Unissant

Utilizing industry-leading best practices in database architecture, modeling and data processing in Big Data, we manage over 180 Terabytes of bio-surveillance data for the Armed Forces Health Surveillance Branch (AFHSB), integrating over two dozen external data feeds in multiple formats and delivery methods.

At the core of any data solution, the basic data management capabilities need to be considered. In order to better utilize and manage information. Understanding your data is more than just defining an inventory of terms and definitions, it also includes capturing and aligning the rules that control its quality, structure, and access; discovering how information flows amongst processes and systems; and determining a common vocabulary across stakeholder groups. Unissant brings a structured approach to capturing and normalizing data requirements and semantics across stakeholder groups while allowing linkages back to individual business units. Unissant’ s services across various data management disciplines include:

 

Data Governance (DG)

Data Governance is the fabric that keeps a data strategy operational and relevant to an organization. Unissant’s Data Governance implementation services focuses on Governance Structures, defining Operating Model, rolling out Policies & Standards and Compliance Monitoring.

 

Data Quality (DQ)

Data Quality is integral to the data governance activities and is often the most significant pain point within an organization.  Unissant develops and implements data quality programs that are “fit for purpose”, meaning that data quality is balanced against the business needs, priorities and use of the data.

 

Metadata Management

Metadata is a discipline that spans business, operations and technology and is a key component of a successful data strategy.  Unissant’s Metadata Management services focuses on developing a Business Glossary/Metadata Catalog. We document Data Lineage by documenting the tools/platform maturity followed by Data Traceability which captures the linkage of data across the technology stack to assess the potential impact of a change across technology assets.

 

Master Data Management (MDM) and Reference Data

Master Data Management (MDM) and Reference Data Management are two distinct, but closely tied data management concepts.  Unissant considers this area as core and a highly important component of a comprehensive data strategy. Unissant’ s MDM service offering focusses on governance, systems, processes and content to define and institutionalize a well-managed framework for MDM by integrating all applications through a service layer and maintaining a master repository with embedded synchronization of data elements.

 

Database Auditing and Monitoring (DAM)

Database auditing and monitoring is essential for the integrity and security of a database. Unissant utilizes our DAM framework with industry standard tools to proactively identify structural changes or schema changes to enterprise databases. In addition, we implement best practices-based access management policies and procedures for enterprise resources thus maintaining and auditing access rights regularly.

 

Semantic Technologies

Enabling quick changes to an underlying schema without impacting the data itself is vital to ensure continuity of operations without massive downtimes. Unissant leverages an ontology, common semantic layer, that is de-coupled from the data itself that allows automatic discovery of new data and incorporates quickly into the semantic layer. It also facilitates data federation-allowing seamless integration across distributed data sources and data sharing across the environments

 

Advanced Analytics

Unissant leverages an industry analytics maturity framework to assess current capabilities and enhance and modernize existing dashboards and tools as part of the engagement. Our Advanced Analytics offering is further enhanced by the implementation of a Data Lake, by providing analysis across all types of data to gain true business insights, based upon the usage patterns for consumption by the business. Our advanced analytics offerings include:

 

Descriptive and Diagnostics Analytics

Looking at historical data (predominantly in relational databases) to gain insights into what happened and why.  We leverage traditional analytics tools such as SAS and R to perform core analytics, and present the results using BI tools such as Tableau, Data Robot, MicroStrategy or Qlik.

 

Predictive and Prescriptive Analytics

Facilitate deeper insights where organizations require more sophisticated capabilities to provide predictions and recommendations for best course of action. This includes cognitive intelligence utilizing Artificial Intelligence, Machine Learning and Neural Networks.

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