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Data Quality Services

How good is the data that you receive, use, and send to others?

  • Do you have stand-alone systems and business processes, because of data flow issues between applications?
  • Can your management trust the data provided in a variety of reports?
  • Do you have data problems whenever a new version of software is released?
  • Can you rely on the data you’re getting from other sources?
  • Do you have a way of tracking the quality of data in your databases?
If your organization depends on data, consider the data quality issues that impact your organization’s reputation, productivity, efficiency, and success.

Symbolic is Raising the Bar on Data Quality
Symbolic provides a full range of data quality consulting services and uses a patent-pending tool, DATAMRI®, to monitor and provide metrics on the quality of data that is being presented to users, exchanged between systems and enterprises, and provided in information products.


These non-invasive inspections of data, create a condition report that details its findings, grades the data so that improvements or degrading of data quality can be easily and readily recognized, and maintains an historical record database that can be used to perform root cause and other types of analyses. Symbolic Data Quality Services include:
  • Content Validation - Analyze the content of each element in a data set against pre-determined business and structure rules. Validations review for both technical viability (e.g., data type, format, size) and content quality (e.g., valid values for each data element).


  • Product Version Analysis - Compare files or databases to determine where they differ and where they match, including analysis of the delta results. This can be done to compare new versions before implementation or to compare different but related databases or files, e.g., order to bill of lading.


  • Data Quality Metrics - Incorporate into an analysis of a file(s) or database(s) one or more measurements that can be used to provide feedback to the development or business process, perform root cause analysis, track trends, and determine accountability.


  • Information Products Grading - Incorporate into an analysis of a product (e.g., an output report) a grade based on pre-determined rules that provides the recipient with metrics by which to determine the data quality prior to acceptance and/or prior to populating recipient databases.


  • Data Harmonization - Fuse data from different sources (e.g., part numbers in an order against a master part list) and based on pre-determined rules, correct conflicts in the data.


  • Content Control Totals - Add control totals to your files so that you will have greater confidence that they’re complete and accurate as they’re processed through subsequent steps.


Data Quality Applications

Applications of DATAMRI® in some real world situations include:
  • Validate the contents of data files for exchange between systems, for use in applications, for use on websites, etc.
  • Determine differences between versions.
  • Determine the differences between an As-Is and To-Be system architecture.
  • Validate that the database system tables are compliant with the physical schema and the data model is compliant with the enterprise data model.
  • Validate that data in a database is accurate and complete.
DATAMRI® in the U.S. Government

Symbolic’s Data Quality Services are used in the Department of Defense, especially in support of the organization’s efforts to develop architectures, which are used for the acquisition and interoperability of systems for the battlefield that support the digitized warfighter. Examples of our efforts include:
  • Data Modeling – Product Version Analyses, Data Content Validations of the Core Architecture Data Model (CADM) used as the meta database for weapons systems development. Not only used to confirm data model changes, but also used by the architecture developers who need concise feedback of any model changes that will affect their systems development efforts.
  • Architectures – Product Version Analyses against the Data Model to confirm the architecture uses the data model correctly so that interoperability issues are caught during development and not in the testing and deployment stages. This includes complex Modified Table of Equipment (MTOE) to MTOE and MTOE to System Architecture comparisons.
  • DoD Architecture Repository System (DARS) –Content Analyses of a variety of DoD architectures being stored in DARS to ensure that they meet DoD quality standards.
  • Tactical System Initialization – Content Validation to ensure the quality of data that is used to initialize digital warfighter systems.

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