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Digital skills defined: A glossary of technical terms for business upskilling

Technology is evolving so quickly, it’s hard to keep up with the new words and tools that come with the changes. It can feel like a foreign language. Thankfully, when it comes to the tech that impacts business the most, we can narrow down the list of terms that you actually “need to know” to keep your head in tech-heavy conversations. 

These definitions aren’t meant to be exhaustive, complete explorations of topics. We’ve created this list of technical terms to help the non-technical gain insight and understanding into the technologies and skills their teams need to prepare for the future of work, or that they may be using already. It’s also a good list for digitally skilled people to explore.

List of terms and definitions 

3-D printing – the process of using digital files to create three dimensional objects, like prototypes of machine parts, medical devices or architectural renderings. Even though it’s called printing, it’s piling on layers of material using plastic, metal, glass or wood as the “ink.”

Advanced RISC Machine (ARM) – microprocessors that are used in common devices like smartphones and tablets, as well as industrial products; they’re known for being inexpensive and having low power requirements. See the definition for “Reduced Instruction Set Computer (RISC).”

Agile – a project management approach that helps organizations develop new products or services in a flexible and interactive way; using shortened development cycles called “sprints”, teams can focus on improving quickly and go to market faster. It originally gained traction in the software development process, replacing the “waterfall” method of development which was characterized by large software updates released at set intervals. But the flexibility of agile project management has lent itself to applications across departments and projects.

Algorithm – instructions or a set of rules designed to perform a task, calculations or other kinds of problem solving; in computer programming, algorithms are used to perform calculations, data processing and automations. An algorithm can be something simple in the analog world, like a cooking recipe. In the world of big data, algorithms do everything from helping social media companies rank search results and organize user feeds to encrypting patient medical data. 

Artificial Intelligence (AI) – the ability of a computer or machine to mimic human thinking, especially to discern patterns from large amounts of data. AI can be used to automate tasks that usually require human reasoning, like fraud detection or creating maintenance schedules for aircraft or cars. AI tools can help people make better decisions, like which go-to-market strategies to use for new products. 

Augmented reality (AR) – the interactive experience of superimposing information or visuals on the real world through the use of graphics, sound, haptic (touch) feedback and even smell to enhance the natural world. This is different from virtual reality (VR) which is an immersive digital experience. See the definition for “virtual reality.” 

Automation – the process of using one or more technologies to complete a series of tasks that would normally be done one-by-one by a human. For example, opening a folder of digital invoices, taking important data – like vendor name, contact information, product, and price – and putting them into a spreadsheet that populates a dashboard showing quarterly spending breakdown. Learn more about how upskilling in automation can transfer to real business impact.

Big data – refers to large data sets; data can be structured – meaning it’s been formatted or organized in some way and unstructured – this can mean not easily searchable, like video, audio or social media. Learn more about how upskilling can help you put big data to work for your organization

Blockchain – a digital ledger that records and confirms data in decentralized blocks that are linked together in chronological order using an encrypted verification process. No one person has control over the data, and data entry is irreversible. It’s most commonly used for shared public ledgers and was made popular by the development of the digital currency, Bitcoin. Learn more about the benefits of blockchain and its applications across different industries

Bot – short for "robot", a computer program that follows a set of instructions to complete a task or series of tasks. Bots can be unattended, meaning that a person can start a bot and let it run without additional steps, or attended, meaning that a human will have to do some steps before the task can be completed. See the definition for “automation” (as the term “bot” is often used to describe the software built to execute an automation).

Cloud – refers to internet servers and all the information and services you can get using the internet; when something is stored “in the cloud” it’s stored on internet servers instead of on your local computer. This can be used for email, backup data, web applications and entire office suites, like Google and Microsoft 365. 

Cloud computing – refers to on-demand computer resources like data storage, software, analytics, networking and computing power using the internet rather than local storage. 

Coding –  the process of writing code, which is a language a computer uses to understand and act on instructions. Since computers are machines that only understand “yes” or “no”, they need instructions to be translated into their language. Code is that translation.  

Cybersecurity – the protection of internet-connected systems, networks and programs from digital attacks or unauthorized access. 

Data acquisition – the system of collecting, storing and distributing information and converting it into values, which are then decoded by a computer.

