal a skill (although both are action-oriented). A skill may be one of the components of a competency, or several skills may be part of one competency. This competency concept is also important in k-12 and higher education where it is called competency-based education (CBE).

Now, thinking about data competencies, I believe the best approach to using competencies is to look at a variety of competency models or frameworks to better understand the expectations for knowledge, skills and behaviors related to your work position or desired job.  Many of the published competency models overlap, and you may be surprised to learn that your professional organization may have already established some data competencies for your role or position. Different competency models or frameworks might also overlap, so try looking outside of your specific area for more general competency areas such as research, communication, and collaboration. Use the competencies you find to guide personalized data professional development. What competency areas are you comfortable with? Which ones do you need to learn more about? You can combine the competency components that best fit your particular work situation, and use that as guide a build a professional development plan. The competencies can be a way to focus your learning and reflect on how to level up your data knowledge and skills.

Here are a few models to review and reflect on and find focus areas for your professional development:

• The Medical Library Association (MLA) has just recently published the data services competency to guide  professional development in this area. This article is the research done to develop the data competencies. There are 5 performance indicators divided into basic and advanced levels. You will see that data services outreach (performance indicator 2) and training and consultation on data topics (performance indicator 5) could be built upon other core librarian competency models and skills you already have as a librarian. MLA also has established health science librarian competencies.
• The ALA and ACRL competency website provide links to a variety of competency models for librarians involved in many different library contexts – for example core competencies, ACRL instruction and coordinator proficiencies (now called roles), teen services staff competencies, and ALA/PLA data competencies
• The Association of Research Libraries (ARL) has provided guidance on competencies for data management, scholarly communication, and open science.
• I even came across a Post-Doctoral set of competencies that overlap data and research competencies as I was working with a researcher on a grant.
• The Librarian 2.0 competencies are discussed in an article from the Library Trends Journal from John Hopkins Press and from the University of Illinois at Urbana-Champaign adress the changes in librarian role based on new technologies and perspectives of the library.
• An open access text, Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers, will help with assessing researcher and students needs and provides examples of teaching about data and information literacy.
• A whole program of recordings and slides from the 2020 Southeast Data Librarian Symposium weave together application of data literacy topics and information literacy.

So enjoy reflecting on your librarian role and how you can expand your competencies! It will help you articulate your strengths and skills and make a better case for your impact on the library and community.

Image: Gerd Altmann from Pixabay