The initial stage involves a collaborative effort among the Stakeholders to define the data dictionary according to the company’s data Governance policy. The process involves closely working with users when building the information repository.
Unique Data Element™ : An exclusive reference is assigned for each unit of information used in the application. It is a standard reference and an easy way to understand the data dictionary database content.
Functional Grouping : Functional, Technical and Compliance User’s have a comprehensive and uniform view of Enterprise applications through Unique Data Element™ which acts as building blocks of the data dictionary database.
FDB Object Definitions & Grouping : The definition and Data Objects grouping feature enables Users to document the data dictionary database more effectively. The process enables Technical teams to bring more clarity about the database objects and their usage.
The comparative analysis feature is built to draw out the differences that exist between two databases and also ones that exist within a single data dictionary database.
Impact Analysis : The impact analysis feature allows carrying out any planning and executing Regulatory changes or column modifications within the data dictionary. Specific information present in the application can be easily referred using this feature.
Mismatch Analysis : This analysis helps to ensure data integrity and adherence to data dictionary quality. Mismatch Analysis also helps ensure consistency of data representations within the data dictionary database. Mismatch Analysis can be particularly important for companies which audit their Database applications.
Schema Comparison : The schema comparison is an effective method to compare two different schemas for an application and to understand their differences. It also allows the upgrading of an out-of-date data dictionary database.
Data Comparison : Data Compare compares the data present within a table in two different schemas in order to retrieve information on data missing/added between them. Data Compare can be particularly helpful in a data dictionary Audit.
Process Impact in Database : Captures the database size at each process level and further analyzes the impact in the data dictionary by process. Impact analysis delivers strong outcomes for companies experiencing data growth issues.
Dot Net Source Code Comparison : A feature that allows users to compare two different release folders in order to verify the release updates. The feature allows file level comparison of code for tracking changes within the data dictionary code.
The audit feature provides information about the changes taken place within the database/schema. The feature is also capable in locating the root cause if utilized effectively.
DML Audit : An audit and analysis of CRUD (Create/Read/Update/Delete) done on any table and the related database source code, to help to do an in-depth analysis faster.
Schema Audit : To bring about schema changes since the last audit. Audit alert emailing can also be configured using this feature. The auto-alert capability provides enhanced control of the data dictionary.
The control feature helps in identifying the role and access of users using the data dictionary.
Role based Access Controls : The access to the database can be controlled based on “need to know” or “need to change” basis. Specific access rights can be assigned to stakeholders like developers and testers.
