Dx in Practice: Triggers, Impacts, and Outcomes

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The "Dx in Practice" series of articles will look at the trends and triggers, the organizational/behavioral impacts, and the outcomes, both intended and unexpected, emerging from digital transformation (Dx) in higher education. Topics include Dx and instruction, strategic planning and organizational models, IT commodification, data rights, and third-party products.

Digital Transformation with web icons in futuristic theme for, ai, technology, communication, data, iot, automation and networking.
Credit: buffaloboy / Shutterstock.com © 2019

Progress, far from consisting in change, depends on retentiveness. When change is absolute there remains no being to improve and no direction is set for possible improvement: and when experience is not retained . . . infancy is perpetual. Those who cannot remember the past are condemned to repeat it.

—George Santayana
The Life of Reason; or, The Phases of Human Progress (1905)

The message of this often-cited quote from Santayana—that real progress is rooted in the understanding and retention of prior experiences—is worth remembering at a time when higher education is enmeshed in discussions about digital transformation (Dx) and its impacts on the day-to-day IT work that brings value to our colleges and universities.

What Is Dx?

Anchoring the meaning of Dx can be difficult because the concept is subject to interpretation. Does Dx imply a new strategic approach that supplants existing frameworks? Or is Dx more akin to data processing and will thus turn out to be a temporary placeholder for a set of ideas that will be absorbed into our IT lexicon?

Various thought leaders have been exploring the concept of Dx. For example, the EDUCAUSE Learning Initiative (ELI) brief 7 Things You Should Know About Digital Transformation states, "The term 'digital transformation' (Dx) encapsulates the seismic cultural, workforce, and technological shift under way as the diverse digital landscape influences—and changes—almost everything we do." The 2018 EDUCAUSE Task Force on Digital Transformation uses Dx to describe the strategic and organizational impacts for higher education: "Dx focuses on the intersection between technology and institutional strategies, and then shapes digital architecture, applications, campus organizations, workforce, and staffing to achieve strategic goals."1

Meanwhile, Gartner defines the closely related term digitalization as "the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business." Gartner finds parallels in the term digital business transformation, defined as "the process of exploiting digital technologies and supporting capabilities to create a robust new digital business model."2

All of these definitions convey a spectrum of impacts and consequences tied closely to the journey that a business takes from analog, legacy processes to digital, more modern approaches. But what does this mean for those of us in higher education? Are we looking for a new digital business, such as online courses, or are we seeking new digital models to improve our current "business," that of teaching, learning, and research? The answer is likely both.

What is significantly different about Dx is that the processes overtly leverage the collection and use of data. In much the same way that the assembly line transformed the industrial age through automation and mechanization, the data pipeline is transforming the information age through the generation of, and interaction with, knowledge via artificial intelligence (AI), machine learning, and other processes. Dx is redefining key aspects of the human experience, including how we interact, learn, and make decisions.

In Dx, users' transactions inform and reinforce the systems, and the updated systems in turn influence all users. For example, when you are driving down the highway and Waze asks you to confirm that a traffic delay is caused by an accident, your input contributes to a complex information exchange between you and the Waze system and other drivers. Your feedback immediately helps the drivers behind you, validates the Waze reporting data, and affirms your own reliability as a member of the Waze community. This techno-social exchange also helps to create personal affinity. Instead of merely looking at the next car's driver and sharing a frustrated shrug, you are now positively influencing the drivers behind you. By turning a microaggression into a microtransaction, Waze improves its business value and increases its consumers' confidence that the system will accurately report traffic events in the future.

Are there dangers for users? Will people become over-reliant on these Dx technologies and lose the human capacity, need, and/or interest to perform tasks themselves?3 New programs—for example, the Stanford Institute for Human-Centered Artificial Intelligence and the United Kingdom's Centre for Data Ethics and Innovation (CDEI)—bring together thought leaders from diverse disciplines to trace the relationships between ethics and policymaking, including the human impact of AI. Efforts like these will ensure we do not get to the point where only machines, not humans, are learning.


Both impetus and outcome, Dx has evolved due to an economic shift in the value of data. In higher education, that shift has reshaped build-versus-buy decisions, changed the way product features and interface modifications are rolled out, revolutionized traditional pricing structures, and forever altered the role of IT service providers. Dx seeks to personalize the digital experience at scale, cultivate audience loyalty, and monetize customer behavior patterns.

In our push toward the Dx future, we have an opportunity to reconsider the consequences for privacy and security, social interaction, ethics, machine learning, and a host of other critical issues. At this important juncture, we are introducing "Dx in Practice," a series of articles that will allow us to consider the trends and triggers, the organizational/behavioral impacts, and the outcomes, both intended and unexpected, of Dx in higher education. In the coming months, this series will bring a number of perspectives—both academic and administrative—to the potential benefits and possible drawbacks of Dx by exploring use cases, edge cases, and consequences of change.

If you have examples of serendipitous or unintended uses of an emerging technology, please share them with us and contribute to the discussion. By reflecting on shared experiences, we can better understand the effects of Dx rippling across the private and public sectors. As Santayana noted, true progress can be made only through retentive attention to the past.


  1. 7 Things You Should Know About Digital Transformation (Louisville, CO: EDUCAUSE Learning Initiative, 2018); Report from the 2018 EDUCAUSE Task Force on Digital Transformation (Louisville, CO: EDUCAUSE, November 2018), p. 2.
  2. Digitalization and Digital Business Transformation, "Gartner IT Glossary," Gartner (website), accessed May 13, 2019.
  3. If this sounds alarmist, look no further than the recent examples of Tesla "drivers" asleep behind the wheel: Alex Davies, "A Sleeping Tesla Driver Highlights Autopilot's Biggest Flaw," WIRED, December 3, 2018; Abigail Shalawylo, "Tesla Driver Appears to Be Sleeping in Car As It Travels Down California Highway," ABC News, March 6, 2019.

Jim Phillips is Director of Learning Technologies, Information Technology Services, at the University of California, Santa Cruz.

Jim Williamson is Director of Educational Technology Systems and Administration, Office of Information Technology, at the University of California, Los Angeles.

© 2019 Jim Phillips and Jim Williamson. The text of this article is licensed under the Creative Commons BY-NC-ND 4.0 International License.