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Data Integration

The Importance of Data Integration

Overview

In today's business environment, it's more important than ever to be able to fully leverage your organization's information assets through enterprise data sharing with internal teams and applications, clients, and business partners reliably and quickly.

How do organizations organize, store, and retrieve the information they need to streamline operations, make better business decisions, and forge better relationships with business partners? They use data integration – a combination of technical and business processes that combines data from many different sources into a central system that provides information for operations and business intelligence. A complete data integration solution provides data extraction from a variety of sources, cleansing and transforming data in preparation for loading into a destination database, and delivery of the data to a destination of choice such as a data warehouse or data mart.

Data integration can be a complicated process, especially if the data is complex and comes from unstructured or heterogeneous sources. There isn't a single data integration solution for all projects and the field of data integration is constantly evolving to accommodate the needs of different organizations. Depending on specific needs, different data integration approaches work better than others.

For most medium and large enterprises, data integration involves synchronizing both flat files and complex data in a variety of formats from different sources, both internal and external. Legacy and partner data may be in flat file, network, or hierarchical databases, while newer databases use relational data, which requires data integration software that can handle the requirements of both flat file data and complex data in order to store or extract it. If the project is not implemented properly, it can become a major drain on IT resources, affect the quality of the data, and result in major cost overruns and even project failure. Careful advanced planning is the best way to successfully overcome these issues.

Before starting an integration project, a thorough analysis of the characteristics and uses of data should be done. A model should be developed that will meet the needs of the business applications and other uses of the data. Communication and collaboration between business analysts and IT will help to develop a plan that works for everyone. Businesses are beginning to recognize that those who make business decisions should be involved in developing data integration processes to deliver what they need rather than relying on developers whose expertise is in perfecting systems but not in how the data from the systems is used.

Finally, the database developer needs to ensure that the process will work for all parts of the organization. Depending upon the complexity of the main database system and the sources and destinations for the data, it can be quite a challenge to build the necessary hierarchical structures.

In addition to building the right architecture, it is important to provide for scalability and extensibility as the organization’s data needs grow and change in the future. Failure to build these requirements into the system can result in costly fixes down the line if a solution for processing larger amounts of data than originally anticipated has to be retrofitted. Choosing a data integration software solution that has been developed to provide extensibility and scalability as data grows is essential.

Data integration costs are often escalated due to IT labor costs for project overruns caused by unanticipated integration problems, difficulties implementing data integration software, and labor and direct costs for data storage and maintenance.

A data integration tool that is high performance and scalable, yet intuitive and easy enough to use that business professionals can use it without needing to write code can go a long way towards enabling both developers and business users to work together to ensure the enterprise’s information is reliable and useable for everyone, at a reasonable cost.

Centerprise Data Integrator

Centerprise is a complete data integration solution that includes integration, transformation, quality, and profiling. One hundred percent managed .NET code provides a reliable, scalable, and secure platform and the flexible environment enables users to choose from multiple integration scenarios and control individual users' view of data integration. The high-performance parallel processing engine delivers the performance and scale needed to tackle even the most complex data integration projects. Centerprise’s impressive complex data mapping capabilities make it the best platform for overcoming the challenges of complex hierarchical structures such as XML, electronic data interchange (EDI), web services, and more.

Centerprise Data Integrator has an intuitive, user-friendly interface designed for ease of use by business information experts, enabling them to participate in data integration solution decisions and take responsibility for day-to-day integration tasks. Centerprise minimizes development risk, reduces time to production, and eliminates error-prone, hand-coded data conversion. Users get better business information because the software reduces roadblocks due to integration, quality, or availability issues. Automation features streamline data exchanges using scheduling, file drop events, and API calls. View the Centerprise product page for details.

Centerprise Data Integration Features

  • An easy-to-use environment to develop and test even the most complex integration flows
  • Connectivity to a vast range of sources – from applications and databases to files and web services
  • Data validation and profiling for full visibility into data composition and quality
  • Batch, near-real-time, and real-time integration intervals
  • A powerful, high-performance, parallel-processing engine that takes full advantage of multicore and multiprocessor hardware
  • Scalability to efficiently accommodate massive data volumes now and into the future
  • Extensibility and openness to add custom functions, destinations, sources, or transformations