Data Service Applications | Silver Creek Systems

Data Service Applications Deliver Reliable Data

The traditional approach to representing business rules in data integration tasks has been to write custom code or scripts that are called from ETL or other DI tools. This typically results in a “brittle” integration with limited portability or reuse as well as long deployment times and high maintenance costs.

By contrast, Data Service Appliations (DSAs) are built in a code-free, drag-and-drop environment that is fast to build and inexpensive to maintain. Since DSAs use Data Lenses to interpret and standardize data, they recognize data quality issues and incorporate sophisticated exception management to integrate data between systems and assure data quality and reliability in the process.

DSAs are a strategic approach to automating product data integration. They are composite applications that capture business rules and apply them to complex data integration tasks. DSAs can be reused with any enterprise system or Information Supply Chain. They incorporate:

Capture buiness rules to perform complex data integration tasks

Expert Processes – DSAs incorporate the ‘expert rules’ and business logic that manipulate, transform, and validate product data for consumption in other systems. This includes complex logic as well as the ability to access external systems for reference, enrichment and decision-making. Common processes that require ‘expert’ knowledge and that can be fully automated within a DSA are:
- Match & Merge – identify duplicate items from disparate sources with disparate formats and structures.
- Enrichment – access external sources to fill gaps in existing data.
- Alternates & Substitutes – identify functional equivalents and related items.
- Custom Publication – deliver data in any format, language or structure.
- Extract & Load – simple or complex extraction of data from enterprise systems to deliver a complete data integration environment.
Data Quality Assurance – DSAs monitor data quality at an item level and incorporate exception management processes to actively manage and quickly resolve product data quality issues. They not only identify data quality issues, but quickly resolve them to ensure that only data of known quality and consistency is passed between systems.
Data Governance – By monitoring data quality at an item level, DSAs can deliver real-time data quality alerts as well as aggregate quality metrics to form the basis of a closed-loop data governance process.
Loose Coupling & Easy Integration – DSAs are called as a service by any system or user. They are standards-based for easy incorporation into SOA (Services Oriented Architecture) or any other data integration scenario.

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