More Notes on Practical Data Literacy

More Notes on Practical Data Literacy

StockSnap_LT3CKJ1TD9_letters_NotesPracticalDataLiteracy_BPLast month we posted some thoughts on the importance of data literacy for modern organizations, how growing data literacy can be part of a data governance program, and how the Data Cookbook in particular can contribute to efforts to increase data literacy.

Those preliminary thoughts led us to do some more research into concepts that underlie data literacy, specifically literacy and numeracy. And it turns out that we gave short shrift to serious work already done on these topics! While numerous definitions of literacy exist, this one in particular (from the Program for the International Assessment of Adult Competencies, or PIAAC) caught our eye: "Literacy is understanding, evaluating, using and engaging with written text to participate in society, to achieve one's goals, and to develop one's knowledge and potential." (This is a considerably more nuanced definition than simply the ability to read and write!) Written texts, whether print or electronic, can be a means of communication, they can be a method of accomplishing tasks and solving problems, and they can serve as an instrument for learning.

The same organization defines numeracy as "the ability to access, use, interpret, and communicate mathematical information and ideas, in order to engage in and manage the mathematical demands of a range of situations in adult life." (We have also seen numeracy referred to as quantitative literacy, which sounds more reasonable when literacy is understood to include accomplishing tasks and meeting goals.) Examples of numerate behavior would include creating, understanding, and expressing arguments and propositions supported by quantitative evidence across a variety of formats.

PIAAC's literacy and numeracy assessments divide respondents into proficiency levels, and we wonder if that might not be a more effective way to think about data literacy. Remember that our working definition of data literacy included understanding, interpreting, asking questions about, and communicating using data; each of those aspects is an area where a person or group might show greater or lesser levels of proficiency.

For example, Level 3 (on a scale of 1 to 5) literacy proficiency tasks are described thusly: "Tasks require the respondent to identify, interpret, or evaluate one or more pieces of information and often require varying levels of inference. Many tasks require the respondent to construct meaning across larger chunks of text or perform multi-step operations in order to identify and formulate responses. Often, tasks also demand that the respondent disregard irrelevant or inappropriate content to answer accurately."

Level 3 numeracy proficiency tasks "require the respondent to understand mathematical information that may be less explicit, embedded in contexts that are not always familiar, and represented in more complex ways. Tasks require several steps and may involve the choice of problem-solving strategies and relevant processes. Tasks tend to require the application of number sense and spatial sense; recognizing and working with mathematical relationships, patterns, and proportions expressed in verbal or numerical form; or interpretation and basic analysis of data and statistics in texts, tables, and graphs."

Given these descriptions, maybe we should not be talking about "data literacy" at all! Maybe what we are really talking about is the application of literacy and numeracy proficiencies, such as evaluating information, drawing inferences, constructing meaning, and recognizing patterns, in a data-specific or data-rich context.

When we discuss data literacy with clients, we are almost always operating in a specific situation in which organizations wish to utilize their collected data to improve operations and to run their business more successfully. Most of our clients are staffed by a highly literate and numerate workforce, members of which generally understand that data is a valuable asset, that certain data is sensitive and must be protected vigorously, and that accurate and available data is an organizational necessity. This understanding already reflects a certain amount of data literacy!

In the rarefied air of data management, it is easy to take that level of data literacy proficiency for granted, and as with any under-interrogated assumption, we do so at our own peril. As younger people enter the workforce, for example, they may have different expectations about the way data is used and shared, and which safeguards are appropriate. As cloud storage and mobile platforms supplant traditional technologies, existing policies and practices may no longer be applicable. It behooves all of us to look up from our narrow focus now and again.

Data literacy as a concept is frequently couched in the language and rhetoric of data science, data mining, developing models to support predictive and prescriptive analytics, etc., and it is supplemented by a bewildering array of charts, graphs, and other visualizations. The whole package is typically flavored with statistical jargon such as confidence intervals, margins of error, and so on. This can seem an insurmountable challenge. But is this really the level of proficiency we expect from our employees? Some of them, perhaps. But the vast majority need to fall other places on the proficiency scale.

Last time we wrote about the need for data literacy at the various stages of the data life cycle. When collecting data, the critical data literacy proficiency may be ensuring that data is valid and complete. When transmitting data between systems, that proficiency may entail recognizing and accounting for consonance or dissonance between systems. When analyzing data, or preparing data for analysis, data literacy will entail distinguishing relevant data from irrelevant data, and organizing it appropriately. When publishing data, data providers may need to construct meaning to assist data consumers, who themselves must be able to make reasonable inferences and draw supportable conclusions.

Any organization in this situation uses a variety of tools, techniques, and personnel. Our Data Cookbook solution is designed to be part of this infrastructure, and to help you keep track of, understand, and better utilize your data as it circulates. For each stage listed above, the Data Cookbook has a role to play.

And this circulation of data occurs inside an ongoing conversation about the importance, meaning, usage, storage, and provenance of your data; that is to say, inside the data governance or data intelligence framework. We will return to this topic later, when we will expound further on the idea of data literacy as a data governance or data intelligence operation.  We hope that you enjoyed this post.

The Data Cookbook can assist an organization in its data governance, data intelligence, data stewardship and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.

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Aaron Walker
About the Author

Aaron joined IData in 2014 after over 20 years in higher education, including more than 15 years providing analytics and decision support services. Aaron’s role at IData includes establishing data governance, training data stewards, and improving business intelligence solutions.

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