2020 Strategic Technologies Glossary


Analytics and Artificial Intelligence

  • Analytics hubs

    Analytics hubs are tools to curate and simplify access to analytics from across multiple heterogeneous environments.

  • Augmented analytics and data discovery

    Augmented analytics and augmented data discovery is a form of advanced data discovery that automates data insight using machine learning and natural language generation. It automates data preparation and helps end users more easily share data.

  • Big data frameworks (e.g., Hadoop, Spark)

    Massively scalable database architectures allow for the distributed processing of very large data sets by dividing the work across computer clusters. This technology allows for high-performance and highly scalable data management that can handle massive data.

  • Career planning systems

    These technologies allow students to engage in career planning, including previewing job-market data, salary data, and the financial impact of choosing a degree that prepares them for that career.

  • Conversational analytics

    Conversational analytics is the process of applying AI to analyze, improve, and personalize user interactions with chatbots and other conversational interfaces.

  • Course demand forecasting

    These technologies use student program data to forecast course demand. It can be used for course scheduling and course forecasting. These solutions allow institutions to ensure they are offering the curriculum needed in the right sequence to support student success and completion.

  • Enterprise data lake

    An enterprise data lake is a pool or repository of raw structured and unstructured data, allowing analysis across various data sources and types of data.

  • Institutional support for machine learning

    Institutions generate vast stores of data. Through machine learning colleges and universities may be able to identify patterns in the data that would be otherwise undetectable and make decisions that support institutional goals such as student success.

  • Master data management (MDM) tools

    Master data management (MDM) tools are technologies that support enterprise data quality and governance (data uniformity, accuracy, stewardship, semantic consistency, and accountability).

  • Predictive analytics for institutional performance

    Predictive analytics for institutional performance is the application of analytics for improving institutional services and business practices. It uses modeling to determine what will happen based on historical and transactional data.

  • Predictive analytics for student success (institutional level)

    Predictive analytics for student success is the statistical analysis of massive amounts of data to create models that establish risk factors relating to student persistence, retention, and completion. These models enable proactive institutional support of student success.

  • Predictive learning analytics (course level)

    Predictive learning analytics is the practice of gathering and analyzing a variety of learner data that results in predictions about the likelihood of future student outcomes in the course. These predictions can be used by students and instructors.

  • Text/content analytics

    Text/content analytics is a set of techniques and processes that analyze unstructured, text-based information to discern themes and patterns that can be used as data for analysis and decision-making.

  • Visual data discovery

    Visual data discovery is the process of using visualizations and iterative visual exploration of data to improve time to insight. Visual data discovery improves business decision-making by enabling the exploration of data through interaction with charts and images.


  • Blended data center (on premises and cloud based)

    As institutions move services to the cloud, they usually move into a blended environment where they continue to maintain an on-premises data center while also managing a set of services that may run the gamut from software as a service to infrastructure as a service. While cloud-based solutions offer advantages related to agility, performance, and scalability, the blended environment requires a shift in strategy to one that encompasses both environments.

  • Containerization

    Containerization is a lightweight alternative to full machine virtualization that involves encapsulating an application in a container with its own operating environment.

  • Ethernet fabrics

    Ethernet fabrics are a data center network protocol that enables connections between multiple physical and virtual devices as part of an integrated network system. The goal is to increase flexibility and bandwidth and provide a scalable, low-latency networking approach.

  • Hyperconvergence

    Hyperconvergence is an IT framework that combines storage, computing, and networking into a single system in an effort to reduce data center complexity and increase scalability. Multiple nodes can be clustered together to create pools of shared compute and storage resources, designed for convenient consumption.

  • Private-cloud computing

    Private-cloud computing refers to cloud infrastructure operating for a single institution and closed to other use. Some institutions have used virtualization technologies to run parts of their environments on private-cloud virtualized platforms.

  • Robotic process automation

    According to Gartner, "robotic process automation (RPA), sometimes called smart automation or intelligent automation, refers to advanced technologies that can be programmed to perform a series of tasks that previously required human intervention."