Data analytics – collecting and using data to answer questions, discover patterns and make recommendations in a way that’s easy to understand with graphics and stories. Data analysis includes three kinds of skills: acquiring, cleaning and combining data from many different sources. Learn more about how upskilling can help you with these data wrangling processes

Data-driven insights – findings from data which may have business value. These findings, and related actions, may need input from people who have relevant experience. For example, marketing performance data will provide the foundation for insights when analyzed by a marketing expert, who would potentially see trends that a finance professional or data science specialist might not discover. Data-driven insight development is often accelerated through the use of data visualization platforms. Learn how upskilling in data analytics can lead to these kinds of insights and drive value across your organization.

Data governance – managing the quality and integrity of data across an organization. It includes determining who has access, when they have access and who is responsible for controlling data. It also refers to managing and protecting an organization’s data. 

Data mining – a way to discover patterns, relationships and exceptions from usable data that’s taken from a larger set of raw data using tools like statistics, database systems and machine learning. 

Data modeling – a representation of data within a system, the relationships among data points in a system and any rules that may govern them. 

Data roadmap – a plan that helps a team or organization plan and organize the way they collect and handle data to meet business objectives. A data roadmap can help identify what data is already being collected and what’s missing. For example, an understanding of what you want to discover with data will influence which data you need to plan to collect. A data roadmap can also include the activities needed to meet business objectives. 

Data science – the study of data and the way it’s recorded, stored, handled, analyzed, shared and evaluated. An overarching goal of this field of study is to gain insights from structured (organized and formatted) and unstructured (not easily searchable, like video, audio or social media) data that can inform decision making. Data scientists often use data management and data visualization tools to help with insight development, and also have experience in programming languages like SQL, R and Python.

Data validation – a process that confirms whether or not data is valid, and that any insights or results from analyzing the data can be trusted. Data validation can include setting rules for types of data and formatting standards. Validating data can be done by software programs or code scripts. 

Data visualization – using charts, graphs or maps to show patterns in data. People can process these visuals more easily than spreadsheets, and these designs can show complex ideas.

Design thinking – a way to design products that focuses users and encourages improvement through testing and applying user feedback. It is not limited to product design and can be used to problem-solve, collaborate and make decisions. Learn more about how upskilling in design thinking and other areas can help your people thrive across departments

Digital dashboards – similar to the car dashboards they get their name from, these are focused data visualizations that show all the important information someone like a business manager needs to know, arranged around a singular topic (such as sales performance or machine utilization) and that often highlights KPIs or performance metrics; great dashboards are easy to understand, get information from lots of sources automatically, are on one screen and can help show trends in data for one person or the whole business. 

Digital marketing – the part of marketing that applies to digital devices, like smartphones, tablets and desktop computers to tell people about products, services and brands; digital marketing is mostly done online, and can also mean wherever people may see a screen––like airports, gas pumps or train stations. Digital marketing can include email campaigns, social media, webinars, influencer marketing, content marketing, search engine optimizations (SEO), online advertising and texting campaigns. 

Digital storytelling – a way of telling stories using computer-based tools; it often includes media like audio clips, images and video clips. 

Java – a computer programming language; it’s one of the most popular, especially for desktop computing, mobile computing, video game development and back-end development projects that can handle big data. 

Machine learning – a type of artificial intelligence (AI) which uses algorithms to identify patterns and learn over time. It takes the data programmers feed it and uses that data to get better at what it does. For example, speech recognition programs use machine learning to learn a person’s voice and are designed to get better the more it listens to a person talk. 

Natural Language Processing (NLP) – a field of computer science focusing on teaching computers to understand human languages, and turning what it understands into actions; a popular example are digital assistants on smartphones, tablets and smart home devices who “listen” for their names and then complete a task based on what the person says. An example is Alexa. A user says, “Alexa, set a timer for 30 minutes” and the digital assistant responds, “Okay. Timer is set for 30 minutes.” 

Persona development – the creation of fictional characters who represent actual people; useful personas are based on real scenarios––including problems, what people need and knowing who the target customer is––and help meet business objectives by helping the team figure out possible solutions. 

Predictive analytics – using statistical techniques, like machine learning and algorithms, to predict the chances of something happening based on what’s happened before. It can be used to help businesses improve operations, manage resources, attract new customers, promote cross-sell opportunities, predict existing customer behavior, make recommendations to existing customers (think Netflix) and more. 

