---
title: Still More Notes on Practical Data Literacy
description: IData Insights | Still More Notes on Practical Data Literacy
image: https://blog.idatainc.com/hubfs/StockSnap_1942FD49BF_fingerpoint_typesdatarequests_BP.jpg
---

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# Still More Notes on Practical Data Literacy

## Still More Notes on Practical Data Literacy

[February 02, 2022](https://blog.idatainc.com/archive/2022/02) By  [Aaron Walker](https://blog.idatainc.com/author/aaron-walker)  In [data](https://blog.idatainc.com/tag/data), [governance](https://blog.idatainc.com/tag/governance), [intelligence](https://blog.idatainc.com/tag/intelligence), [literacy](https://blog.idatainc.com/tag/literacy)

![StockSnap_1942FD49BF_fingerpoint_typesdatarequests_BP](https://blog.idatainc.com/hs-fs/hubfs/StockSnap_1942FD49BF_fingerpoint_typesdatarequests_BP.jpg?width=261&name=StockSnap_1942FD49BF_fingerpoint_typesdatarequests_BP.jpg)In previous ruminations on this topic, we have suggested that a useful understanding of data literacy would build on existing knowledge of literacy and numeracy. While excellent and nuanced definitions abound, we can describe literacy very simply as the ability to communicate ideas, understand situations, and perform tasks using written texts. Similarly, numeracy involves the ability to express, interpret, and act on information using mathematical language, notation, and concepts.  This blog post continues our discussion on data literacy.  Check out our "[A Practical Understanding of Data Literacy](https://blog.idatainc.com/a-practical-understanding-of-data-literacy)" and "[More Notes on Practical Data Literacy](https://blog.idatainc.com/more-notes-on-practical-data-literacy)" blog posts.

Literacy and numeracy can be measured in levels of proficiency, and the same may be true of data literacy. To demonstrate proficiency at data literacy, a person must be able to understand, interpret, and explain data in a particular (usually business-specific) context. To do that, a person must be knowledgeable of data sources, key enrichments and transformations data is subjected to, analytical methods applied to data, techniques for displaying and visualizing information, as well as strategic and/or tactical use cases for those analytical applications.  We think that it makes sense to think of data literacy as a set of related capabilities that can be divided into levels of achievement or expertise. Some of these specific capabilities include:

- *Recognizing data: knowing where to find relevant, useful data and how to access or acquire it*
- *Processing data: securing and managing data for business or personal usage*
- *Defining data: knowing how to validate, verify, augment, or cleanse data*
- *Interrogating data: performing computational operations on and statistical analysis of data*
- *Communicating data: visualizing raw and processed data across a variety of applications and formats*
- *Evaluating data: assessing, interpreting, and applying data outputs in a business context*

To be a data literate organization, you need a deep enough bench across all these capabilities. But not every person on that bench needs to exhibit the same level or type of proficiency.

---

To assess data literacy proficiency in your organization, you might consider the following.

- *How well do people in your organization know where their data comes from, and how it is collected?* Can they assess the validity of the data's sources, and can they articulate the value of collecting and utilizing the data? If they do not know the use or importance of a particular data element or set of data elements, do they know who is responsible for that data, and how to learn more?
- *Can people in your organization interpret basic statistical operations such as correlations and averages?* Can they explain what, if anything, these operations tell us about our data? What about more involved techniques such as linear or logistical regressions?
- *How many data managers or data stewards can explain the ingestion, manipulation, and output of their systems or processes?* Of this number, how many are able to construct a business case or action plan based on appropriate and pertinent data?
- *When aggregated data is published, do those publications conform to principles of visual and information design?* Are graphs and charts properly labeled, free from clutter, and arranged for ease of understanding? 
- *How fully do your data consumers appreciate and understand the data shared with them?* How successful are they at taking meaningful action based on data?

When we talk with our clients and look at their data utilization, we often see a highly uneven or skewed distribution of data literacy proficiencies. 

Knowledge workers tend to be highly literate, as they send and receive sophisticated written texts frequently. While they may not all have attained the same level of numeracy, they probably have had some training in arithmetic and algebraic methods, they are likely to have had budgetary responsibilities at some point, and they traffic in some subset of your organization's data. Moreover, they tend to be digitally savvy: they use email, web applications, texting and other communications apps.

All of which is to say, many of them surely have the cognitive capacity, the logical reasoning framework, and the problem-solving mindset to do great things with your organization's data. 

---

How then do we move the peak of our distribution and help our organizations avail themselves of improved data literacy? In many cases organizations will see their overall data literacy improve simply by prioritizing data operationally.

*Make data more available across more tools and platforms, and make sure employees know how to use those tools at a sufficient skill level for their role.*

*Document where data comes from, how it is defined (and by whom), and how it is used.*

*Share data widely, and make data analysis a formal piece of decision-making (as well as evaluating the decisions made based on that analysis, and publicizing those evaluations).*

*Hire new employees who demonstrate high levels of data literacy for the role they are hired into - make the existing skill set the floor, rather than the ceiling.*

*At the same time, make training available to existing employees who wish to grow their skills (and reward them for taking advantage of this training).*

How do we take these steps?

Well, one way forward is to invest in organizational data intelligence and to commit to data governance. For data intelligence, you might create an inventory of data assets, a business glossary organized around key data elements and concepts, and a curated catalog of data deliverables, all of which would help interested users learn more about data in context. For data governance, you might develop data quality standards, a system for classifying data so as to better protect confidential and private information, and a framework for data stewards to be identified, trained, and assisted in collaborating. The [Data Cookbook](http://www.datacookbook.com) solution is a tool that provides easy entry into any or all of these activities.

We hope that you enjoyed this post.  Additional data governance and data intelligence resources can be found at [www.datacookbook.com/dg](http://www.datacookbook.com/dg).

The [Data Cookbook](http://www.datacookbook.com/tour/) 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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Photo  Credit: StockSnap\_1942FD49BF\_fingerpoint\_typesdatarequests\_BP #B1210

![Aaron Walker](https://blog.idatainc.com/hubfs/aaron_2.jpg)

##### 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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