Data Architecture and Data Governance

The GITS Data Management Big Picture

A comprehensive Data Architectural program may potentially have a lot of moving parts. It is important to understand the overall landscape in order to determine what would need to be addressed when. GITS provides an ‘ala carte’ method of developing Roadmaps and Frameworks within the context of implementing Data Architectural Framework Components.

corporateDATA ARCHITECTURE AND DATA GOVERNANCE CHARTER/POLICIES AND STRATEGY

When Business Requirements and Corporate Policy Documents are written, it is important to demonstrate, when possible, that Business and Technical Resources are keeping an accurate record of what was done and why. It is their joint responsibility to track how these Policies and Charters are being realized within the context of the GITS Data Management Complex. GITS will analyze your Policies and Strategies and identify which Policy documents can be improved, and demonstrate how to document which Policies are being followed.

socialUNSTRUCTURED AND BIG DATA MANAGEMENT

There are two areas of Data Management that do not follow normal conventions. These areas are referred to as Unstructured and Big Data Management. GITS will provide methods, tool sets and ‘tagging’ methods for managing data in these two areas in accordance to your needs.                                                                                       

                                                                                            STRUCTURED DATA MANAGEMENT
newstructureddataGITS will provide ‘Best Practices’ Data Architectural methods for managing Structured Data. Our resources will provide guidance and knowledge transfer on effective Data Acquisition techniques, Data Model Standardization, Process Modeling methods, Meta-Data Management, and on developing a Technical Architecture Design to accommodate your Structured Data needs.

 

 

 

 

                                                                                               ODS/DATA WAREHOUSING/BI CONCEPTS/DATA ANALYTICS

data-qualityGITS will provide methods and procedures that are aligned with best practices for determining the best Design Paradigm (i.e., Operational Data Stores (ODS), Data Warehousing, Data Vaults, Canonical Model-Based Storage Structures, etc.) that is germane to Business Intelligence. The methods and procedures that are provided will facilitate decision-making based on usage patterns. These Design Paradigms will allow decision-makers to view necessary information on Dashboards by pre-packaging data structures that will allow the end-users to visualize Data Analytics and selected Metrics.

BUSINESS ATTRIBUTE-DRIVEN DATA QUALITY MANAGEMENT

All Business Attributes are not created equal. Different items of data may require different levels of Quality Management, in terms of content and timing of delivery. GITS will provide a comprehensive Data Quality Management Program which addresses the following items:

  • Quality Control/Quality Assurance Policies
  • Quality Control Planning
  • Simplified Quality Control Metrics And Reporting
  • Proactive Quality Assurance

GITS will provide a Meta-Data Repository and demonstrate how to capture these parameters effectively while providing a data-driven methodology for managing Data Quality.

DATA MASTERING

Master Data Management (MDM) has become a hot topic among Data Architects lately. The concept is predicated on the idea of creating a set of data items for which there are no existing Data Quality problems – hence, the resulting collection data items will comprise a ‘perfect’ record of information for a given business subject (e.g., Product, Customer). These perfect records of information are often referred to as ‘Master Records’. The GITS Methodology includes the concept of ‘Data Mastering’, but we also include all activities that are needed to truly ‘Master’ your information. Examples are below:

  • Dissemination and cutover to the usage of ‘Master Records’
  • Match-Merge Methods
  • Conformance Methods for Incoming Data
  • Management and Reporting of Data that cannot be conformed (i.e., ‘Outliers’)
    • Prepackaged Data Objects (e.g., Decision Support, BI)
    • Visualization Methods
  • De-Duping (i.e., How to manage duplicate data)
  • Reconciliation (i.e., How to deal with information that has already been consumed)
  • Making Adjustments to Data Post-Mortem (after the information has been published)
  • Data Integration Methods and Paradigms

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