Summary and Overview

  • Organizations face many data challenges related to data quality, data integration, metadata management, and the list goes on.

  • Implementing a data governance framework helps organizations develop a data-driven approach to managing enterprise data, support business intelligence/data analytics needs, and ensure broader visibility.

  • At the same time, several challenges exist when implementing a data governance program, leaving organizations to implement the right data governance framework and policies to help mitigate risk.

  • Structure and culture are key components of data governance.

  • Corporate and personal data need to be protected and are a key component of data governance success.

  • Evaluating these 9 challenges can help any data steward, cross-functional team, and key stakeholders address potential pitfalls proactively.

The Importance of Enterprise Data Governance

Data governance is the process of managing and protecting data throughout its lifecycle. It involves establishing policies, procedures, and standards for how data is collected, stored, used, and shared. This requires systems that are complex to be put in place by several stakeholders across the organization. Many organizations look at selecting the right software to implement a framework. The reality is that for data governance success, companies need to ensure they have the right toolsets, processes, frameworks, and people. Basically, data governance is complex at the best of times. 

This blog looks at some of the challenges that organizations face and what organizations need to consider. Each organization may require a different approach to mitigate risk.

Evaluating Data Governance Challenges

There are several challenges associated with data governance, including:

  1. Data Quality: Ensuring that data is accurate, complete, and consistent can be a significant challenge, especially when dealing with large amounts of data from multiple sources. Organizations need to develop processes to proactively identify data quality issues as they occur. This is one of the reasons data observability is becoming so important for organizations that need to make sure they have quality data within their data sources and as part of their workflows.

  2. Data Security: Protecting data from unauthorized access and ensuring that data is secure throughout its lifecycle is a major challenge. This includes ensuring that data is protected from cyber threats such as hacking and data breaches. Irrespective of where data resides, there are potential challenges to ensuring security and organizations need to put in processes to meet regulatory compliance and make sure they mitigate broader security risks to protect sensitive data. This is also important for overall data management.

  1. Data Privacy: Ensuring that data is collected, used, and shared in compliance with relevant privacy laws and regulations can be a complex and time-consuming task. This should be a part of a broader security strategy.

  2. Structure: Establishing an effective data governance structure that includes roles, responsibilities, and decision-making processes can be challenging. Engaging the right people and making sure stakeholders assume ownership of data will help organizations create a strong structure. This includes defining business needs, the types of data assets and amount of data to ensure better overall data stewardship.

  3. Culture: Creating a culture of data governance within an organization can be a challenge, as it requires buy-in and participation from all levels of the organization. In many cases, this requires a top-down approach. In many cases this means aligning the data governance policies developed with the organization’s overall data strategy.

  4. Costs: Implementing and maintaining a data governance program can be costly, and organizations may struggle to justify the costs associated with data governance initiatives. Doing so requires understanding the positive outcomes that come from strong data governance and tying those to broader business benefits.

  5. Scalability: Organizations that are growing rapidly may find it difficult to keep up with the scalability requirements of their data governance program. This requires a commitment managing data governance as part of all data initiatives so that growth occurs slowly and can be supported. 

  6. Integration: Integrating data governance with existing business processes and systems can be challenging. It is complex and time consuming. Stakeholders need to understand that it takes time and work to achieve.

  7. Metrics: Measuring the effectiveness of data governance program can be challenging, as it is often difficult to quantify the benefits of data governance initiatives. Organizations should use analytics and internally adopted business intelligence solutions to monitor the success of data governance initiatives. Additionally, it can take time to identify metrics to assess the success of the solution. Additionally, data governance solutions can help identify the right metrics.

For more insight into data governance and requirements, check out these blogs:

Data Governance Framework Policy - What you need to know

ETL for Better Governance and Data Security

Overall, organizations can face many challenges or not have many depending on how the data governance initiative is managed and how an organization develops their governance framework. Successful data governance requires a lot of work and dedication, which is why it is important to develop the right framework for the organization and address these potential challenges before they occur.