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ITECH Ch 15
| Term | Definition |
|---|---|
| Ad hoc reporting tools | Tools that put users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters. |
| Analytics | A term describing the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. |
| AI | Computer software that seeks to reproduce or mimic (perhaps with improvements) human thought, decision making, or brain functions. |
| Big data | A general term used to describe massive amount of data available to today's managers. |
| Business intelligence | A term combining aspects of reporting, data exploration and ad hoc queries, and sophisticated data modeling and analysis. |
| Canned reports | Reports that provide regular summaries of information in a predetermined format. |
| CAPTCHAs | An acronym for Completely Automated Public Turing Test to Tell Computers and Humans Apart |
| Column/field | A column in a database table. Columns represent each category of data contained in a record (e.g., first name, last name, ID number, date of birth). |
| Dashboards | A heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics. |
| Data | Raw facts and figures |
| Data aggregators | Firms that collect and resell data. |
| Database | A single table or a collection of related tables. |
| Database administrator (DBA) | Job title focused on directing, performing, or overseeing activities associated with a database or set of databases. |
| Data management system (DBMS) | Sometimes referred to as database software; software for creating, maintaining, and manipulating data. |
| Data cube | A special database used to store data in OLAP reporting. |
| Data lake | A catch-all term for storage and access technologies used in Big Data |
| Data mart | A database or databases focused on addressing the concerns of a specific problem (e.g., increasing customer retention, improving product quality) or business unit (e.g., marketing, engineering). |
| Data mining | The process of using computers to identify hidden patterns in, and to build models from, large data sets. |
| Data warehouse | A set of databases designed to support decision making in an organization. |
| Deep learning | A type of machine learning that uses multiple layers of interconnections among data to identify patterns and improve predicted results |
| E-discovery | The process of identifying and retrieving relevant electronic information to support litigation efforts |
| Hadoop | A set of mostly open-source tools to manage massive amounts of unstructured data for storage, extraction, and computation |
| Information | Data presented in a context so that it can answer a question or support decision making |
| Knowledge | insight derived from experience and expertise |
| Legacy systems | Older information systems that are often incompatible with other systems, technologies, and ways of conducting business |
| Loyalty card | Systems that provide rewards and usage incentives, typically in exchange for a method that provides a more detailed tracking and recording of customer activity. |
| Machine learning | A type of artificial intelligence that leverages massive amounts of data so that computers can improve the accuracy of actions and predictions on their own without additional programming. |
| Neural networks | Statistical techniques used in AI, and particularly in machine learning. Neural networks hunt down and expose patterns, building multilayered relationships that humans can't detect on their own. |
| Optical character recognition (OCR) | Optical Character Recognition. Software that can scan images and identify text within them. |
| Omnichannel | Providing customers with a unified experience across customer channels, which may include online, mobile, catalog, phone, and retail. Pricing, recommendations, and incentives should reflect a data-driven, accurate, single view of the customer. |
| Online analytical processing (OLAP) | A method of querying and reporting that takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube. |
| Over-engineer | Build a model with so many variables that the solution arrived at might only work on the subset of data you've used to create it. |
| Relational database | The most common standard for expressing databases, whereby tables (files) are related based on common keys. |
| Row/record | A row in a database table. Records represent a single instance of whatever the table keeps track of (e.g., student, faculty, course title). |
| Serverless | A type of cloud-computing where a third-party vendor managers servers, replication, fault-tolerance, computing scalability and certain aspects of security |
| Structured query language (SQL) | A language used to create and manipulate databases. |
| Table | A list of data, arranged in columns (fields) and rows (records) |
| Transaction | some kind of business exchange |
| Transaction processing system (TPS) | Systems that record a transaction (some form of business-related exchange), such as a cash register sale, ATM withdrawal, or product return. |