Spreadsheets,Software,Storytelling,Visualization,Lifelong Learning: Essential Data Skills for Journalism and Strategic Communication Students

From Data Journalism to Data Literacy

Curricular discussions about statistics, data, and computational skills that focus exclusively on data journalism address a niche problem (e.g., Berret & Phillips, 2016; Bradshaw, 2018; Dunwoody & Griffin, 2013; Heravi, 2018; N. P. Lewis, 2020). Journalism students today comprise a minority of JMC program enrollees, next to the more sizable ranks of strategic communication students (McLaughlin et al., 2020). When statistics and data instruction is seen as a component solely of the journalism subfield, other curricular demands likely take precedence and limit the available instructional resources.

Sustainably incorporating data instruction into JMC programs entails reframing these skills as an asset for all students. Such reframing can increase demand and free up resources to support consistent course offerings. While strategic communication students often already receive some statistics instruction in research methods courses (Poindexter, 1998; Robbs & Gale, 2005), the recent datafication of strategic communication (i.e., audience metrics, analytics platforms; Wedel & Kannan, 2016) justifies amplifying data skill instruction. Business schools have responded to the need for data-fluent communication practitioners with marketing metrics and marketing intelligence curricula (Houghton et al., 2018; Liu & Levin, 2018). To maintain their graduates’ employment competitiveness, JMC programs likewise must evolve the data training their students receive.

Fundamentally, the statistical reasoning and data skills that strategic communication and journalism students need to develop do not differ. Employers hiring marketing analytics positions, for instance, look for candidates who can serve three functions:

(1) a “data monger” who knows where data can be found and can go get it, (2) an “applications person” who can apply best practices to data sets, and (3) an “insight person” who can translate the results into useful insights and convey them effectively to management. (Houghton et al., 2018, p. 41)

Analogously, practicing data journalism entails three broad tasks: data acquisition and cleaning, pattern detection, and data representation (N. P. Lewis, 2020; McAdams, 2019). Embracing the overlap in required data skills between strategic communication and journalism can open possibilities for common courses and learning experiences.

Data literacy can serve as an organizing construct for statistics and data instruction for JMC students, in place of the narrower concepts of statistical reasoning, data journalism, and computational journalism. Data literacy is “a suite of data acquisition–, evaluation–, handling–, analysis– and interpretation–related competencies” (Prado & Marzal, 2013, p. 124). The construct also encompasses the ethical implications of working with data, including identifying quality assurance, cultures of practice, and ethical data citation (Carlson et al., 2011; Prado & Marzal, 2013). Skilled data practitioners are expected to not just tell audiences what the data mean, but to “recognize when data are being used in misleading or inappropriate ways” (Carlson et al., 2011, p. 633). Data literacy encompasses and helps organize the corresponding data proficiencies enumerated in disparate discussions across the JMC literature and broader instructional contexts including statistics education (Gal, 2002; Wild & Pfannkuch, 1999), numeracy (PIAAC Numeracy Expert Group, 2009), data science (National Academies of Sciences, Engineering, Medicine, 2018), and information literacy (Association of College and Research Libraries, 2015).

JMC Data Project Lifecycle

Statistical concepts are best taught using the statistical investigative cycle, that is, with hands-on projects in which students progress through planning, collecting, and analyzing data, and then articulating and communicating data insights (Wild & Pfannkuch, 1999). The structure of the statistical investigative cycle mirrors data literacy. Accordingly, progressing through the entirety of a data project should be an effective method for learning data literacy skills. Drawing on the statistical investigative cycle and data literacy frameworks, and on the discussions of statistics and data skills in JMC literature (e.g., Berret & Phillips, 2016; Dunwoody & Griffin, 2013; N. P. Lewis, 2020; Nguyen & Lugo-Ocando, 2016; Ojo & Heravi, 2018), we constructed the JMC Data Project Lifecycle, an inventory of skills used in JMC data projects, and a tool for defining learning objectives and designing data instruction (see Figure 1). The lifecycle consists of two dimensions: project sectors and tool customizability. These combine to identify 15 cells, each containing one or more data skills with a proficiency range from novice to expert.

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Figure 1.

Graphic Representation of the JMC Data Project Lifecycle.