  • Smart campus strategy

    A smart campus strategy uses devices and applications to create new experiences or services and facilitate operational efficiency. IoT, mobile, Wi-Fi, and artificial intelligence are core smart campus technologies.

  • Software-defined networks

    Software-defined networks (SDN) are an approach to designing, building, and operating networks that allow system administrators and network engineers to respond quickly to ever-changing network requirements and to optimize resources. SDNs may do for networks what virtualization has done for servers, allowing administrators to manage the network services in a simpler way and enabling network end users and applications to configure the network according to their needs.

  • Support for 5G

    Fifth-generation cellular wireless provides the ability to connect many more devices to cellular networks at greater speeds and with faster responsiveness.

  • VDI environments to enhance online and mobile learning.

    Virtual desktop infrastructure (VDI) is virtualization technology that hosts a desktop operating system on a centralized server in a data center. VDI is a variation on the client-server computing model, sometimes referred to as server-based computing.

  • Wi-Fi 6 (802.11 ax, AX Wi-Fi)

    New Wi-Fi standard can better support growing numbers of Wi-Fi-enabled devices.


  • Development tools to support multiple key platforms

    Developers must program applications to run on a variety of mobile devices that use different operating systems. Design strategies include responsive web design, which provides an optimal experience across a wide range of devices. Development tools exist that aid cross-platform development.

  • Mobile app development

    Mobile app development is the organizational capability for the development of mobile applications. Organizations must make decisions about native apps for specific devices and mobile web development strategies. Issues of accessibility, security, data protection, and responsive web design also must be addressed when considering mobile app development.

  • Mobile apps for enterprise applications

    Mobile apps for enterprise applications refers to web-based applications that run on mobile devices and are designed to integrate with all aspects of an organization's businesses and processes. These apps make it possible to access enterprise-wide resources (such as course catalogs, student information systems, and human resource systems) and conduct enterprise transactions from mobile devices.

  • Mobile device management

    Mobile device management is the approach an institution takes for the policies, support, and procedures related to the variety of cell phones, tablets, and laptops on campus. Mobile device management involves a balance between the security of institutional data and user convenience and productivity. Some institutions use third-party products and services to manage mobile devices. Considerations include data-security issues, support for personally owned equipment, and application management.

Research and Scholarship

  • Active network measurement tools (e.g., perfSONAR)

    Active network measurement tools enable end-to-end coordination of network infrastructure across multiple administrative domains to support cross-institutional scientific collaborations.

  • Author identifiers (e.g., ORCiD, ResearcherID, Scopus Author ID)

    These tools reliably associate researchers with their publications and other research outputs, regardless of shared names, name changes, or different variations of the same name.

  • Cloud-based HPC

    High-performance computing (HPC) requires substantial processing, high-speed connections, and parallel input/output. When HPC is provided by cloud vendors, additional characteristics typical of cloud are inherited the ability to scale up and down quickly on demand in a pay-as-you-go environment.

  • Institutional repositories for research data

    The management and curation of research data‚ including providing continued access to this data‚ is an important role for many institutions. In addition, publisher or grant-agency guidelines may require data to be in a repository. Institutional repositories help enable local, ongoing management and access, as well as serve as a place to host and share data where appropriate discipline-specific or national repositories are not available.

  • Science DMZ

    Science DMZ provides a network-architecture approach that is optimized for high-performance scientific applications and the transfer of large research data sets over high-speed wide-area networks. It supports big-data movement by improving security, cost-effectiveness, and the nimble handling of large (mostly) scientific data sets. Science DMZ also addresses issues of systematic performance monitoring and file transfer and serves to simplify the use of software-defined networking (SDN) over wide-area network paths.

  • Scientific workflow systems (e.g., Toil, Pegasus, NextFlow)

    A scientific workflow system can facilitate computationally intensive collaborations among scientists at multiple institutions. It is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or workflow, in a scientific application.

  • Simulations

    Games and simulations provide students with realistic learning experiences that mimic real-life situations for learners to develop and apply skills and experience the outcomes of how those skills are implemented.

  • Support for the integration of research computing with infrastructure (e.g., JupyterHub)

    Certain tools can help with the orchestration and scaling of research computing services, particularly with cloud and cloud-like variants (e.g., a bunch of servers in a data center), including defining patterns of compute, data, and networking resources.

