Enterprise Data Governance Model: Steps, Benefits & Challenges

Data governance is a collection of steps an organization takes to ensure data is accurate, usable, and accessible. It improves data quality and analytics, which is crucial for any data-driven enterprise. This, in turn, benefits better decision-making and business performance.

However, factors such as a lack of understanding of the business value of data governance, limited resources, and siloed data hinder effective data governance. Thus, having a framework such as an enterprise data governance model can guide your organization in complying with data governance policies. 

This article gives you comprehensive information on the enterprise data governance model and best practices to implement it. 

What is Enterprise Data Governance Model? 

An enterprise data governance model is a set of principles and practices for ensuring the accessibility, usability, and integrity of an organization’s data. It helps you establish a standard framework for designing and implementing data governance. 

There are different types of enterprise data governance models, such as: 

• Top-down: This is the enterprise data governance model in which your company leadership implements a data governance strategy. The strategy is then passed down to different teams and shared with the rest of the company.
• Bottom-up: In this enterprise data governance model, junior employees implement data governance practices, such as standardizing naming conventions, which spread to the senior levels of the organization.
• Center-out: In the centre-out model, the team or individual responsible for data governance sets the entire organization’s data standards.
• Silo-in: It is a type of enterprise data governance framework in which various departments come together to form a data governance strategy aligned with each group’s needs.
• Hybrid: A hybrid data governance model involves creating a centralized team to set the overarching policies and standards. At the same time, it allows individual business units to manage and govern their data within the established guidelines.

Steps to Create an Enterprise Data Governance Framework

Data governance is crucial for managing an organization’s data assets. Follow the steps below to create a good enterprise data governance model: 

• Define Your Data Strategy: A data strategy is a comprehensive plan for handling your organization’s data. It helps you understand which subset of your data is sensitive or impactful. You can prioritize such data and prepare a governance framework for it.
• Secure Executive Support: The top leadership must understand the significance and objectives of the enterprise data governance model, recognize its business value, and support the investment required to achieve these outcomes. To ensure this, you should clearly convey the objectives of the enterprise data governance model to your executives. Also, identify key stakeholders of your company, such as employees, IT staff, and investors, who play an important role in data governance. 
• Set Clear Data Policies: A data governance policy defines how your organization uses and manages its data. It gives an overview of different aspects of managing data assets, such as data quality, security, and access. Setting clear data policies will help you ensure that your data supports your organization’s overall goals and complies with relevant laws and regulations. 
• Establish Roles and Responsibilities: You should clearly define the roles and responsibilities of all stakeholders related to the enterprise data governance model. It will eliminate confusion and streamline the data governance process.
• Implement, Evaluate, and Adapt Your Strategy: After preparing a roadmap for a data governance framework, you should implement it to test its effectiveness. You should also regularly evaluate and review your enterprise data governance strategy to ensure it fulfills your business objectives. This will help you rectify and evolve your data governance framework according to the organization’s changing needs.

Benefits of Enterprise Data Governance Model 

Some of the benefits of data governance are:

• Data Democratization: Implementing the enterprise data governance model helps provide real-time data accessibility by clearly defining access control mechanisms. It promotes data democratization within your organization.
• Data Integrity: The enterprise data governance model enables you to implement standards and procedures for organizing data with minimum discrepancies. Thus, the data governance model imparts data integrity so that data is not compromised, corrupted, or modified inappropriately. 
• Effective Team Collaborations: Since the enterprise data governance model ensures data accessibility, it becomes easier for different teams in your organization to collaborate on the same datasets. This way, the teams can achieve organizational goals together and faster. 
• Increases Business Performance: Easy data accessibility also helps employees know how to collect and where to find important data. It improves their working efficiency and, cumulatively, your business performance. 
• Better Regulatory Compliance: An enterprise data governance model establishes standard data policies and procedures for consistent data handling across your organization. This ensures your business adheres to data privacy regulations like the European Union’s General Data Protection Regulation (GDPR). 

Challenges of Implementing Enterprise Data Governance Model

You can face various challenges while implementing an enterprise data governance framework, such as:

• Disparate data sources: Disparate data sources refer to different types or formats of data that reside in separate systems, databases, or file formats. They may not necessarily be designed to be integrated or compatible with each other. Such data is difficult to access and may pose a challenge in enterprise data governance. 