Tool Customizability

Along its radius, the lifecycle organizes data skills in three bands of tool customizability: manual, software-, and code-assisted. Data skills at the manual level do not involve specialized tools that could be customized, and include tasks such as calculating averages for small datasets with a calculator, or visually inspecting data for a compelling story. The software-assisted level involves user–interface databases and out-of-the-box software applications (e.g., Microsoft Excel, Tableau). These tools are customizable to the extent permissible by their publishers. The code-assisted level integrates computational skills, where data storytellers function as developers, customizing functions written in programming languages (e.g., Python, JavaScript, R) to match specific project needs. See Figure 2 for an inventory of skills representative of each project sector and tool band.

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Figure 2.

JMC Data Project Lifecycle Skills Inventory.

Effectively teaching all the data concepts, skills, and tools enumerated in the JMC Data Project Lifecycle is impossible in a single course or even program. We thus turned to journalism and strategic communication practitioners to delineate instructional priorities. This inquiry was guided by the following research question: What are the essential data skills for today’s JMC graduates?

Spreadsheet Wherewithal

Practitioners identified two competencies we call spreadsheet wherewithal as fundamental to JMC students’ data literacy: the abilities to navigate the features and components of spreadsheet software, and to imagine how data in a spreadsheet can be wrangled and analyzed to yield useful insights. Practitioners collectively categorized the spreadsheet as the principal tool of their data work and expressed the expectation that JMC graduates work with spreadsheet data, specifically Microsoft Excel: “Being able to work in Excel is essential” (Practitioner 15); “Any kind of Excel data or Excel training is massively important” (Practitioner 4); “It seems like most of my work, for better or worse, with big datasets, happens in Excel, as much as I wish there was something newer to use” (Practitioner 20). Other practitioners itemized specific spreadsheet skills: “understand what a spreadsheet is” and have a “basic fluency or comfort with Excel,” including “some really, really simple formulas like mean, sums, counts” (Practitioner 14); “know your way around a spreadsheet and how to, kind of, group and organize, and categorize data” (Practitioner 7); understand “simple data analysis,” including “something as simple as knowing how to use an Excel formula” (Practitioner 17). Even Practitioner 1, a data journalist who listed several R packages when identifying the main tools she uses, pointed to spreadsheet software: “Obviously we still use Excel and Google Sheets, and those are huge tools.” The practitioners’ use of the shorthand “Excel” in place of “spreadsheet” underscored the ubiquity of this software package and the marketability of Excel skills.

Spreadsheet wherewithal includes understanding the standard structure of data in a spreadsheet and knowing how to execute basic data cleaning and analyses. Practitioner 14 described this as orienting oneself inside a dataset:

Basically, just look at your spreadsheet and understand what seem to be the problems in it. Or what’s complete and what’s not complete. What type of variables are there? Just, basically, learning what your dataset looks like.

This ability presupposes understanding conventional spreadsheet structure, including what makes data clean, and the steps to deriving such a dataset. Practitioner 2 underscored needing experience working with both clean and unclean data:

Some datasets come across really clean or easy to work with and others take a bunch of individual work to, like, make usable. And so, just knowing what a dataset should look like. And knowing what I can just start working on and finding information immediately. And what’s going to take me a day to get in shape, and how do I do that.

Finally, practitioners said spreadsheet wherewithal includes the ability to interrogate data to derive useful insights. Practitioner 15 characterized this as “ask the right questions in the beginning,” including establishing “what are the needs of this data and, like, what are we trying to get out of it?” At its core, Practitioner 14 said, this ability to interrogate data for insights entails “a curiosity of what questions you can ask and potentially answer.”

Software Over Coding

Practitioners favored knowledge of commercial software over the ability to work with data manually or using code. In addition to spreadsheets, they emphasized the need to know the entire suites of Microsoft Office and Adobe Creative Cloud. They pointed to PowerPoint as an essential data presentation tool and to Illustrator as useful for visualization. Other software such as Tableau, Flourish, and Salesforce marketing and analytics fell into a gray area between essential and valuable.

While the practitioners did not advocate that JMC graduates possess coding skills, several said that the ability to code elevates job candidates into an above-average category: “If you have that skill, it does make your resume stand out and people get excited about that” (Practitioner 4). Practitioner 23 differentiated between generalists and data specialists:

I think if a student really has a goal to be a data reporter, they should learn Python. But many types of reporters and editors—those covering science, sports, business, education—need to be able to understand and interpret data in some stories. So, they can get by with commercial software.