  • Tools to support cross-institutional and international research data-sharing

    A core mission of higher education is research, and researchers are increasingly working with colleagues from other institutions and internationally. Understanding the issues of sharing research data with these colleagues is paramount for IT to provide the tools and support that enable this sharing. Tools in this space may address issues ranging from metadata to data access, usage rights, and file format interoperability.

  • XR (including virtual/augmented/mixed reality) for research

    Augmented reality (AR) superimposes graphics, video, text, or other content over a user's field of vision, layering digital content onto the real world. Virtual reality (VR) creates an immersive, 3D environment with which users can interact. These technologies can be used in clinical research, to conduct laboratory experiments in lieu of field research and more.

Security and Privacy

  • Applications of analytics to identity and access management

    Analytics can be used to analyze identity and access data, employing advanced analytics tools to perform functions such as data mining, statistical analysis, data visualization, and predictive analytics. These are not generic data analysis tools. Instead, they draw on identity and access management (IAM) policies, rules, and risk indicators to provide information of immediate value to IAM administrators and analysts, compliance officers, and incident responders.

  • Cloud access security broker

    A cloud access security broker (CASB) is a service that applies institutional security policies, such as authentication and authorization rules, to cloud-based resources. A CASB extends institutional information security policies and practices to the cloud-based services that the institution uses.

  • Cloud-based identity services (e.g., Duo, OneLogin, PortalGuard)

    Cloud-based identity services manage identification and authentication processes to IT systems or data. Authentication services ensure that only authorized individuals (or other systems) are permitted to access IT systems and data.

  • Cloud-based security services (e.g., Duo, Qualys ThreatPROTECT, cloud-based email security solutions)

    These services are usually used in conjunction with on-premises services and tools to enhance an institution's information security posture.

  • Container security

    Containers provide a standard way to package your application's code, configurations, and dependencies into a single object. Containers share an operating system installed on the server and run as resource-isolated processes, ensuring quick, reliable, and consistent deployments, regardless of environment. Common implementations of containers are Docker and Kubernetes. Containers are being adopted to run "microservices."

  • DDoS prevention products and services

    A distributed denial of service (DDoS) attack uses multiple systems to target a single IT system, swamping that system and preventing authorized users from accessing it. Various products and services can be used to protect institutions from DDoS attacks.

  • DevOps/DevSecOps

    DevOps is the culture and practice that unifies software development and software operation into shorter development cycles, enabling organizations to deliver services and applications at a faster pace than organizations using traditional software development practices and processes. DevSecOps is the philosophy and practice of incorporating security into each step of the software development process to deliver more-secure services and products; it requires close collaboration between software engineers and security teams.

  • DNS security

    Domain name systems/servers (DNS) translate textual domain names (such as educause.edu) to IP addresses. DNS security describes a suite of security specifications—such as DNS security extensions (DNSSEC), OpenDNS, or DNS-RPZ—for ensuring the integrity and authenticity of the institutional DNS.

  • E-signature technologies (e.g., DocuSign, Adobe Sign, and SignNow)

    These technologies allow users to electronically sign documents to authenticate the identity of the signer.

  • End-to-end communications encryption

    This approach encrypts digital communications from the sender to receiver as it travels across communications networks.

  • Enterprise GRC systems

    This refers to integrated IT applications that typically offer "modules" that help automate institutional governance, risk, and compliance (GRC) processes and reporting, such as managing the policy-development process, tracking legal requirements, monitoring and ensuring that compliance obligations are met, automating risk-assessment exercises and tracking mitigation activities, and automating incident or issue tracking.

  • Identity as a Service (IDaaS)

    Identity as a Service (IDaaS) is a cloud-based service that is created, hosted, and managed by a third-party provider. IDaaS might include cloud-based authentication infrastructure or access controls, single sign-on (SSO), adaptive multifactor authentication (MFA), and other identity and access management (IAM) functions. In addition to providing improved cybersecurity, IDaaS offers more efficiency because identity provisioning, password resets, software upgrades, and other administrative work can be handled by the service provider.

