Process Automation Glossary

A curated list of terms related to digital transformation and process automation

Top glossary entries

Digital Transformation

Digital transformation describes the process of replacing manual, non-automated work and inefficient technology solutions with newer digital technologies…

Process Orchestration

Process orchestration manages the execution of tasks according to a flow logic that describes a business process. The tasks in the process are executed by endpoints, which can be…

Process Optimization

Process optimization is a follow-on from process automation and as such part of an organization’s digital transformation: once a process has been automated…

All glossary terms

Artificial intelligence is a wide-ranging branch of computer science focused on building intelligent machines capable of performing tasks that typically require human intelligence, understanding and interpretation. In general use, the term artificial intelligence is often used to describe machines that mimic human functions that are associated with the human mind, such as learning, decision making and problem solving.

AIOps, a term coined by Gartner, is an industry category for machine learning analytics technologies that enhance IT operations analytics and decision making. AIOps is the analysis of IT data with the goal being to receive continuous insights that provide continuous fixes and improvements via automation. IT Ops and DevOps organizations benefit from AIOps with the ability to detect technology outages and service issues earlier and to mitigate them in an effort to minimize the impact on business operations and customer satisfaction.

BPM is a systemic approach for capturing, designing, executing, documenting, measuring, monitoring and controlling both automated and non-automated processes to meet the objectives and business strategies of an organization.

Business process modeling is a graphical representation of an organization’s business processes and workflows. It is a vital part of effective process management as it generates critical insights into how smoothly a process is running. 

A comprehensive process model typically includes:

  • The events and activities that occur within the process
  • The owner and originator of said events and activities
  • Decision points and the different path processes can follow based on the decision made
  • Devices that are part of the process
  • The duration of the overall process and its substeps
  • Analytics such as success and failure rates

Process modeling software gives an analytical representation of the current state of an organization’s processes. Tools like Optimize can then be used to identify bottlenecks and other performance issues to make the processes more efficient.

BPMN is a graphical process notation and representation standard, a visual language, that is used to model and automate processes. Since its introduction in 2004, it has become a widely adopted standard in the business process modeling community. BPMN is designed to combine ease of use with support for complex processes and process logic and also facilitates collaboration between business users and IT teams.

Automate business processes from end to end

Cloud automation is the reduction or complete elimination of manual intervention required to manage and operate cloud-based IT infrastructure. Cloud automation tools help automate and orchestrate repetitive and manual processes to reduce administrative overhead and enable continuous integration and continuous deployment. Other benefits of cloud automation include better security and resilience, standardized governance and more reliable backup processes.

DMN is a standard language to describe and model repeatable business decisions. DMN is designed to be easily applied and read by both business users and IT teams, facilitating collaboration around decision management and business rules. While DMN can be used by itself, it is often used to complement BPMN to fully automate a business process, including decisions that have to be calculated according to business rules.

Digital transformation describes the process of replacing manual, non-automated work and inefficient technology solutions with newer digital technologies. The aim is to make it easier for organizations to adapt to changes in the competitive landscape by improving their ability to innovate at pace. This increased business agility often brings other benefits such as a better, frictionless customer experience, cost savings and workable data insights to further improve upon and innovate. To succeed, organizations have to integrate digital technologies into all areas of the business and fundamentally change the way they operate. With this comes the opportunity to disrupt the status quo, invent new strategies for success and truly thrive in today’s quickly changing landscape.
Hyperautomation, a term first coined by Gartner, is a unified, business-driven strategy of automating and coordinating as many business and IT processes as possible. This is achieved through a range of tools such as enterprise resource planning (ERP) software, intelligent business management suites (iBPMS), and robotic process automation (RPA) tools. These are then combined with emerging technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Hyperautomation is a relatively new concept, but seen by many as a crucial part of digital transformation and significantly more impactful than traditional automation strategies.

iBPMS is a term coined by Gartner and describes solutions that offer traditional business process management (BPM) functionality with enhanced technologies such as cloud computing to automate business processes at scale as well as artificial intelligence (AI). They support real-time decision making and analytics as well as Internet of Things (IoT) integration. Some iBPMS coordinate a variety of endpoints, whereas others are limited to orchestrating the work that happens within the suite itself.

Internet of Behaviors, a term coined by Gartner, refers to the accumulation and analysis of human behavior through data gathered from human interaction with connected devices. It is essentially making sense of human behaviors through data mining, with a view to manipulate future behavior based on the understanding gained. Insights gathered from this analysis can be used to influence user experience, fine-tune personalization, optimize search experiences, and track buying behaviors and brand preferences.