Process mining – a group of techniques that combine data science software and process management to analyze event data and figure out what people and machines within organizations are really doing in order to provide insights on a better future-state. Analytics are applied to the data around the current-state to create key performance indicators for each process which provides insights on where a company could improve. The kind of data-driven insights process mining provides are very helpful to folks whose job involves dealing with operational business processes.

Prototyping – creating an early version of a product built to test the design while focusing on the people who will use it; this is a critical step in approving a design, to help it move from a minimum viable product to a finished, better version that can be brought to market. 

Python – an open source (free to use) computer programming language often used for making web applications and dynamic web content as well as plug-ins and extensions. Using what’s called “Python Database API”, Python is also able to interact with databases.

Quantum computing – unlike common computing that uses the binary language of ones and zeros, quantum computing is an architecture based on quantum mechanics where all problems are stated as equations. Quantum computing mimics nature at an atomic level, using a qubit, or quantum bit, and electrons, data is sent to the qubit and the electrons are manipulated using quantum mechanics to measure and read answers. Quantum computing has the potential to make artificial intelligence development, predictive analytics and the handling of complex information full of uncertainty much faster. Currently this is an experimental area of science and computing – quantum computers are sensitive to interference and have to be kept at extremely low temperatures. 

Reduced Instruction Set Computer (RISC) – simplified computing structure that has fewer steps in machine language which make chips less complex, and also means they require less power and are less expensive to make; the most popular application are the ARM (see the definition for ARM) microprocessors in nearly every smartphone and tablet.

Reskilling – the process of teaching employees new skills so they can do different jobs than the ones they have today. It often requires looking for people with skills close to the new capabilities your organization needs. Reskilling provides a learning experience that enables a lateral move from one job to another.

Robotic process automation (RPA) – software that saves time and can improve accuracy by automating time-consuming, rules-based office tasks, like creating weekly productivity reports. It works with existing operating systems and processes. See the definitions for “Automation” and “Bot.”  Read more about RPA and how it can empower individuals to contribute to organizational efficiencies.

R – a coding language and open-sourced free software environment for statistical computing and graphics that can run on UNIX, Windows and MacOS. It’s often used for data analysis and developing statistical software. 

Scrum – part of Agile, it’s the structure that organizes the meetings, tools and roles that help teams work together. Scrums are often defined as short meetings, often held while standing up (to encourage brevity), where team members share project progress and identify “blockers” or barriers to the completion of tasks.

Structured Query Language (SQL) – a computer programming language used specifically for storing, manipulating and pulling data from databases. Social media apps use SQL to store user profile information and update the apps’ databases when people share posts and photos. 

Software project management – the process of planning and finishing software projects from the first idea through delivery and ongoing monitoring; this process often uses Agile.

Structured vs. unstructured data – structured data is any information that has been organized, like spreadsheets, relational databases or anything with rows and columns. Unstructured data is the opposite: it is information that has not been organized, like emails, social media content and text messages.

Talent analytics – the use of statistics and technology to inform decisions around hiring, retaining and upskilling current and prospective employees. 

Upskilling –  the process of teaching workers completely new skills that are applicable to their current roles and delivering continuous education to help employees advance on their current career path. Upskilling also gives employees opportunities to gain the knowledge, tools and ability they need to use advanced and ever-changing technologies in the workplace and in their daily lives. Also referred to as rightskilling or newskilling. 

Visual analytics – the use of digital tools and processes to analyze data from data visualizations. Visual analytics goes beyond showing a visual representation of data. It’s the process of combining data from more than one source and analyzing that data within the visualization in a way that can help decision making. 

Virtual reality (VR) – a computer simulated environment that puts the user inside an experience that they can interact with directly. It usually requires some sort of helmet or headset and is meant to be completely immersive. See the definition for “Augmented Reality.”

Workflow automation – managing the progress of information, usually documents––like a contract––from one person to the next using a computerized system; many workflow automation systems give access to the right person at the right time, notify people when tasks are overdue and send the document to the next person who needs it until it is complete; often has the added benefit of creating an audit trail and creating easily-tracked data for business managers.


This digital upskilling glossary is a work in progress. If you see gaps in one of our definitions, or you’ve heard a tech term you’d like to see us add to the list, please let us know.

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