You can resolve this challenge by including federated computational governance in your data governance framework. This system incorporates feedback loops and bottom-up input from data users. To achieve this, you will have to invest in technological tools for data quality checks, data profiling, data cataloging, and data glossary creation. 

• Limited or misallocated resources: Lack or constraints of resources, such as technological tools, skilled human capital, and limited budgets, can become a hurdle for enterprise data governance implementation. 

To overcome the resource constraints, you can adopt certain practices, such as continuously upskilling your employees on data governance, using open-source and cloud-based tools, and curating expenses. To avoid excess expenditure, you can prioritize and implement a data governance framework in stages. 

• Lack of functional leadership: While organizations usually have executive-level officials involved in implementing sponsor enterprise data governance, they often lack a functional leader who oversees their day-to-day operational aspects.

You can establish a cross-functional data governance council in your enterprise to resolve this issue. This council can collectively oversee daily operations, with rotating leadership or designated subcommittees for specific tasks. You can ask all the teams to fulfill the data governance responsibility within their area. Further, you can also outsource certain data governance tasks for this. 

• Regulatory Compliance: Enterprises have to fulfill various regulations and compliance standards governing data privacy and security, such as GDPR, HIPAA, CCPA, etc. Compliance requirements can vary based on the industry and geographic location, adding complexity to data governance efforts. 

To create a comprehensive data governance framework incorporating relevant regulations, you can establish standardized data policies that address common compliance requirements across different regulations. You should also monitor any changes in data regulations and update your data governance framework accordingly. 

Best Practices in Enterprise Data Governance

Best practices used by organizations that have successfully implemented enterprise data governance programs include the following:

• Automate: You should use updated technological tools to automate your enterprise data governance model. It saves time and increases the efficiency of the model.
• Communicate: When implementing the enterprise data governance framework, you should communicate regularly with all stakeholders. This will align everyone with the program’s goals and objectives. 
• Training: You should arrange training workshops to educate your employees about data governance, its importance, roles, and responsibilities. This will help you build an efficient team equipped with proper data governance knowledge. 
• Monitor Progress: To track the progress of your data governance framework, you can define the necessary KPIs, conduct audits, collect feedback, and prepare reports. It will help you in updating your data governance objectives. 
• Secure Your Data: If your data is not protected, your entire governance framework can collapse instantly. Data breaches can potentially destroy your company’s reputation and lead to financial losses. To avoid this, you should protect the confidentiality and integrity of your organization’s data by taking steps such as access controls, encryption, and data loss prevention (DLP). 

Use of Hevo Data for Implementing Enterprise Data Governance Model

Data integration ensures that your data is accurate, consistent, and reliable, which is essential for enterprise data governance. It consolidates varied data and helps rectify any data inconsistencies. You can use Hevo Data to integrate data from multiple sources and streamline workflows for better compliance with data governance frameworks. 

Hevo Data is a no-code ELT platform providing real-time data integration and a cost-effective way to automate your data pipeline workflow. With over 150 source connectors, you can integrate your data into multiple platforms, conduct advanced analysis on your data, and produce useful insights.

Get Started with Hevo for Free

Here are some of the most important features of Hevo Data:

• Data Transformation: Hevo Data allows you to transform your data for analysis with a simple Python-based drag-and-drop data transformation technique.
• Automated Schema Mapping: Hevo Data automatically arranges the destination schema to match the incoming data. It also lets you choose between Full and Incremental Mapping.
• Incremental Data Load: It ensures proper bandwidth utilization at both the source and the destination by allowing real-time data transfer of the modified data.

Conclusion

This blog explains the enterprise data governance model in detail. It also comprehensively discusses the challenges associated with the framework and best practices for overcoming them. Hevo Data simplifies data integration through its built-in connectors, user-friendly design, and change data capture features. The platform helps in the proper consolidation of data to execute data governance. You can schedule a demo for Hevo today to leverage its features for robust data governance. 

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.

Share your experience of AWS Aurora Postgres to Snowflake integration in the comments section below!

FAQs

1. What’s the difference between Data Governance and Data Management?

Data Governance is a set of procedures and guidelines for ensuring your data is secure and aligned with your business objectives. Data management includes tools and practices for handling data and implementing the policies outlined by data governance.

2. How do we measure the success of data governance? 

Establishing key performance indicators (KPIs) can help you measure the success of your data governance program. You should define KPIs according to your organization’s corporate strategy and business goals. This will measure success in terms relevant to management and meaningful to the entire business.