Practitioner 13 advocated for JMC students to have exposure to coding but not necessarily fluency, and recommended learning activities “where you’re touching [coding] but you’re not, like, building it all from scratch.” At the other end of the tools continuum, Participant 12 argued that fluency in manual data analysis helps individuals adapt to evolving software. He urged educators to

Emphasize the concepts because programs will change. Open Source is here, and other things die all the time. But what if you’ve learned the methodology? Then you just have to relearn a new technology. But you know what you need to do and what each step is, to make sure your analysis is well done.

Storytelling with Data

Practitioners coalesced around the skill of translating data insights for both colleagues and external audiences. This includes the ability to make data understandable. Practitioner 15 drew on her experience with PR and marketing teams to underscore the ability to present data in simple terms:

When you’re presenting the data to your team, and they need to use that data to then do the next thing on their list, I think just remembering that it has to be simplified. Not that they’re not smart, because they’re all very smart. But let’s say they’re not in Excel every single day, or if they don’t know how to use a pivot table. That is going to be a hurdle, you know, when they’re reading the data.

Conveying the relevance of the data for an audience by humanizing it constitutes another dimension of data storytelling. JMC graduates need to understand that presenting data without contextualization is ineffective: “You need to know how to write it in a way that’s not just like, ‘Here’s numbers, have fun’” (Practitioner 2). Practitioner 21 characterized this as the ability “to use numbers without even using numbers.” Several practitioners discussed using human anecdotes to communicate the meaning of data:

You have to be able to relate it back to a person. A lot of readers connect more with people, that’s why they read a lot of stories. Your readers are looking for that personal connection and numbers aren’t always a connection that they can make. (Practitioner 1)

Practitioner 18 argued that the anecdote is “so much better than [just saying] ‘10 percent,’ right? I mean, you have to have both. But you have to have that emotional connection. So, obviously, that’s [the job of] the anecdote.” This marketer also emphasized JMC graduates elevating the data’s emotionality:

Understand that people ultimately make decisions based on emotion, not on facts and data. Facts and data help create emotions. But people do not make decisions solely based on facts and data, so the purpose of your facts and data are to create emotions. . . . So if you’re presenting your data to an audience of five people, what do those five people care about? . . . Whatever it is, tie it to the emotional things they care about and use your data as a foundation, but not the end of the story.

Connected to this, Practitioner 12 argued that data need to be supplemented by conventional information gathering techniques: “Data only tells you the ‘what’ and the ‘why,’ so nothing replaces the importance of being out in the community.”

How and When to Visualize Data

Practitioners believed having data visualization skills is essential. Practitioner 4, who did not learn visualization in college, discussed the difficulty of depending on her coworkers for these skills:

It was always talked about [in college] but it was not something that I had to do an assignment on. Then, once you’re on the job, I find in newsrooms, there’s not a lot of people to teach you brand new skills outside of your editor. But my editor is not dealing with graphics either. So, if I were to, like, pick something up in the newsroom, it would be from a graphics person.

Practitioner 15 argued explicitly that data visualization and presentation skills are essential for JMC graduates:

If you’re a journalism student, you need to know how to work with data and make it into like, you know, a visual slide or presentation. I think you do need to know how to work Excel and I think you need to know how to work a presentation tool like PowerPoint . . . any college student needs to know how to use PowerPoint.

Practitioner 7 dug deeper into the necessary skills, emphasizing knowing which visualizations to use and when to use them. She said that students should know “basic rules about chart types, and which kind of chart types are best in which situations.” Practitioner 2 underscored the supplementary nature of visualization, and the ability to discern when visualization is needed: “I think being able to visualize data helps the reader a lot, but I think you can use data in a story without having a graph next to it.”

Lifelong and Self-Directed Learning

Practitioners expected fluency but not expertise in data analysis and interpretation from JMC graduates. They suggested instead that those individuals with limited data skills should connect with experts and data partners and draw from a community of practice to ensure the data projects they are undertaking are accurate and of high quality. Practitioner 4 urged seeking out experts to critically verify results:

I think that it is important when you talk about data analysis, to get other people’s opinions on the data. Because we can make our own assumptions. But you have to get somebody who’s really an expert in the field to really crunch those numbers for you.