Intelligent process automation (IPA) is the combination of robotic process automation (RPA) and machine learning with the aim of automating more complete, end-to-end business processes and further reducing the need for manual intervention. By adding artificial intelligence to the rules-based approach of RPA, IPA tools can leverage machine learning algorithms to improve task performance over time and execute enhanced decision-making capabilities.

By mimicking the tasks traditionally carried out by human workers and learning to do them better, IPA promises to drastically improve efficiency, response times and customer experiences while reducing operational risks.

Low code is a development approach that allows people to connect to data, build simple user interfaces or even entire software applications using a graphical user interface instead of, or in addition to, writing code. Low-code development platforms abstract many software development concepts with the goal of enabling a wider range of users to create basic applications for specific business purposes, expediting the development process. In a low-code platform, extensive customization or sophisticated application development may be constrained and often requires the use of pre-packaged tools (such as proprietary integrated development environments) or programming languages.

Machine learning is a branch of artificial intelligence that focuses on getting computers to take actions that they haven’t been explicitly programmed to carry out. Machine learning is the study of making machines more human-like in their behavior and decisions by giving them the ability to learn and develop their own programs with minimum human intervention. The learning process is automated and improves based on the experiences of the machines and human feedback throughout the process.

Microservices is a modular architecture style in which a complex application is made up of small, autonomous and loosely coupled services. These services are fine-grained, implemented using various programming languages, messaging enabled, independently deployable and decentralized. Services are organized around business capabilities and work together to fulfill business goals. Key advantages over other, monolithic styles are a more flexible technology stack largely independent of specific vendors as well as quick deployments and scalability.

Natural language processing is a type of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from disciplines such as computer science and computational linguistics, in an effort to understand both written and verbal human communication and the contextual nuances of the language within them. NLP can then accurately extract insights from such communication to support more informed decision making. The technology has many uses, including in translation software, voice assistants and social media monitoring tools.

Like low code, no code is a development approach that allows non-developers to build simple software applications using a graphical user interface and drag and drop features. While low-code development platforms are designed for users who have limited knowledge of programming languages, no-code development platforms are designed for business users who have no such knowledge. These platforms often have limited capabilities for building custom application logic or custom user interfaces and are frequently used for line-of-business applications.

Process automation is the orchestrated execution of business processes via a workflow engine. End-to-end process automation is a key component of digital transformation. Process automation reduces cost and resource requirements, thereby improving productivity and efficiency and ultimately customer experience. It also contributes to standardization of processes and the creation of an audit trail, therefore increasing security and compliance.

Process mining is the use of data science and process management to discover, monitor and improve processes via event log data. Process-based event logs are analyzed and turned into insights and actions with the end goal of improving processes over time. If there is a substantial amount of digitization (and thus data), process mining can help identify opportunities to enhance performance by comparing current performance against process KPIs.

Process optimization is a follow-on from process automation and as such part of an organization’s digital transformation: once a process has been automated, it requires monitoring and periodic review to remain as effective and efficient as possible. Process optimization enables organizations to adapt and improve processes: it helps locate and remove bottlenecks, identify more areas where manual processes can be automated and fix performance issues with technical means, for example by adding more workflow engines to improve data flow. This is especially important in sectors with high competitive and legal pressure such as telecommunications, insurance and financial services.

Process orchestration manages the execution of tasks according to a flow logic that describes a business process. The tasks in the process are executed by endpoints, which can be people, software systems, or physical devices. Process orchestration coordinates all of these process endpoints, and sometimes even ties multiple processes together. Without process orchestration, you have a disconnected set of local tasks and automations, leading to an end-to-end process that is not fully automated. Process orchestration is necessary when your business processes involve a diverse set of endpoints and when processes reach a level of complexity beyond a straightforward sequence of steps. To learn more, check out The Process Orchestration Handbook.

Robotic process automation (RPA) is a type of business process automation technology that uses software robots (bots) in situations where people interact with digital systems. Instead of a person carrying out often repetitive, administrative digital tasks such as invoice handling, RPA allows bots to take over these tasks to free up the human worker’s time so they can focus on mission-critical tasks. 

RPA has a large number of use cases. Some areas where bots offer especially high benefits are finance and accounting, HR and retail.

Workflow automation describes the process of designing, executing and automating a series of tasks that run on their own without the need for manual intervention. Workflow automation is typically based on rules and if/then logic to trigger the next task. Once the rules and logic have been determined, workflow automation can be used to send emails, schedule tasks, and more with the aim to increase productivity, reduce errors and save time and money.

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