More Effective Business Intelligence With Data Governance

Evolution of Data Warehouses to Meet Business Needs

Data warehouses have had to evolve to maintain pace with the needs of the business community. The individuals who are the customers of the data warehouse team are becoming more sophisticated regarding their knowledge of the value of enterprise data assets.

• Increased Collaboration and Input:
  This results in demands for more input into how information is made available for decision-making.
• Business Intelligence as a Partnership:
  Business intelligence (BI) consumers are becoming partners with the teams that provide access to corporate data resources in a quest to influence how data is gathered, presented, and used.

Enhancing Business Analytics Through Effective Data Governance

Data Governance is a method with which organizations can improve the way they handle data and make it available for business-impacting analytics. The goal is to simplify the production of actionable data and reduce the time necessary to provide it to decision-makers.

Key Aspects of Data Governance for Success
A successful partnership between data providers and consumers relies on several critical aspects:

• Increased intelligence regarding enterprise data resources.
• Constructive collaboration between data consumers, architects, and stewards.
• A searchable catalog that simplifies the process of developing business intelligence solutions.

Addressing these items puts an organization in a good position to use its data assets to generate BI solutions.

Challenges to Creating Viable BI Solutions

Developing productive BI solutions is a complex process, fraught with challenges that hinder the transformation of raw enterprise data into digestible information for decision-makers.

Addressing Data Source Complexity

Organizations collect information from multiple data sources, and effectively managing these sources is critical. A well-documented process and appropriate tools are essential for understanding the specific needs of business analysts and eliciting requirements from the BI user community.

Identifying and Managing Data Sources

Technical teams must inventory and evaluate potential data sources to select the ones best suited to architect a viable BI solution. This selection process plays a pivotal role in the overall success of the BI initiative.

Ensuring Data Consistency and Relationships

To create valuable BI solutions, data consistency across sources is crucial. Coordination between data architects and producers ensures that similar data from different sources can be used efficiently. Training in identifying and managing data relationships enhances the utility of BI solutions.

Overcoming Challenges for Usable BI

Addressing these challenges increases the likelihood of crafting effective and actionable BI solutions from enterprise data assets.

The Role of the Data Catalog in Business Intelligence

A data catalog that serves as a repository of enterprise information assets is at the heart of processes designed to provide BI solutions to corporate decision-makers.

Key Data Governance Processes for Creating a Data Catalog:

• Surveying Data Storage Environments: Identifying and inventorying data assets.
• Profiling Data: Ensuring data quality, obtaining metadata, and identifying potential issues.
• Documenting Data Owners and Producers: Detailing how data is produced and its lineage.
• Classifying Data Elements: Populating a searchable glossary of business terms.

Benefits of a Data Catalog for Business Intelligence:

The resulting data catalog provides substantial benefits to the organization’s BI consumers. Having a centralized data repository gives teams a common starting point from which to browse information assets and determine which ones are applicable for analysis and reporting purposes.

Fostering Collaboration for Collaborative Data Governance

ER/Studio Enterprise Team Edition offers organizations a flexible collaborative tool with which to create a data governance foundation that will contribute to their ability to create productive BI solutions.

• Easy Design and Sharing of Data Models: ER/Studio makes it easy for teams to design and share data models and associated metadata across the organization.
• Data Asset Discovery: The tool can discover and document existing data assets, creating a logical starting point for governance initiatives.
• Centralized Data Dictionaries and Glossaries: These facilitate the communication necessary to develop valuable solutions for enterprise data consumers.
• Consistency Between Models and Databases: Teams benefit by maintaining consistency between models and databases, allowing everyone to speak the same language as it relates to corporate data assets.
• Enhanced Productivity: This in itself puts an enterprise in a position to use its information more productively.

An IDERA whitepaper by David Loshin that goes deeper into the issues of using data governance techniques to improve enterprise BI solutions is available and highly recommended.

It speaks to the importance of obtaining the right information to provide the most effective business intelligence to enterprise data consumers. Give it a read if you want to enhance the way corporate data assets are used to generate value for the organization.