What is Data Citizenship?

While there is a lot of discussion around Data Citizenship, what exactly is that and how does it work?  2021 HEDW Conference Chair, Dale Amburgey, from Embry-Riddle Aeronautical University, takes a moment to discuss Data Citizenship, Part 2


Data Citizenship in the Modern Age, Part 2

Last month, we discussed data citizenship from the data consumer perspective.  This month, we focus on data citizenship from the perspective of those producing data or output.  Think of data producers as individuals or entities that utilize data to generate reports, dashboards, or statistics to influence an audience.  Data producers should approach their work with an unspoken principle of reducing as many barriers to interpretation as possible so that the audience does not lose the message due to frustration or confusion.  The following are a sampling of the areas where the efforts of data producers can pay dividends by making communication easier to understand for the audience.

Understand data stewardship

I used to refer to data ownership quite frequently, but some of the more recent literature has shifted from using ownership to stewardship, and I feel that is a better description.  We often encounter situations where we use data with which we are not familiar.  In these instances, consulting with the data steward responsible for the data can provide valuable insight into any idiosyncrasies that may be present.  Further, the data steward may provide guidance on the appropriateness of the data points to use for analysis and steer you clear of any potential misinterpretation.

Data stewards have a more intimate knowledge of data under their purview and can offer you valuable insight when crafting your analysis or report.  Their experiences may prevent you from misrepresenting a data point or may expose you to other items that may be more impactful for your analysis.  Take advantage of their experiences and use the knowledge gleaned to help inform others.

Provide accurate definitions

Providing accurate definitions is critical to the communication process.  From personal experience working at a multi-campus institution, defining accurate terminology can sound simple in theory but may prove to be extremely complex when put into practice.  However, overcoming these challenges are imperative for data producers.  Accurate definitions help provide meaning, understanding, and context so that the audience is not left to make their own determinations.  Leaving an opportunity for individual interpretation requires an individual to draw upon their professional and personal experiences to decide.  Often, there may be wide gaps in those experiences based on their background.

Anticipate possible misinterpretations

A component of the review process for a dashboard or report should include a review for possible misinterpretations.  As mentioned earlier, the audience of your output is most likely going to possess a variety of backgrounds and experiences.  Understanding the composition of your audience may assist you with anticipating the variety of ways in which your presentation may be interpreted.  Once these possibilities are considered, then you will be able to take preemptive action to reduce confusion.  It may mean providing more context through notes or callouts, or it may be adjusting the design of a visualization.  The more effort you place on correcting misconceptions, the more effort your audience can place on understanding your data.

Be wary of omission of facts

Sometimes what we leave out may have larger consequences than what we leave in.  We are often faced with having too much data to share in a report or dashboard.  From a transparency perspective, it may seem prudent to share all the data you have utilized.  However, there are some valid reasons as to why you may not initially share all data points.  You may need supporting evidence for future reports, or some of the data points may not be as convincing as others.  When selecting the data to include in an analysis, think not only about what is being left in, but think about what you are not including and evaluate how omitting those data may impact interpretation.  If leaving out some data points can impact the meaning of the narrative you are sharing, then that is a warning sign against omission.

Regulatory implications

It seems that knowledge workers live in a world or regulatory acronyms.  Whether it is FERPA, HIPAA, or GDPR, there are numerous regulatory policies that our data may fall under.  As a data producer, it is important to understand fully any regulatory and compliance implications that may be associated with data utilization.  This is also a time where understanding data stewardship may be beneficial because you can take advantage of the expertise of your stewards to explain the nuances associated with regulatory agencies.

The points covered in our discussion on data citizenship highlight the responsibilities we have as data producers and data consumers.  As data democratization continues to increase our access to data and the institutional silos of yesterday continue to crumble, we must proactively promote the responsible  interpretation and dissemination of our institutional data.  All our efforts will have a positive impact on the narratives we share.


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