Practitioner 12 spoke about having a “data buddy,” that is, someone with whom to practice data skills and who can “fact check your work.” He suggested that college courses include “building community and connecting students to outside resources,” which can help answer data-related questions and motivate professional development after graduation.

More broadly, practitioners advised that JMC graduates engage in lifelong self-directed learning. Practitioner 20, among others, argued that continually building knowledge through in-person and online relationships is an essential skill:

The biggest thing is realizing that it’s not as important to know things as to know where to find things. I think you have to get over the fear of being afraid to ask. For me, data work has a lot more to do with making sure I have the relationships with people to make it make sense.

Illustrating such relationships, Practitioner 14 and others explained that they are active in online communities through social media and other digital platforms, and attend conferences to learn about industry standards, best practices, and to supplement their knowledge. Practitioner 7 argued that knowing where and how to look up answers is a key skill:

I always tell people that the best skill that you can have is to be a good Googler, because there’s no way that you’re going to know all of these [programming] languages or, you know, even half of them. But you need to know just enough to be able to Google what you’re looking for. Oftentimes that’s the first big hurdle because there are so many great resources on the web, you can find videos and tutorials and stuff. But if you don’t know even the term to Google, you can’t get where you need to be.

Others echoed the fundamental ability to engage in on-the-job problem-solving: “Don’t ask me questions if Google can answer those questions for you. Learn to teach yourself stuff” (Practitioner 15).

This call for integrating lifelong learning practices into professional practices aligns the Data Project Lifecycle model with data and science literacy in recognizing the social and dynamic nature of data storytelling. The lifecycle additionally underscores opportunities for students and practitioners to pause in the development of a project to draw from personal and communal knowledge, to question the accuracy of data they have identified or collected; the extent to which visualizations accurately represent proportions, change, and timeframes (statistical reasoning); and the ability of the data analysis to address the questions asked. In conjunction with editors, other practitioners in the community and the public, data-, and science-literate communicators can ensure that the data stories they tell are the stories they mean to tell, and that they accurately represent and cite the data that underlie these stories.

Limitations

The Data Project Lifecycle skills inventory (Figure 2) is not exhaustive of all data skills, and new skills and tools emerge regularly. For example, we highlight some ethical considerations needed for the transparent and accurate interpretation and communication of data. We give attention to concerns about the privacy and security of data collection, the recognition of uncertainty in statistical results that should prohibit overconfidence (Francois et al., 2020), and the need to cite sources. Yet, we leave space for others to incorporate a deeper and more robust discussion of ethical issues that arise in data collection, analysis, and reporting, and the skills needed to incorporate these into the model.

Furthermore, it is important to highlight that, although we are confident in the durability of the general lifecycle structure, some of the specific skills and tools may be time-dependent. In addition, while we attempted to collect a diverse sample of varied perspectives, we avoid making generalized conclusions due to the convenience nature of our sample. Given the common themes around which the practitioners converged, however, we believe these findings hold substantial value. Finally, we acknowledge that our sample was drawn only from a U.S. population. The lifecycle approach is universal but because population-level adult numeracy varies across countries, the essential skills JMC students develop in college likewise also may be country-dependent.

Implications

The data literacy construct and the JMC Data Project Lifecycle encapsulate related educational outcomes, including the JMC accrediting organization’s expectation that graduates be numerate and comfortable with statistics (Accrediting Council on Education in Journalism and Mass Communication [ACEJMC], n.d.). Data literacy likewise incorporates statistical reasoning, the focal concept of Dunwoody and Griffin’s (2013) analyses (Griffin & Dunwoody, 2013, 2016, 2017). According to the instructional method of the Data Project Lifecycle and the statistical investigative cycle on which it is based (Wild & Pfannkuch, 1999), students develop statistical reasoning by completing data projects. Statistical reasoning consists of understanding concepts such as distribution, randomness, sampling, association, and using these concepts to derive inferences (Dunwoody & Griffin, 2013). Depending on the project, instruction and practice with these concepts may be incorporated where they are most salient, including in the planning, data acquisition, or analysis and interpretation sectors of the lifecycle.

