Technical Glossary

Analytics:
Data that helps companies track business trends.  Analytics comprises all programming that analyzes data about an enterprise's business activities and customer information and presents it so that better and quicker business decisions can be made.

Application Consolidation:
Merging two or more applications together to reduce the number of applications in an enterprise.

Application Integration:
The process of creating communication between applications performing distinctive functions such as CRM, Billing, and Logistic, and sharing and updating common information such as customer details, services,  products catalog information, etc.

Business Activity Monitoring (BAM):
The monitoring of all of an enterprise's business processes and IT activities through the use of specialized software components. BAM solutions can be used to alert individuals to changes in the business that may require action.

Business Intelligence (BI):
Business intelligence (BI) is a category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include decision support systems, query and reporting, online analytical processing, statistical analysis, forecasting, and data mining.

Changed Data Capture (CDC):
Changed Data Capture (CDC) helps identify the data in the source system that has changed since the last extraction. With CDC, data extraction takes place at the same time the insert, update, or delete operations occur in the source tables, and the change data is stored inside the database in change tables. The change data, thus captured, is then made available to the target systems in a controlled manner.

Connectivity:
Refers to a program or device's ability to link with other programs and devices. For example, a program that can import data from a wide variety of other programs and can export data in many different formats is said to have good connectivity. However, computers that have difficulty linking into a network (many laptop computers, for example) have poor connectivity.

Consolidation:
The process of taking data from different systems and disparate formats, and combining and aggregating that information to create a unified view.

Data Integration (DI):
The process of combining two or more data sets together for sharing and analysis, in order to support information management inside a business.

Data Partitioning:
The process of taking a set of data and dividing it into smaller subsets that are more easily maintained. Partitioning data can aid in performance, by employing concurrent processing for example.

Data Mart:
A database, or collection of databases, designed to help managers make strategic decisions about their business. Whereas a data warehouse combines databases across an entire enterprise, data marts are usually smaller and focus on a particular subject or department. Some data marts, called dependent data marts, are subsets of larger data warehouses.

Data Migration:
The process of translating data from one format to another. Data migration is necessary when an organization decides to use a new computing systems or database management system that is incompatible with the current system. Typically, data migration is performed by a set of customized programs or scripts that automatically transfer the data.

Data Profiling:
Data Profiling furnishes thorough, accurate information about the content, quality, and structure of data and is critical in enabling effective data integration.

Data Synchronization:
Where multiple applications hold the same sets of data and one of the users change the state of their shared object, this change will immediately propagate to the shared objects of the other users.  The process of sending, receiving, and updating data between multiple systems.

Data Warehouse:
A collection of data designed to support management decision making. Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time.  Development of a data warehouse includes development of systems to extract data from operating systems and applications plus installation of a warehouse database system that provides managers flexible access to the data.  The term data warehousing generally refers to the combination of many different databases across an entire enterprise.

Enterprise Application Integration (EAI):
EAI enables data propagation and business process execution throughout the numerous distinct networked applications as if it would be a unique global application. It is a distributed transactional approach and its focus is to support operational business functions such as taking an order, generating an invoice, and shipping a product.

Enterprise Information Integration (EII):
An integration technology that pulls and combines data from multiple systems “real time”, without storing it on a disk (“on the fly” transformation), creating a “virtual” data warehouse; eliminating the need to store or move data.

Extract, Transform, Load (ETL):
Three database functions that are combined into one tool to pull data out of source databases and place it into target databases.  ETL is used to migrate data from databases to others, to form data marts and data warehouses and also to convert databases from one format or type to another.

• Extract—the process of reading data from databases.
• Transform—the process of converting the extracted data from its previous form into the form it needs to be in so that it can be placed into other databases. Transformation occurs by using rules or lookup tables or by combining the data with other data.
• Load—the process of writing the data into the target databases.

General Reporting:
Basic information about the operations of a company.

Grid Computing:
Grid computing is a model for allowing companies to use a large number of computing resources on demand, no matter where they are located.

Integration Competency Center (ICC):
An ICC is typically a shared, centralized resource that defines uniform approaches to integration with reusable assets. There are a variety of ways to set up an ICC—from simply defining a series of best practices to specifying specific tools or architectures that must be used, to providing centralized developers and architects that can actually create and manage integrations. What’s right for your company depends on such things as the corporate structure (centralized or decentralized), frequency of projects, your level of standardization, and your IT infrastructure.

Legacy Retirement:
The process of decommissioning an older system (i.e. mainframes) that are no longer necessary or in use.

Mainframe Conversion:
The process of decommissioning a mainframe system by converting all the data and transactions currently being maintained on the mainframe, to another mainframe system.

Mainframe Integration:
The process of taking data from a mainframe system and combining it with other applications in the environment.

Metadata Management:
Data about data. Metadata describes how and when and by whom a particular set of data was collected, and how the data is formatted. Metadata is essential for understanding information stored in data warehouses and has become increasingly important in XML-based Web applications.  Metadata Management is becoming very important because as systems become more and more interdependent, it is vital to know the impact that results when data is altered.

Operational Data Store (ODS):
A type of database that serves as an interim area for a data warehouse in order to store time-sensitive operational data that can be accessed quickly and efficiently. In contrast to a data warehouse, which contains large amounts of static data, an ODS contains small amounts of information that is updated through the course of business transactions. An ODS will perform numerous quick and simple queries on small amounts of data, whereas a data warehouse will perform complex queries on large amounts of data. An ODS contains only current operational data while a data warehouse contains both current and historical data.

Single View of “X”:
The ability to have one standard set of data for a topic, such as a customer, across all departments.

Star Schema:
A method of organizing information in a data warehouse that allows the business information to be viewed from many perspectives. The star is a picture of the way the data is being stored. The basic factual information is in the middle of the star. The points of the star represent various perspectives from which the factual information can be viewed. 

Visibility:
The ability to understand and interact with enterprise data for analysis and for making better business decisions.

Zero Latency:
No time lapse between receipt of information and the ability to analyze and act on that information.