7 Data Governance Challenges & How to Beat Them

Data governance may seem fairly straightforward, and for some companies, is even considered a nice-to-have. But Forrester research found that less than 10% of organizations have advanced data insights – and the reason is insufficient data governance.

If data governance is such a well-known concept, why is it so difficult to put into practice? In this blog, we’ll explore the top seven data governance challenges, and best practices for overcoming them.

What is Data Governance?

Before we dive into data governance challenges, it’s important to clarify what data governance is, and what it is not.

Data governance refers to the overall management of data availability, usability, integrity, and security throughout the data lifecycle, including processes, policies, standards, and technologies. It establishes an accountability framework for data-related matters within an organization, so stakeholders can effectively manage data, maintain its quality, adhere to data compliance regulations, and mitigate risks, all while still deriving value from their assets.

It’s worth noting that data governance is not a one-off endeavor. A strong and enduring data governance framework is one that is constantly evolving in tandem with your organization’s data needs, and being optimized in order to unlock new use cases.

Top 7 Data Governance Challenges

Despite the benefits that data governance delivers, navigating its complexities and ensuring an effective implementation requires careful planning and alignment. These are seven of the most common data governance challenges.

1. Lack of Leadership & Alignment

Like any business objective, standing up effective data governance and management starts with securing organizational buy-in, setting clear expectations, and defining success metrics. Without strong executive sponsorship and clear direction from leadership, establishing priorities, allocating resources, and fostering a culture of data governance will likely be met with resistance, confusion, misalignment, or a combination of the three. As a result, collaboration and forward momentum will stall, hindering the development of a comprehensive data governance strategy.

2. Complex Data Ecosystems

As self-service data use and distributed data architectures like data mesh grow in popularity, ecosystems are becoming increasingly complex to manage and govern. A proliferation of data sources, formats, and users require scalable controls, and legacy manual processes can no longer keep up. Enforcing security and governance controls platform-by-platform is a time-intensive task for data engineers, and is prone to inconsistency or error. As a result, it’s easy for sensitive data to slip through the cracks, exposing it to risk or misuse with little visibility or oversight.

3. Data Silos & Lack of Visibility

Data silos have a negative connotation, and with good reason. Storing data in isolated silos across different systems or domains impedes accessibility, integration, and consistency. This leaves all stakeholders – from data consumers to governance teams – questioning where data lives, whether it’s accurate, and who manages it. The ensuing fragmentation leads to inefficiency, duplication, and compromised decision making. Without visibility into data usage, lineage, and quality, it’s difficult to implement a modern data governance program that mitigates and promptly addresses issues.

4. Data Quality Issues

Inaccurate, incomplete, or inconsistent data undermines the trustworthiness of information used for decision making and analysis. Whether data quality issues arise from data entry errors, legacy systems, or a lack of standardized processes, they are a blocker to effective data governance. Failure to address data quality issues may result in misguided business decisions, diminished operational efficiencies, and noncompliance with data security regulations.

5. Data & Metadata Mismanagement

Properly and efficiently managing data and metadata helps your systems and processes run smoothly. On the flip side of the coin, mismanaging these assets can derail data-driven initiatives. The sheer volume and variety of data types makes it difficult to maintain accurate, organized, and up-to-date data and metadata repositories. Additionally, effectively governing these assets often requires a dedicated team member who is responsible for managing their upkeep and ensuring interoperability across systems. Without clear accountability, mismanagement is bound to arise.

6. Compliance Concerns

Compliance concerns are a central driver of data governance efforts, but the ever-evolving regulatory landscape sets complex requirements that must be met in order to ensure data security, privacy, and ethical use. This is true not just for major regulations like the GDPR, HIPAA, and CCPA, but also industry standards, contractual obligations, and internal rules. Interpreting and implementing guidelines from compliance laws and regulations often calls for an investment in processes, technologies, and expertise to bridge the gap between legal language and enforceable controls. Failure to meet these standards may incur legal penalties, reputational damage, and lost customer trust, which has long-term implications for bottom line results.

