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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

Enterprise Data Architectures (Logical)
Enterprise Data Architectures (Physical)
Data Life Cycle Management
Data Governance in Complex Environments
Metadata Registration and Data Lineage
Master Data Management
Data Warehouse Architecture and Design
Metadata Management
Enterprise Information
Data Cleansing & Migration
Next Generation Data Warehousing
Data Integration Technologies
Extract, Transform, and Load
STEP and PLCS Support
Material and BOM data Cleansing and Migration
Product Data Cleansing
Product Data Requirements
Interactive Electronic Technical Manuals
IETM Rendering Using Commercial Products
Federated Data Integration
Federated Data Warehousing
Commodity Standardization and Classification for Procurement Optimization
Component & Supplier Management
EAI Design & Implementation
Hub Design & B2B Standards
Financial Data Profiling Reconciliation
Process-Oriented Data Sourcing
Reverse Engineering to Support Data Integration
Data Policy & Data Management Planning
Data Readiness Planning