Market interest and demand to automate business processes continues to grow. With growth comes change. This includes new concepts and a vocabulary to articulate change. To help navigate this change, we thought it might be helpful to provide this intelligent automation glossary. Use this reference guide to understand the many new terms associated with this practice. The act of automating a process simply means to have one event trigger another. Automation doesn’t immediately imply better productivity. Doing the wrong thing fast is hardly any company’s objective. Increasing business performance means acting smarter while making decisions. Add intelligence to an automated process means that you can leverage knowledge to operate with greater efficiency or performance.
Here is an intelligent automation glossary of terms associated with this practice of coupling intelligence with automation.
Application Programming Interface (API) – An interface that allows different software applications to interact and share data with each other.
Artificial Intelligence (AI) – The ability of a computer to simulate human thinking and understanding.
Automation – The act of automating a task to operate without human intervention.
Bot – An autonomous software program on the Internet or another network that can interact with systems or users; short for “robot”.
Business Process Automation (BPA) – A tactic to automate workflows with the purpose of improving an organization’s efficiency; see Software Bot.
Business Process Management (BPM) – A process improvement strategy typically focused on re-engineering and modifying business processes; the ultimate goal is to improve enterprise-wide efficiency and productivity.
Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) – This is a type of security measure known as challenge-response authentication. Websites with CAPTCHA technology embedded will require a different approach when using web scraping technologies to extract data as part of a digital workflow.
Data Extraction – The process of scanning data from paper or other non-digital media into a digital format; other software applications can then read and perform processes based on this data.
Data Parsing – The process of converting a string of data from one format to another, such as converting HTML data into plain text.
Database Administrator (DBA) – The title of a person who is responsible for creating, managing, securing, and maintaining a database.
Data-driven Decision Support – An ideal end-state of implementing intelligent process automation; the right information is provided to the right person at the right time so they can make better decisions that improve efficiency, performance, and customer satisfaction.
Deep learning – An area of machine learning that puts neural networks on top of each other to achieve higher accuracy and performance than an individual machine learning algorithm.
Extract, Transform, Load (ETL) – The process involved in extracting, transforming, and loading raw data into a database that is accessible and in a readable format.
Hyperautomation – A relatively new term defined by Gartner; it that means to rapidly identify and automate as many business processes as possible using software, robotic process automation, and machine learning.
Intelligent Process Automation – A collection of business-process improvements and modern technologies that combines fundamental process redesign with robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), and cognitive technologies like optical character recognition (OCR) and natural language processing (NLP).
Machine Learning – The use and development of computer systems that are able to learn and adapt without following explicit instructions. Note that all machine learning is AI, but not all AI is machine learning. Learn more here about how Artificial is different than machine learning.
Managed IT Services – The practice of outsourcing the responsibility for maintaining, and anticipating the need for, a range of IT processes and functions, ostensibly for the purpose of improved operations and reduced budgetary expenditures through the reduction of directly-employed staff. This practice is an effective approach to gaining access to skillsets missing from existing IT staff.
Natural Language Processing (NLP) – A process that utilizes software or other technologies to understand, interpret and manipulate human language.
Optical Character Recognition (OCR) – A technology that converts images of handwritten text into the machine-interpreted text to simplify the interpretation of the results to ease the data entry process.
Quality Assurance (QA) – This is a critical part of custom application programming whereby applications are tested to ensure they perform as expected and planned while any redundant programming or resource allocation is minimized or eliminated.
Robotic Process Automation (RPA) – An automation technology that utilizes software robots, commonly referred to as “bots” that often rely upon Artificial Intelligence (AI) to perform an assigned task.
Semi-structured Data – Data that is not located in a database or spreadsheet but still has some attributes that make it easier to organize; examples might include XML documents and NoSQL databases; see Structured Data and Unstructured Data.
Software Bot – A computer program that performs automated, repetitive tasks that simulate human activity; the term “Bot” is short for Robot; see Robotic Process Automation (RPA).
Structured Data – Data that is organized into a format or fields that can be readily extracted from one application to another; an example is data contained in a spreadsheet or database; see Unstructured Data and Semi-structured Data.
Tag Image File Format (TIF or TIFF) – An image file format for storing graphics images with higher resolution; popular among graphic artists, the publishing industry, and photographers. TIFF files are widely supported by scanning, faxing, word processing, optical character recognition, image manipulation, desktop publishing, and page-layout applications.
Unstructured Data – Data not organized into any particular format; examples include photos, videos, emails, books, social media posts, or health records. This data may be in a digital format, yet still, be unstructured; see Structured Data and Semi-structured Data.
Workflow – A series of activities that are necessary to complete a task; a workflow is digitalized when performed by a software application or automation program.
Did we miss any terms? Please let us know and we’ll be sure to append this intelligent automation glossary. Send us an email at email@example.com.