Data Integration

Data Integration
Having bad data in your organization can be a criminal offense. No kidding. Under the Sarbanes-Oxley act, executives may be held criminally liable for reports that are inaccurate due to incorrect data. Data Integration mitigates data problems by aligning data from different sources so that it is complete, accurate, and timely.
During the last decade, organizations around the world have invested billions of dollars in large Information technology systems such as Enterprise Resource Planning, Customer Relationship Management, and Supply Chain Management. Unfortunately, many of these projects were limited in scope and left fragmented technology landscapes in their wake. The result is that a typical business’ IT portfolio consists of multiple systems supporting different (and in many cases, the same) business processes.
Enterprise Integration, Inc. has developed its own proven methodology and uses the market-leading tools to present a single and accurate view of data across all business processes, regardless of the originating system and organization. We also understand that Data Quality is an essential prerequisite for Data Integration. Our proven experience with technologies such as Data Quality Assessment; Master Data Management; and Extract, Transform & Load eliminates the worry that poor data quality will erode the benefits of Data Integration in your organization.
Your business can have a terrific strategy for success and be extremely efficient in its processes, but still have terrible problems if the data it runs on is fragmented, inaccurate, incomplete, or unavailable. True competitive advantage comes from using the Data Integration, like the methods shown below, in conjunction with Management Integration and Process Integration to create a truly holistic Enterprise Integration Solution.
Data Integration Methods
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Enterprise Data Architectures (Logical)
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Enterprise Data Architectures (Physical)
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Data Life Cycle Management
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Data Governance in Complex Environments
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Metadata Registration and Data Lineage
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Master Data Management
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Data Warehouse Architecture and Design
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Metadata Management
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Enterprise Information
Integration
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Data Cleansing & Migration
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Next Generation Data Warehousing
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Data Integration Technologies
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Extract, Transform, and Load
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STEP and PLCS Support
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Material and BOM data Cleansing and Migration
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Product Data Cleansing
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Product Data Requirements
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Interactive Electronic Technical Manuals
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IETM Rendering Using Commercial Products
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Federated Data Integration
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Federated Data Warehousing
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Commodity Standardization and Classification for Procurement Optimization
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Component & Supplier Management
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EAI Design & Implementation
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Hub Design & B2B Standards
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Financial Data Profiling Reconciliation
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Process-Oriented Data Sourcing
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Reverse Engineering to Support Data Integration
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Data Policy & Data Management Planning
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Data Readiness Planning
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