DB Luminous™ Data Governance Management

DB Luminous™ is a web-based metadata repository that provides Business, Technical and Compliance leaders a uniform approach to effective data dictionary management. It provides organizations with the capability to plan a perfect data integration, data quality, data governance and other data management challenges and ensuring security of Data, all from a single interface.

A Web-based solution

DB Luminous is a web-based solution that analyzes the database schema as building blocks of functional data elements in addition to analyzing data relationships. DBLuminous manages these elements as Unique Data Element™ (UDE). UDE simplifies the complex modeling and normalization techniques that typically create layers of complexity for non- technology Stakeholders. The Unique Data Element methodology gives immense flexibility for Functional, Technical and Compliance Users to defuse all of the inherent complicated database data definition relationships.

Using UDE we strategically created multiple options to utilize building blocks of data to understand the functional operation of Enterprise applications, identify gaps in meeting Governance, Risk and Compliance (GRC) requirements and show anomalies in data element construction. The UDE methodology can be used to identify the proper dimensionality for dimensional modeling. Since we have consolidated the relational construct, it is easy to identify the dimensions required to analyze the relevant data.

Tactically, DB Luminous provides numerous ways to manage single or multiple database instances. Some of the options include Metadata change tracking, Impact analysis, Mismatch analysis, Redundancy analysis, Schema audit and Schema compare.

DB Luminous uses auto-alerts to provide active monitoring and proactive database management, resulting in better database control. With built-in access control, DB Luminous securely supports separation of duty for different user groups. DB Luminous tracks and records every change that affects data elements.

Companies managing data GRC requirements, supporting multiple life cycles of ERP, BI, Data Warehouse, CRM, Portal and Social Media needs across different Database environments with separate Change Management systems may discover significant value in using DB Luminous to create a Corporate Data Dictionary repository.

Benefits of DB Luminous

DB Luminous is complemented by a comprehensive dashboard that provides the user with the ability to instantly view the environment’s intrinsic details. The dashboard reveals a status of all data dictionary processes.

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.

Features of DB Luminous

Data Operations & Governance
  • Data Development
  • Metadata Management
  • Data Quality Management
  • Data Security Management
  • Document & Content Management
  • Data Warehousing & Business Intelligence Management
  • Knowledge on modules & related tables
  • Knowledge reference on dependent objects
  • Overall information flow
  • Functional analysis
  • CRUD analysis
Business Analyst
  • Overall information flow
  • Functional analysis
  • Impact analysis
  • Interface information
System Analyst
  • Impact analysis
  • Mismatch analysis
  • Entity relationship diagram
  • CRUD analysis
Quality Analyst
  • Conceptual view
  • Expose anomalies
  • Entity relationship diagram
  • Establish DQ process
Project Manager & DBA
  • Establishing standards for database assets
  • Schema audit and audit alerts
  • Release management
  • Database monitoring
IT Heads
  • Easy maintenance of data assets
  • Gap analysis on enhancements
  • Less worry on team attrition and training
  • New resources
  • Submission of IT audit reports before time

Frequently Asked Questions

Microsoft .Net Framework 2.0 SP2 & above, SQL Native Client and MS SQL 2005 & above for Client version. Microsoft .Net Framework 3.5, IIS 5.1 (or) IIS 6.0 and Browsers like Firefox 3.0.7 or Internet Explorer 7.0 (Preferred).

DB Luminous (DBL) is designed to be used on multiple platforms. DB Luminous is independent of the platform of the application and the database. Currently DBLuminous has the ability to support major databases that include Oracle, MS SQL, Postgres, MySQL, Sybase and DB2. However, DB Luminous requires .NET framework and SQL database to host the metadata.

Yes, DB Luminous can be configured to an application having multiple schema / database or databases from different applications. For example, if an ERP has separate databases for each of its domains such as Production, Sales, Accounts, and Purchase etc. then all of it can be configured under one application. DB Luminous will identify this as a single “schema set “. Extending the schema set concept, a schema set can be maintained for a database from multiple environments such as Development, Testing, and Production. This technique is very efficient for migrations and monitoring database across environments for consistency.

The limit to the number of database is based on the licenses purchased (DB Luminous is licensed for number of databases) and there are no limitations to the number of databases that can be configured as long the required licenses are available. However, there is a soft limit of 99 databases for a group to allow usability of the organization.

Yes. Reporting is the key component of DB Luminous. There are number of pre-formatted reports available as part of out of the box tool implementation. The latest information about the metadata will be available at runtime. Further, DB Luminous comes with a reporting engine, which can also be used to create additional reports similar to BI tools.

DB Luminous only stores the information about the metadata and does not retain the actual data content within a Database.

DB Luminous is designed to function without affecting the performance of the database and is not intrusive. DB Luminous connects to the application database, only on demand, to collect, track metadata and other statistics as required. DBLuminous does not use any sniffers that are triggers that cause performance degradation of the database.

Identifying the elements from disparate sources is the key aspect in designing an effective Data Warehouse. The performance and the effectiveness of the data warehouse depend on carefully choosing these elements to form the dimensions required for the data warehouse. Further, understanding the unique elements that have similar functional implications / meaning across multiple data sources are challenging in complex warehouse architecture. Building a metadata repository using DB Luminous (DBL) will provide the required knowledge of various databases and its elements. The Unique Data Element (UDE), Functional grouping features of DBL will allow you to group and analyze the elements of similar construct. These handy features significantly reduce time in creating a data warehouse. DBL is also useful as DW evolves to cater the constant requests from users to enrich the data. With DBL, it is easy to identify the source and granularity to enhance DW as needed.

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