header-cs-dm

Case Studies

Learn more about the innovative work and services Symbolic Systems has provided its clients. These case studies provide an informative summary of the solutions we designed for our clients’ specific need.

Data Management
Data Management


 
Development of a Biometrics Glossary, Data Dictionary, and Logical Data Model
biometrics

Three essential data products—a glossary, a data dictionary, and a logical data model—are needed to support a key goal of the Biometrics Identity Management Agency: define and standardize an architecture that will meet DoD’s current and future biometric requirements in support of business and warfighter needs. Together, the three products will help establish and promote a consistent language for the data that are used and exchanged within the DoD community.

Read more...
   
Data Management

Data Management is the practice of ensuring that data meets the organizational needs in terms of availability, accuracy, timeliness, and quality in ways that encourage horizontal as well as vertical sharing of data within the enterprise. . At Symbolic, we work with clients at all phases of data management from data strategy through the entire data life cycle to ensure that their data management goals are achieved.

Data Strategy and Governance - Our professionals can work with you to create a framework of roles and responsibilities for both the business and data professionals. This framework will formalize guidance and behavior over the definition, production, quality, and usability of information and information-related assets. Implementing a data governance framework is the first step in an overall data management strategy.

Data Architecture, Analysis Design - Symbolic takes a holistic approach when developing solutions for data management. We use best practice methodologies and appropriate technologies to ensure that the solution is delivering the right data to right level of the enterprise effectively and efficiently. This includes enterprise data modeling, function/data analysis, logical data modeling, and model management.

Data Quality - Symbolic uses the Total Data Quality Management methodology developed at M.I.T. to improve data quality and ensures that our clients: (1) users (customers) of data are involved in improving data quality, (2) predetermined requirements for excellence are defined in terms of measurable data characteristics, and (3) data conforms to these requirements. We employ a rigorous methodology to ensure that the quality of data can be maintained throughout its’ lifetime.

Data Integration - Aligning, reconciling and coordinating your data across disparate systems, components, and data sources will help you achieve your IT goals. The Symbolic data integration approach is to fuse the data by performing a semantic / syntactic analysis, normalizing the data, mapping source data elements into a target data model and then loading the data into the physical storage space.

MetaData Management - Managing all of your data requires a strategy that includes the development and execution of programs and policies for the discovery, vetting, housing, sustaining, and distribution of the characteristics of the data utilized by the enterprise. Symbolic can help you develop and implement a comprehensive metadata management strategy.

Data Interoperability - Today, many Enterprise data inter-operability efforts struggle with how to solve the problem. Achieving data interoperability is a language, data and process issue more than a technical one. Symbolic’s Data Interoperability Enablement Framework (DIEF) provides a blueprint of operational components and their relationships that is necessary to comprehensively but efficiently define the information to be exchanged and to organize the processes to execute an interoperability effort in a timely manner.