  • Privacy-enhancing technologies (e.g., limited-disclosure technologies, anonymous credentials)

    Privacy-enhancing technologies and tools protect a user's personally identifiable information during online transactions.

  • Security analytics

    Security analytics uses analytics, adaptive learning, and other tools to detect, anticipate, and respond to incidents and compliance issues.

  • Security orchestration, automation, and response (SOAR)

    Gartner describes SOAR as the collection of disparate technologies that enable businesses to gather data and security alerts from different sources. SOAR combines three previously different technology sectors—security orchestration and automation, threat intelligence, and incident response.

  • Social login (authentication)

    Social login is single sign-on for end users. Using existing login information from a social network provider such as Facebook, Twitter, or Google, the user can sign into a third-party website instead of creating a new account specifically for that website.

  • Threat intelligence technologies

    These services or tools generate and share cyber threat intelligence information with other tools and services (and institutions).


  • Cryptocurrency malware

    Cryptocurrencies (such as Bitcoin) are digital currencies that use encryption technologies to control the creation and transfer of the units of currency. Cryptocurrency transactions are verified through a process known as "mining," which requires significant computing resources. In some instances, an end user’s computer or device can be used for mining without the user's permission. This unauthorized use of resources for cryptocurrency mining can be an information security concern.

  • Digital credentials

    Blockchain is a public, distributed ledger of transactions maintained by a peer-to-peer network. Its most notable current use is to support value exchange with Bitcoin, but it has also been considered in the context of credentialing.

  • Institutional support for accessibility technologies

    A wide range of accessibility technologies are available for students, faculty, and staff with physical, cognitive, or other kinds of disabilities. Institutional support for such technologies may focus on straightforward educational applications (e.g., language learning) or otherwise improving access.

  • Institutional support for speech recognition

    Speech-recognition systems translate human speech into text or commands. Institutional support for such technologies may focus on straightforward educational applications (e.g., language learning) or improving accessibility for students who are blind or physically disabled or have learning disabilities.

  • Support for use of personal cloud services

    Faculty, staff, and students may use personal cloud services such as Apple's iCloud or Google Drive instead of or in addition to institutionally supported storage services. Institutional support includes guidelines, education, and policies to ensure adequate information security.

Student Success

  • CRM covering full student life cycle

    CRM systems that extend tracking and engagement with students across the student life cycle, from the recruitment phase through the student phase, on to career selection, and extending into the alumni phase. A full life-cycle CRM system allows colleges and universities to engage constituents consistently and effectively at every stage, ensuring a connected experience.

  • Facial recognition

    A facial-recognition system is a technology capable of identifying or verifying a person in a digital image. Facial recognition systems on campus can improve campus security, help with attendance tracking, and provide feedback about audience response during lectures.

  • Inclusive access for course materials

    As the cost of course materials continues to rise, many students are treating textbooks as optional resources. Colleges and universities are turning to inclusive access programs that include textbooks in the cost of tuition as a way to ensure that students receive the resources necessary to successfully complete their courses. In this way, course materials can be bought for less, schools can create a revenue stream, and students can get the savings they want.

  • Integrated student success planning and advising systems

    Student success planning systems aggregate a broad range of academic, learning, financial, and other data, enabling personnel throughout the institution to collaborate in support of retention and completion.

  • Machine learning for student recruitment/success

    Machine learning is the practice of developing computer systems that can “learn” about something without having explicitly been given that information. In higher education, machine learning allows colleges and universities to explore large quantities of data to surface patterns that can be used to draw inferences and make predictions that lead to improved student experience, institutional outcomes, and student success.

  • Nudge technologies

    According to Gartner, "nudge tech is a collection of technologies that work together to achieve timely personalized interaction with students, staff and faculty, such as a just-in-time text (SMS) reminder for class. Technologies used include, but are not limited to, chatbot, texting, algorithmic analytics, machine learning and conversational AI."

  • Technologies for improving analysis of student data

    These technologies enable immediate access to and rapid analysis of large, complex data sets, making it possible to discern trends in student engagement, in the types of difficulties students are encountering, and in the likelihood of success in attaining credentials across the student body.