7. Haphazard Security Controls

As with data quality and management, risks abound when data security controls are applied inconsistently. This is often the case with homegrown or platform-native controls, which require more manual upkeep. Since these approaches cannot be easily and consistently deployed across all platforms and users, particularly at scale, they weaken governance, risk, and compliance frameworks rather than strengthen them. As a result, gaps in enforcement leave data vulnerable to breaches, unauthorized access, and malicious activity.

Best Practices to Overcome Data Governance Challenges

Despite the data governance challenges that most organizations face, there are several tactics for avoiding them and putting in place a resilient governance strategy.

Address Scalability Issues

It’s clear from the management and complexity challenges mentioned above that scalability is a glaring issue in modern data stacks. Without the ability to easily scale systems, processes, and controls, data access will be delayed while data engineers spend a disproportionate amount of time trying to manage it.

Embracing automation, implementing dynamic controls, and minimizing the need for human intervention are the best ways to address data governance scalability issues.

  • Automation streamlines repetitive tasks such as identifying sensitive data, responding to data access requests, or creating roles and policies for each new user and data source, all of which take time that data teams could be spending on more innovative and business-driving tasks.
  • Dynamic attribute-based access control (ABAC) is the method of choice for forward-looking enterprises seeking speed, flexibility, and adaptability within constantly changing data ecosystems. An ABAC policy grants or restricts data access at query time based on various context-aware attributes, making it the most scalable, agile choice for managing access and meeting data governance objectives.
  • Ideally, automating dynamic controls will inherently reduce the need for human intervention in data governance implementation. While some degree of human oversight is still required to ensure that systems and processes are running as intended, minimizing reliance on manual and/or ad hoc tasks will reduce the risk of inconsistency, avoidable errors, and inability to scale.
  • Increase Visibility

    If data silos and a lack of transparency are blockers to data governance, the best resolution is increasing visibility. But in increasingly popular decentralized data architecture models, how can this be done without causing headaches?

    Centralizing data policy enforcement and data monitoring in a single platform that is not tied solely to an individual storage, compute, or analytics tool creates visibility across all your data – including how it’s been accessed, used, shared, and protected, both in transit and at rest. This breaks down data silos and makes it easier to govern data throughout its lifecycle, regardless of where it lives.

    Additionally, with increased visibility comes a better ability to proactively identify and respond to threats. This not only helps mitigate compliance and data quality concerns, but also provides accountability and trust that are key to effective data governance.

    Incorporate the Secure By Design Principle

    Similar to Privacy by Design, the Secure by Design principle is the idea that security measures should be integrated into every stage of the data lifecycle from the start, rather than retroactively. The goal is to stay ahead of and mitigate risks, using tactics such as sensitive data discovery, data access control, dynamic data masking, and data monitoring.

    Since data governance pertains to data at any point in its lifecycle, the natural connection to Secure by Design is clear. Enlist your security experts, data architects, and legal advisors to align on how to create a data governance framework that incorporates Secure by Design.

    Foster a Governance-Focused Data Culture

    Closely associated with Secure by Design is a governance-centric data culture. In other words, you need to ensure that anyone who has a hand in data use at your organization understands and shares a commitment to upholding data governance principles.

    This is important because even the most advanced tech stacks will fail to enable data governance without the buy-in from data users. Fostering a governance-focused data culture goes beyond simply writing and enforcing processes and policies; it requires a concerted effort to instill a mindset of accountability, responsibility, and continuous improvement with regard to data management.

    Since change is often met with resistance, it’s important to identify your champions across functions, set clear roles and responsibilities, and provide the necessary training to ensure that data governance efforts are effective and collaborative. Creating an environment that encourages open communication and iteration will empower stakeholders to become stewards of data quality, drive better decision making, and improve organizational innovation and performance.

    Putting Data Governance into Practice

    Tackling data governance challenges may feel complicated and daunting, but with a few best practices and internal alignment, it can be done easily and effectively.

    Addressing scalability issues, increasing visibility, embracing Secure by Design, and fostering a strong data culture are the best starting points. But to truly future-proof your data governance framework, adopting automated tools to manage some of the most difficult components, such as data security, allows you to further streamline processes with safeguarding your most valuable assets. Investing in a data security platform is also an investment in your peace of mind and ongoing commitment to data governance, so you can navigate data management with ease and confidence.