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More Effective Business Intelligence With Data Governance

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.

This results in demands for more input into how information is made available for decision-making. 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.

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.

In particular, several aspects of data governance are critical to a successful partnership between data providers and consumers. Greater value to data consumers comes from:

  • 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

As with many worthwhile undertakings, developing productive BI is easier said than done. Some substantial challenges can make it difficult to transform raw enterprise data assets into information that can be digested and used by decision-makers. Some of the main challenges are found when dealing with the multiple data sources with which organizations collect information. 

  • It is essential for data producers to understand the specific needs of business analysts. This understanding is facilitated by the existence of a documented process and tools for eliciting the requirements from the BI user community.
  • Identifying and managing the sources of information incorporated into the solution is critically important. Technical teams should inventory the potential data sources and select those that can be best used to architect the respective BI solution.
  • Ensuring data consistency and relationships between terms is necessary for BI solutions to be valuable to prospective users. Coordination is needed for similar data produced from various sources so it can be used efficiently by data consumers.  Data architects and those providing BI to consumers need to be trained in identifying and managing the relationships between related pieces of information. 

Addressing these challenges makes it more likely that usable BI solutions can be crafted from enterprise data assets.

The Role of the Data Catalog

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. Data governance techniques and processes that contribute to the creation of a data catalog include:

  • Surveying all data storage environments to identify and inventory data assets.
  • Profiling data to ensure its quality, to obtain metadata, and identify any possible issues with the information.
  • Documenting data owners and producers along with details on how the data is produced and its lineage.
  • Classifying data elements and populating a searchable glossary of business terms.

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.

ER/Studio makes it easy for teams to design and share data models and associated metadata across the organization. The tool can discover and document existing data assets, creating a logical starting point for governance initiatives. 

Centralized data dictionaries and glossaries facilitate the communication necessary to develop valuable solutions for enterprise data consumers. Teams benefit by maintaining consistency between models and databases, allowing everyone to speak the same language as it relates to corporate data assets.

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. 

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