  • Technologies for offering self-service resources that reduce advisor workloads

    These platforms make tools such as online registration, scheduling, and academic planning available directly to students, enabling those with professional responsibilities for guiding students to reserve in-person appointments for higher-level interactions and counseling on individual issues.

  • Technologies for planning and mapping student educational plans

    Educational planning tools allow students and advisors to work together to build customized pathways through the curriculum that are appropriate for each individual's interests and goals. In addition, these technologies offer a reliable way to chart and track progress toward a degree or credential. They also support institutions in the development of schedules that match demand.

  • Technologies that support mental health

    Digital tools that support the mental health of students include platforms in the areas of self-management apps, skills training for coping and thinking skills, online access to screening and counseling, passive symptom tracking, and data collection. Such tools can help researchers better understand mental health and develop better interventions.

Teaching and Learning

  • Adaptive learning

    Adaptive learning is one dimension of personalized learning, which aims to provide efficient, effective, and customized learning paths to engage individual learners. Adaptive learning technology dynamically adjusts to student interactions and performance levels, delivering content in an appropriate sequence that individual learners need in order to make progress.

  • Artificial intelligence in instruction

    AI can be deployed as an explicit resource in the curriculum in three ways: first, as an instructional resource that can offer coaching and tutoring; second, as a domain for curricular projects; and third, as an automation tool, used to conduct some of the administrative tasks associated with teaching and learning.

  • Courseware

    Courseware is any digital curricular resource that contains a blend of content, study aids, and instructional expertise. Courseware is typically housed and delivered by a digital platform or application. Courseware's content is a direct descendent of the textbook, and study aids might include tools such as highlighting, commenting, and ways to interact with learners and instructors.

  • Digital assessment

    Feedback via assessment is critical to the learning process. Digital technologies provide a wide variety of assessment tools, such as summative and formative assessments and peer review. These tools are available in the LMS or third-party tools integrated with the LMS, where they can be aggregated on dashboards to provide comprehensive views for both learner and instructor.

  • Digital microcredentials (including badging)

    A digital microcredential is a like a mini-degree or certification that conveys information about a competency or skill related to a specific topic area. Digital microcredentials or digital badges can be issued by anyone and typically contain detailed metadata that communicates what the learner has learned or is able to do as a result of earning the credential.

  • Games and gamification

    Gamification or game-based learning refers to the use of a pedagogical approach that utilizes gaming designs and principles but that is implemented within a nongame context, such as an instructional setting. Gamified learning environments are meant to support learner engagement and motivation, problem solving, critical thinking, and decision-making skills development.

  • Incorporation of mobile devices in teaching and learning

    Mobile devices integrated into courses can be used for course assignments, field work, collaboration, and other activities. Such integration includes ensuring that course content functions well on mobile devices, as well as leveraging the unique capabilities of mobile devices for learning.

  • Next-generation digital learning environment

    Next-generation learning environments replace conventional learning tools with a digital environment based on open standards that can be highly customized to support key learning functions such as analytics, collaboration, and universal design. Such environments are characterized by interoperability, personalization, collaboration, accessibility, and analytics.

  • Open educational resources

    Open educational resources (OER) are freely accessible, openly licensed documents and media that may be useful for teaching, learning, assessing, and research. OER are used in various learning settings including online, face-to-face, and blended, as well as structured learning environments such as college courses and self-paced, student-driven learning.

  • Open standards for educational and learning technologies

    Open standards are the linchpin for next-generation digital learning environments. These standards enable applications to integrate quickly and inexpensively, and they also make possible the aggregation of learning data across a variety of tools and platforms. Open standards should be a component of the digital architecture planning and required in procurement practices.

  • Robotics

    Robotics uses smart machines to complement learning by enabling students to develop and enhance relevant skill sets.

  • Supporting data-intensive and computationally intensive instruction (e.g., data science, computational sciences)

    These practices blend significant cloud-based and local computational resources to enable ambitious instructional projects. Such technologies range from applications (such as statistical, engineering, and mathematical) to research systems, which consist of computational clusters and large-scale data capacities.