The JMC Data Project Lifecycle is meant to be used by JMC administrators or instructors for curricular design in both programs that can afford a data course or sequence (e.g., N. C. Lewis, 2020), and in those where data skills are integrated into courses addressing other topics such as editing or public relations writing. Such curricular development would follow these steps: (a) identify the data skills students need to learn, (b) designate the optimal proficiency level for each skill, (c) plot the lifecycle sectors through which students need to progress to learn the targeted skills, and (d) designate course units in which students advance through the lifecycle to reach each skill’s desired proficiency. The circular nature of the lifecycle emphasizes the iterative process by which individuals learn data skills, supporting the use of learning progressions and instructional sequences within and across courses (Duschl et al., 2011). Instructors ideally will design learning experiences that allow students more than one pass through the project lifecycle, prompting students to work with increasing independence through data projects (Johnstone et al., 2002; Lauer & Hendrix, 2009). Reflecting a key participant recommendation, instructors also would require students to engage in self-directed learning to develop the expectation that as future practitioners, they continually strive to supplement their professional data skills.

The JMC Data Project Lifecycle emphasizes JMC-relevant skills used for data storytelling and deemphasizes skills that may be more useful in other professions. Specific JMC subfields likewise may accentuate only some of the skills presented in the lifecycle sectors. For example, a JMC program that emphasizes digital user experience design may focus on analytics, deemphasizing the acquisition of data by having students work only with data generated by analytics software. A marketing communications program, in contrast, may center instruction around sentiment analysis and emphasize the acquisition of primary sentiment data. Both programs might also deemphasize organizing data in favor of analysis and explanation skills. The Data Project Lifecycle thus provides a flexible framework that can be tailored to specific JMC program characteristics and student needs.

The practitioners consulted for this project identified seemingly rudimentary skills—spreadsheets, software, storytelling—as essential. They characterized as less vital computational skills emphasized in recent JMC pedagogy literature (Berret & Phillips, 2016; Coddington, 2015; N. C. Lewis, 2020; McAdams, 2019). This should not be surprising, as research findings in this literature have regularly stressed the continued need for foundational skills. For instance, even though Berret and Phillips (2016) examined the teaching of advanced data skills, they found that practitioners largely desired to learn nonadvanced skills like acquiring data, identifying insights, and presenting those findings. Fink and Anderson (2015) also wrote about journalists needing basic spreadsheet skills. Treadwell et al. (2016) found that JMC graduates’ “minimal knowledge of Microsoft Excel” (p. 302) prevented the successful implementation of a specialized data journalism training. Many university students today, not just JMC students, lack rudimentary spreadsheet skills (Eichelberger & Imler, 2015; Wilkinson, 2006), likely because many students today do not learn spreadsheets in middle or secondary school (Hindi et al., 2002).

This project’s participants agreed that the minority of students who plan to specialize in data analysis as journalists or strategic communication practitioners will benefit from fluency in a coding language. They felt that the ability to code is admirable but unnecessary for generalists. The JMC Data Project Lifecycle positions such computational skills as extensions of conventional data skills, distinguished by the customizability of the tools involved. Instructors may expose students to computational processes as an alternative pathway to completing a data project, and offer advanced students opportunities to learn and use computational packages. Practitioners preferred all JMC students to be fluent in software with user-friendly interfaces and out-of-the-box applications, reasoning that understanding how to operate software and interpret its output constitutes a more general and immediate need than the ability to code.

While the practitioners also did not specifically emphasize the ethical principles of working with data, questions about privacy, security, accuracy, transparency, among others, are important to each segment of the Data Project Lifecycle. As with other JMC courses, instruction must include attribution conventions of material. Data literacy instructors can create opportunities for students to engage with questions about what should be done with data in addition to what can be done with data. The JMC Data Project Lifecycle includes elements of lifelong learning that can position data storytellers within communities of practice that assist with ethical data collection and quality assurance.

Broadly speaking, data literacy supports the development of an informed global citizenry (Fontichiaro & Oehrli, 2016), which is widely recognized as a key mission of higher education. Although this project focused narrowly on data literacy for JMC professionals, greater understanding of software-based data work is bound to increase JMC students’ understanding of data outside their professional field and help them participate more effectively in a data-driven society.