Term
| Applications Architecture |
|
Definition
An applications architecture describes the structure and behavior of applications used in a business, focused on how they interact with each other and with users. It is focused on the data consumed and produced by applications rather than their internal structure. In application portfolio management, the applications are usually mapped to business functions and to application platform technologies.
The applications architecture is specified on the basis of business requirements. This involves defining the interaction between application packages, databases, and middleware systems in terms of functional coverage. This helps identify any integration problems or gaps in functional coverage. A migration plan can then be drawn up for systems which are at the end of the software life cycle or which have inherent technological risks.
Applications architecture means managing how multiple applications are poised to work together. It is different from software architecture, which deals with design concerns of one application |
|
|
Term
|
Definition
| Application Development (AD) software market comprises tools that represent each phase of the software development life cycle: Application Lifecycle Management (ALM), design, construction, automated software quality and other Application Development software |
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
| Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance |
|
|
Term
| Business Process Improvement |
|
Definition
|
|
Term
|
Definition
| A style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies |
|
|
Term
| Customer Relationship Management |
|
Definition
|
|
Term
|
Definition
|
|
Term
| Data Governance & Quality |
|
Definition
|
|
Term
|
Definition
| data warehouse is a storage architecture designed to hold data extracted from transaction systems, operational data stores and external sources. The warehouse then combines that data in an aggregate, summary form suitable for enterprise-wide data analysis and reporting for predefined business needs. The five components of a data warehouse are production data sources, data extraction and conversion, the data warehouse database management system, data warehouse administration and business intelligence (BI) tools |
|
|
Term
| Enterprise Analytics & Reporting |
|
Definition
| Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user. These analytics solutions often come with prebuilt industry content that is targeted at an industry business process (for example, claims, underwriting or a specific regulatory requirement). |
|
|
Term
| Enterprise Resource Planning |
|
Definition
is defined as the ability to deliver an integrated suite of business applications. ERP tools share a common process and data model, covering broad and deep operational end-to-end processes, such as those found in finance, HR, distribution, manufacturing, service and the supply chain.
ERP applications automate and support a range of administrative and operational business processes across multiple industries, including line of business, customer-facing, administrative and the asset management aspects of an enterprise. However, ERP deployments tend to come at a significant price, and the business benefits are difficult to justify and understand.
Look for business benefits in four areas: IT cost savings, business process efficiency, as a business process platform for process standardization and as a catalyst for business innovation. Most enterprises focus on the first two areas, because they are the easiest to quantify; however, the latter two areas often have the most significant impact on the enterprise. |
|
|
Term
|
Definition
| All the sources of information — including paper, graphics, video, speech and thought — that define the enterprise are represented by this layer of applications architecture. It also defines the sources and destinations of information, its flow through the enterprise, as well as the rules for persistence, security and ownership. |
|
|
Term
|
Definition
|
|
Term
| Integration Management & Middleware Development |
|
Definition
|
|
Term
|
Definition
Project management is the application of knowledge, skills, tools and techniques to project activities to meet the project requirements.
Gartner analysts observe many clients focused on enhancing the collaboration, communication and resulting project portfolio-level reporting needed to gain first-line visibility into project demand, project status, resource capacity and utilization, and cost estimates versus actuals.
PPM leaders recognize that the primary value of projects goes beyond delivering on-time and on-budget projects, and rather ensures the expected business outcomes and realized value. Further, they are increasingly aware that the biggest changes from project activities are actually people-oriented, and that they influence culture and mind-sets across the enterprise.
Successful project management is characterized by the ongoing development and refinement of methods and practices and ensuring that any software tools deployed meet the needs without becoming a burden. |
|
|
Term
|
Definition
| Mobile Web applications refer to applications for mobile devices that require only a Web browser to be installed on the device. They typically use HTML and Ajax (and, increasingly, HTML5 components), although they may make use of augmented rich Internet application (RIA) technologies, such as Flash, JavaFX and Silverlight, but are not written specifically for the device. Rich, mobile Web applications have roughly equivalent usability to PC-rich Web applications (or RIAs), when designed specifically for smaller form factors. Simple mobile Web applications limit the use of RIA technologies and are designed to present information in a readable, action-oriented format. Mobile Web applications differ from mobile native applications, in that they use Web technologies and are not limited to the underlying platform for deployment. |
|
|
Term
| Quality Assurance & Software Testing |
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
| Open Source & Web 2.0 Technologies |
|
Definition
|
|
Term
|
Definition
|
|
Term
| QA Testing Automated & Manual Tools |
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
|
|
Term
|
Definition
The discipline of data integration comprises the practices, architectural techniques and tools for achieving the consistent access and delivery of data across the spectrum of data subject areas and data structure types in the enterprise to meet the data consumption requirements of all applications and business processes.
Data integration tools have traditionally been delivered via a set of related markets, with vendors in each market offering a specific style of data integration tool. In recent years, most of the activity has been within the ETL tool market. Markets for replication tools, data federation (EII) and other submarkets each included vendors offering tools optimized for a particular style of data integration, and periphery markets (such as data quality tools, adapters and data modeling tools) also overlapped with the data integration tool space. The result of all this historical fragmentation in the markets is the equally fragmented and complex way in which data integration is accomplished in large enterprises — different teams using different tools, with little consistency, lots of overlap and redundancy, and no common management or leverage of metadata. Technology buyers have been forced to acquire a portfolio of tools from multiple vendors to amass the capabilities necessary to address the full range of their data integration requirements.
This situation is now changing, with the separate and distinct data integration tool submarkets converging at the vendor and technology levels. This is being driven by buyer demands as organizations realize they need to think about data integration holistically and have a common set of data integration capabilities they can use across the enterprise. It is also being driven by the actions of vendors, such as those in individual data integration submarkets organically expanding their capabilities into neighboring areas, as well as by acquisition activity that brings vendors from multiple submarkets together. The result is a market for complete data integration tools that address a range of different data integration styles and are based on common design tooling, metadata and runtime architecture. |
|
|