  • Uses of the Internet of Things for teaching and learning

    The Internet of Things (IoT) refers to the network of small, often everyday objects equipped with both computing and sensing capabilities, as well as the capacity to send and receive data via the Internet. The two dimensions of the curricular use of the IoT act as a way of providing learning data about student activities and as a source of student projects in disciplines such as computer science and engineering. The IoT may also be a domain of student extracurricular activity through makerspaces and related activities.

  • XR (including virtual/augmented/mixed reality) for teaching and learning

    Augmented reality (AR) superimposes graphics, video, text, or other content over a user's field of vision, layering digital content onto the real world. Virtual reality (VR) creates an immersive, 3D environment with which users can interact. These technologies can be used as an experience consumed by the learner or as a programming exercise in which learners create AR and VR experiences.

Tools and Operations

  • Application performance monitoring tools

    Application performance monitoring tools track the performance of applications in relation to end users' experiences and to internal metrics (for example, for load and capacity) that may be leading indicators of future performance issues. The goal of these tools is to automate tracking and improve the reliability of application performance.

  • Cloud monitoring platform to track distributed infrastructure apps, tools, and services (e.g., Datadog)

    The proliferation of cloud applications and services is challenging to support because it can result in a mix of distributed and centralized systems and tools, some under IT's control and some not. Cloud monitoring platforms allow institutions to track the expanding set of cloud resources.

  • Data center capacity planning and management tools

    Data center capacity planning allows IT to meet the institution's evolving needs for data center resources such as storage, power load, and cooling capacity. Some vendors provide tools for capacity planning. IT service management frameworks such as ITIL describe subprocesses for capacity management that include business capacity management, service capacity management, and component capacity management.

  • Enterprise data catalog

    An enterprise data catalog is a metadata management tool designed to help organizations find and manage large amounts of data—including tables, files, and databases—stored in ERP, human resources, finance, and e-commerce systems, as well as in other sources such as social media feeds.

  • Institutional support for public-cloud storage (e.g., Box)

    Public-cloud storage options provide easy access, sharing, and backup of files and data. Institutions are moving to such options to provide cloud storage and collaboration services that work with the university's identity management system, integrate with other services, and provide contractual assurances of privacy, security, and uptime.

  • Integration platform as a service

    A typical institutional enterprise environment is made up of a complex mix of applications and architectures, some in the cloud and some on-premises, that need to communicate with each other and share data appropriately. Instead of handling data integration in-house, some institutions are turning to integration platform as a service (iPaaS), which is a suite of generally cloud-based services that support and enable integration among disparate systems.

  • IT accessibility assessment tools

    IT accessibility assessment tools allow institutions to test the designs of their web pages and other online materials to ensure they are usable by individuals with disabilities.

  • Life-cycle contract management

    Life-cycle contract management refers to a formal process or system for managing contracts from the time of negotiation through compliance to renewal. Life-cycle contract management systems have the potential to create efficiencies and lead to cost savings. They also can increase compliance with regulations and other requirements.

  • Location-based computing

    Location-based computing uses location data to deliver online content to users based on their physical location, using various technologies including GPS, cell phone infrastructure, and wireless access points.

  • Service-level reporting tools

    Service-level reporting tools allow institutions to track and report on IT service delivery and management. They facilitate tasks and workflows associated with delivering IT services and track how well the delivery of services conforms to service-level commitments.

  • Software license tracking technology

    Software license tracking technology (sometimes called software asset management tools) allows the organization to keep track of details about the software licenses in effect at the institution in order to maintain compliance, control spending, and manage use.

  • Tools to support cross-institutional and international collaborations

    Collaborating with colleagues beyond the institution is getting easier through a variety of options that include enterprise-level collaboration tools and free web-based tools. Enterprise tools offer more assurance of privacy and security through the institution's identity management system.

  • Uses of APIs

    An API defines how a system interacts with other systems and how data can be shared and manipulated across programs. A good set of APIs is like building blocks that allow developers to more easily use data and technologies from various programs. APIs are used in many ways in higher education—for example, to pull data from the student information system into the learning management system, to integrate cloud-based with on-premises services, as an approach to security, and to access web-based resources.