data ethics

Data Ethics and the Stories We Tell With Data

By Teri Hinds

I recently joined First San Francisco Partners, transitioning from nearly two decades of data-centered work in the public sector, specifically in higher education. As an admitted and unashamed data geek, it’s been exhilarating and affirming to learn there is an entire industry dedicated to recognizing data as an organizational asset — and assets deserve, even demand, intentional care and maintenance.

Like many of my fellow data geeks, though, data is not really the thing that excites me when it comes down to it. It’s what we do with data and how we apply it to make sense of the world around us that makes my eyes light up.  

I’m also a fiber artist. I knit and spin and have been known to dabble in weaving. So, when I think about how we use data, I’m reminded of tapestries. When I look at data, I’m trying to draw out the pattern, the picture or story the data tells.  

Another way to think of this is to create the picture of what’s happening by finding the strands and threads that weave the whole together. A single data element, like a single fiber strand, may be beautiful, but it’s rarely complex. Only by combining it with others and teasing out the patterns does the larger picture in all its glory become clear. 

Context is Everything

The attention and care a weaver invests in selecting strands in their tapestry are akin to the care and maintenance necessary to support data contextualization and storytelling. If the threads are worn from neglect or yarn is tangled, essential threads may snap and leave holes, possibly leaving the picture unrecognizable.  

Likewise, if data isn’t ethically managed and governed with integrity, the story it tells will be distorted, incomplete and potentially misleading. Unfortunately, the investment in data management is too often viewed as a nice-to-have rather than essential to good operational practice, leaving us with muddy impressions and unclear direction. 

Higher Ed’s “Data Stewards”

My former professional home was a field in higher education known as institutional research. The mission of the Association for Institution Research (AIR), the national professional membership association for institutional researchers, is to “empower higher education professionals at all levels to utilize data, analytics, information and evidence to make decisions and take actions that benefit students and institutions and improve higher education.”  

Institutional researchers are the people within individual colleges and universities typically charged with carrying out that mission. They are interpreters and liaisons, often working across functional areas to build enterprise context for data and information to guide institutional decision-making. Institutional researchers aren’t data stewards, per se, but they work with stewards and subject matter experts across campus in ways very similar to data governance offices in the private sector. 

Manage (and Use) Data With Integrity

Like stories, data never exists in isolation; it is embedded in the context and realities of our world. Using data to tell stories, therefore, requires not just attention to clarity but also to context.  

Integrity, one of our FIRST company values, really speaks to me. Because without helping our clients understand data integrity or what it means to have an ethical foundation, their data can change from being a business asset or decision-making tool to something they can’t rely on. 

Conversations around data ethics typically focus on maintaining individual privacy rights or algorithms used for machine learning (ML) and artificial intelligence (AI). Privacy rights focus more specifically on avoiding the risk of fines or other sanctions embedded in regulations and legislation, such as the European Union General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).  

Algorithms, especially those that use past data to predict future behavior, are inherently limited by structural discrimination baked into every society and, therefore, destined to perpetuate them rather than alleviate them.  

As our world becomes even more internet-enabled with more details from our lives shared in public and private-sector data stores to drive ML and AI, intentional and ethically focused data governance is the only way we can escape the echo chambers of societies past. 

Data Management’s Ethical Foundation

In addition to its mission, the AIR Board approved a new Statement of Ethical Principles in 2019, specifically to address the emerging topics of data analytics, big data and vendor relationships. The AIR Statement of Ethical Principles articulates competencies and responsibilities that can align nicely with the Data Management Knowledge Areas* articulated in the Data Management Book of Knowledge (DMBoK).  

Specifically, AIR’s ethics statement calls on institutional researchers to act as responsible data stewards (data governance), provide accurate (data quality) and contextualized information (metadata), and protect privacy and confidentiality (data security). The inclusion of so many foundational data management principles in a statement of ethical use signals that rather than data ethics being an add-on, it is, in fact, a starting point and central to good data management. 

Human services fields like education, healthcare and, increasingly, retail and insurance must embody integrity in their work, especially in their use of data. As human services consumers and users require more customized and just-in-time experiences, an ethical foundation for data management is increasingly paramount.  

No matter what industry we work in, let’s be conscious of how the stories we tell with data can be used for good or bad. If we begin with data ethics as a foundation for data management, it can guide our decisions and actions to ensure data an invaluable and trusted organizational asset. 


* DMBoK’s Data Management Knowledge Areas include Data Governance, Data Architecture, Data Modeling & Design, Data Storage & Operations, Data Security, Data Integration & Interoperability, Documents & Content, Reference & Master Data, Data Warehousing & Business Intelligence, Metadata and Data Quality. 

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