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Software Functionality Revealed in Detail
We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.
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 jobs data warehousing business intelligence


Comparing Business Intelligence and Data Integration Best-of-breed Vendors' Extract Transform and Load Solutions
There are two types of extract transform and load (ETL) vendors. Business intelligence (BI) vendors integrate ETL functionality into their overall BI framework,

jobs data warehousing business intelligence  schedule process builds and jobs on any server located within the network. Additionally, processes such as hierarchy and data validation definitions are automated, allowing embedded support for slowly changing dimensions and late arriving data. Data Manager enables the data integration process within a simple drag-and-drop environment. SAS 's Data Integration uses a wizard-driven user interface to provide ease of use for end users. Included in its ETL functionality is the ability for processing to occur

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

Discrete Manufacturing (ERP)

The simplified definition of enterprise resource planning (ERP) software is a set of applications that automate finance and human resources departments and help manufacturers handle jobs such as order processing and production scheduling. ERP began as a term used to describe a sophisticated and integrated software system used for manufacturing. In its simplest sense, ERP systems create interactive environments designed to help companies manage and analyze the business processes associated with manufacturing goods, such as inventory control, order taking, accounting, and much more. Although this basic definition still holds true for ERP systems, today its definition is expanding. Today’s leading ERP systems group all traditional company management functions (finance, sales, manufacturing, and human resources). Many systems include, with varying degrees of acceptance and skill, solutions that were formerly considered peripheral such as product data management (PDM), warehouse management, manufacturing execution system (MES), and reporting. During the last few years the functional perimeter of ERP systems began an expansion into its adjacent markets, such as supply chain management (SCM), customer relationship management (CRM), business intelligence/data warehousing, and e-business, the focus of this knowledge base is mainly on the traditional ERP realms of finance, materials planning, and human resources. The foundation of any ERP implementation must be a proper exercise of aligning customers'' IT technology with their business strategies, and subsequent software selection. 

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Documents related to » jobs data warehousing business intelligence

The Challenges of a Business Intelligence Implementation: A Case Study


The University of Illinois provides a good example of extensive integration of its business intelligence (BI) solution and data warehousing environment with its enterprise resource planning (ERP) solution.

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Business Intelligence Success, Lessons Learned


Is business intelligence (BI) an application that pays off? We have all heard mixed results but a 2003 extensive study on on-line analytical processing (OLAP) states that BI usually pays off over 60 percent of the time , explains where the value is found, and describes what’s required to get the pay off.

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Open Source Business Intelligence: The Quiet Evolution


As organizations face a pressing need to rationalize the cost of enterprise software, open source business intelligence (BI) is fast becoming a viable alternative. Learn about the current state of open source BI, with particular focus on one vendor's products.

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Business Intelligence Standardization


Business intelligence (BI) is often an area of friction between information technology (IT) (who provide information) and the business users (who need it to do their jobs). By allowing you to connect goals, metrics, and people across the enterprise, an enterprise BI standard helps organizations manage and optimize information flows like other business processes, leading to improved alignment and transparency.

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Data Quality: A Survival Guide for Marketing


Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more.

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Is My Business Too Small for Business Intelligence (BI)?


Does business intelligence (BI) make sense for really small businesses? While BI solutions can surface and analyze business data, providing a competitive advantage for enterprises, this inquiry puts the spotlight on smaller businesses. But how does a small business deal with the costs associated with this kind of system? Learn why (and how) BI can be a smart choice for even the smallest business.

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2011 Trends Report: Business Intelligence (BI)


The current economic environment is marked by more frequent changes than in the past. It’s no wonder that more businesses are looking to business intelligence (BI) software to identify and analyze business data to speed responses to these changes. So, what changes are ahead for BI? In this guide, experts Bill Cabiro, David Crandall, Wayne Kernochan, Kirsty Lee, Clarice Lin and Shawn Rogers share their predictions for BI.

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Making Business Intelligence Easy: Collaborative Business Intelligence


The merging of BI and Web 2.0 technologies has given rise to the new concept of collaborative BI, a type of collaborative decision making (CDM) platform. Collaborative BI has been hailed by many as the answer to the persistent problem that, despite increasing amounts of money being spent on BI, many organizations are failing to utilize reporting and analytics effectively. Read this white paper to learn the essential features of a collaborative BI platform and how to enable collaborative BI in your organization.

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The Operational Data Lake: Your On Ramp to Big Data


Companies recognize the need to integrate big data into their real-time analytics and operations, but this poses a lot of technical and resource challenges. Meanwhile, those organizations that have operational data stores (ODSs) in place find that, while useful, they are expensive to scale. The ODS gives real-time visibility into operational data. While more cost-effective than a data warehouse, it uses outdated scaling technology, and performance upgrades require very specialized hardware. Plus, ODSs just can't handle the volume of data that has become a matter of fact for businesses today.

This white paper discusses the concept of the operational data lake, and its potential as an on-ramp to big data by upgrading outdated ODSs. Companies that are building a use case for big data, or those considering an upgrade to their ODS, may benefit from this stepping stone. With a Hadoop relational database management system (RDBMS), companies can expand their big data practices at their own pace.

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Big Data Comes of Age: Shifting to a Real-time Data Platform


New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose-built platforms more capable of meeting the real-time needs of more demanding end users and the opportunities presented by big data. Read this white paper to learn more about the significant strategy shifts underway to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support big data and the real-time needs of innovative companies.

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