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Here is a comprehensive list of Business Intelligence terms of your interest and reference.
In case you do not find any information you are looking for, please feel free to email us and we will get back to you as soon as possible. |
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Glossary Locate by searching our alphabetical listing. |
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| Dashboard |
| It refers to the visual similarities to a car dashboard. A user interface that organizes and presents information in an easy-to-read format and help organisations align people's actions with strategy by tracking and analysing key business metrics and goals. Dashboards and scoreboards enable proactive management via "what-if" analysis, customer segmentation, forecasting, and analysing business processes. It provides graphical depictions of current key performance indicators in order to enable faster response to changes in areas such as sales, customer relations, performance assessments etc on single screen. |
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| Data Cleansing |
| The process of ensuring that a program operates on clean, correct and useful data. It includes manipulation of data - using a variety of techniques: parsing, standardizing, correcting, and consolidating- extracted from operational systems so as to make it usable by the data warehouse. |
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| Data Cube |
| A multidimensional structure that forms the basis for analysis applications. Cubes have three dimensions and allow for a variety of calculations and aggregations. The dimensions can usually be pivoted in reports. |
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| Data Mapping |
| The process of identifying a source data element for each data element in the target data warehouse environment. For example, gender (‘M’/ ‘F’) is decoded and mapped to a gender description field as ‘Male’ or ‘Female’. |
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| Data Mart |
| A data mart (DM) is a specialized version of a data warehouse (DW). Like data warehouses, data marts contain a snapshot of operational data that helps business people to strategise based on analyses of past trends and experiences. The key difference is that the creation of a data mart is predicated on a specific, predefined need for a certain grouping and configuration of select data. A data mart configuration emphasizes easy access to relevant information. |
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| Data Mining |
| A component of business intelligence. Data mining relates to discovering previously unknown patterns within a data set, typically by testing the validity of different ways of describing the data. |
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| Data Quality |
| Data quality pertains to aspects such as availability, completeness, accuracy, consistency, relevance and timeliness of data. High data quality is essential to business intelligence’s role as a means of decisional support. Poor data quality examples: missing fields, old or inaccurate information, data conflicts, inaccessible data in legacy systems. |
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| Data Source |
| In basic terms, a data source is a facility for storing data. It can be as sophisticated as a complex database for a large corporation or as simple as a file with rows and columns. A data source can reside on a remote server, or it can be on a local desktop machine. Applications access a data source using a connection, and a Data Source object can be thought of as a factory for connections to the particular data source that the Data Source instance represents. |
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| Data Staging |
| This denotes a system area where all the data extraction, transformation and loading operations are performed. This is the work area where data warehouse developers clean, summarize, filter, decode and prepare data. Program codes are split into different columns: degree and major. Misspelled majors are fixed. |
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| Data Visualization |
| This means data represented by visually recognised patterns and trends. It maintains live data connectivity, provides visuals and interactivity. |
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| Data Warehouse |
| A database that is geared toward the business intelligence requirements of an entire organisation. The data warehouse integrates data from the various operational systems and is typically loaded from these systems at regular intervals. Data warehouses contain historical information that enables analysis of business performance over time. |
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| Decision Support System (DSS) |
| The purpose of a decision support system is to provide decision makers in organisations with information. The information advances the decision makers' knowledge in some way so as to assist them in making decisions about the organisation's policies and strategy. Key characteristics of DSS are ease of use, flexibility and adaptability. DSS applications focus on the types of problems encountered by senior managers. |
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| Dimensional Hierarchy |
| A dimensional hierarchy refers to the different levels of data within a dimension table. Data can be rolled up or drilled down to for analysis. This can be represented in a data model by multiple columns within a dimension table in standard star schemas called hierarchy columns. |
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| Drag and Drop Interface |
| A graphical user interface (GUI) capability that lets you perform operations by moving the icon of an object with the mouse into another window or onto another icon. For example, files can be copied or moved by dragging them from one folder to another. Programs can be executed by dragging and dropping. Drag and drop is essential for graphics applications where you need to position text and images on the page or on top of each other. |
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| Drag and drop OLAP |
| Online Analytic Processing can be done on the fly based on the criteria, the business users provides to the X- & Y- axis. Drag and Drop OLAP functionality allows users to rapidly select the most appropriate view from a multitude of perspectives to understand the drivers behind the business. |
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| Drill Down |
| A component of OLAP analysis. The term drill down, in the context of data analysis, refers to the process of navigating from less detailed aggregated information to viewing more granular data. |
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| Drill Through |
| Drill through is an action in which you move horizontally between two items via a related link. An example to drill through is in the case of two reports that are in a master /detail relation with each other, and by clicking a master item on the master report you reach the details of the clicked item on the details report. |
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| Drill Up (Roll up) |
| It is a specific analytical technique whereby the user navigates among levels of data ranging from the most detailed (down) to the most summarized (up) along a concept hierarchy. For example, when viewing the data for the city of Toronto, a roll-up operation in the Location dimension would display Ontario. A further roll-up on Ontario would display data for Canada. |
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| Drillable Charts |
| Users can drill-down to underlying, granular data from the graphic analysis. In drillable pie charts, you can drill directly down into each segment or in a bar graph by double clicking on the bar. |
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