7 Common Data Governance Mistakes & How to Avoid Them

Updated July 28th, 2023
Data Governance Mistakes

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In the data-driven landscape of the 21st century, information is power. Companies swim in oceans of data, pulled from countless sources and in multiple formats. The capability to navigate these complex seas, ensuring every data point’s relevance, security, and accessibility, gives organizations a competitive edge.

This is where data governance comes into play. As the backbone of effective data management, data governance is like the compass guiding a ship through stormy waters, establishing the ground rules and strategic direction that enable organizations to leverage their data in a reliable, efficient, and ethical manner.

In this blog, we’ll dive deep into why data governance is not just important but absolutely crucial for today’s organizations, what happens to them when data governance fails, and some of the common mistakes of data governance and how to avoid them.

Table of contents #

  1. What is data governance?
  2. Reasons why data governance is critical for organizations
  3. What happens to organizations when data governance fails?
  4. 7 Common data governance mistakes
  5. Related reads

What is data governance? #

Data governance is a set of procedures and guidelines that detail how data is to be properly managed, accessed, and used. Good data governance helps ensure the quality, integrity, and security of organizational data. Data governance grew out of data stewardship, which is about managing the flow of, and access to, data in order to protect an organization from risk.

Still, why is it important to organizations? Let’s dive right into it.

7 Reasons why data governance is critical for organizations #

Here are the seven reasons why organizations should care about data governance:

  1. Data quality
  2. Compliance
  3. Security and privacy
  4. Data usability
  5. Operational efficiency
  6. Strategic decision making
  7. Trust

Now, let us look into each of these reasons in detail:

1. Data quality #


Through policies and procedures, data governance helps ensure the accuracy, completeness, consistency, and reliability of data. High-quality data is essential for informed decision-making and effective operations.

2. Compliance #


With ever-increasing regulations surrounding data privacy and security (like GDPR, CCPA), data governance is necessary to ensure that an organization adheres to these regulations, avoiding penalties and reputational damage.

3. Security and privacy #


A robust data governance framework includes measures to protect data from breaches, misuse, and unauthorized access, ensuring the confidentiality and integrity of data. It also helps ensure that sensitive data is handled appropriately, safeguarding individual privacy.

4. Data usability #


Proper data governance makes data more usable and accessible to those who need it within the organization. It ensures that data is properly cataloged, stored, and maintained, making it easy to locate and use.

5. Operational efficiency #


By providing a clear structure for data management, data governance reduces redundancy, promotes best practices, and improves efficiency. It can also help prevent costly mistakes and reduce the time spent locating and correcting data issues.

6. Strategic decision making #


A strong data governance program ensures decision-makers have access to reliable, high-quality data, which is critical for strategic planning, forecasting, and business intelligence.

7. Trust #


Good data governance builds trust, both internally and externally. Employees, customers, and stakeholders can have confidence in the organization’s data and its ability to handle data responsibly.

Overall, data governance provides a framework that helps organizations manage their data effectively, ensuring its quality, usability, compliance, security, and privacy. It’s a vital component of leveraging data as a strategic asset.

What happens to organizations when data governance fails? #

When data governance fails, or is poorly implemented, it can lead to several negative consequences for an organization. Here are some popular after effects:

  1. Decreased data quality
  2. Compliance failures
  3. Security breaches
  4. Operational inefficiencies
  5. Misuse of resources

1. Decreased data quality #


Poor data governance often results in low-quality data that is inconsistent, inaccurate, or incomplete. This can lead to misguided decision-making, faulty analyses, and inefficient operations.

2. Compliance failures #


A failure in data governance can result in non-compliance with data regulations, which can lead to severe penalties, legal complications, and damage to the organization’s reputation.

3. Security breaches #


Inadequate data governance may lead to insufficient security controls, increasing the risk of data breaches. This can result in substantial financial costs, legal repercussions, and reputational damage.

4. Operational inefficiencies #


Poor data governance can result in redundancies, inefficiencies, and confusion. It can also lead to a lack of trust in the data, which hampers decision-making processes.

5. Misuse of resources #


Without clear governance, data can be misused or overused. This can lead to unnecessary costs, such as maintaining redundant data storage or spending time cleaning up messy data.

In short, effective data governance is essential for organizations to avoid the negative consequences of poor data management.

7 common data governance mistakes & how to avoid them #

In this section, let’s explore some of the most prevalent mistakes made in data governance and gain valuable insights on how to avoid them. Here they are:

  1. Lack of clear ownership and accountability
  2. Not aligning with business objectives
  3. Neglecting data quality
  4. Inadequate communication and training
  5. Ignoring privacy and security
  6. Failing to evolve and adapt
  7. Lack of tools and technology

Let’s look into each of the mistakes in detail, and how to avoid them.

1. Lack of clear ownership and accountability #


This refers to a situation where data management responsibilities are not clearly defined within an organization. For example, in the 2017 Equifax data breach, a GAO report indicated a significant breakdown in internal processes and a lack of accountability.

The company lacked specific roles accountable for data management, which resulted in incorrect system operation and weak data security. To avoid this, an organization should establish and clearly define roles and responsibilities, including data owners who are accountable for data accuracy and usage, and data stewards who oversee the data’s lifecycle.

2. Not aligning with business objectives #


This mistake occurs when data governance initiatives do not support or align with the broader business goals. As a Deloitte report highlights, it’s common for data governance initiatives to be overly focused on technology and neglect their correlation with underlying business objectives.

To avoid this disconnect, the governance framework should be designed with the business’ goals in mind. If the primary business goal is enhancing customer satisfaction, for example, data governance could prioritize maintaining the accuracy and reliability of customer data.

3. Neglecting data quality #


This occurs when the data governance strategy overlooks the importance of maintaining high-quality data. The money laundering scandal at HSBC in 2012, as discussed in this Financial Times article, was partially attributed to poor data quality in their customer records.

This led to a failure in identifying suspicious transactions. To ensure this doesn’t occur, data governance should include mechanisms for ongoing data quality checks and data cleansing to maintain the reliability and integrity of the data.

4. Inadequate communication and training #


Without proper communication and training, data governance initiatives may be misunderstood or improperly implemented. The UK’s NHS National Programme for IT, a multibillion-dollar initiative to digitize health records, faced significant challenges due to inadequate training and poor understanding of the system by users.

To avoid this, clear, consistent communication about the value, purpose, and processes of data governance is crucial. This should be supplemented with thorough training for all relevant staff members.

5. Ignoring privacy and security #


Inadequate attention to data privacy and security can lead to massive data breaches. The breach at Yahoo in 2013-14, which affected all 3 billion of its user accounts as reported by The Guardian, resulted from insufficient data security measures.

To prevent such breaches, data governance strategies must include strong policies and procedures for data privacy, security, and compliance. These should encompass strong data access controls, encryption standards, anonymization techniques for sensitive data, and regular audits to check adherence to standards.

6. Failing to evolve and adapt #


This refers to the inability to adjust data governance strategies to accommodate changes in business objectives, technology, or regulatory requirements. A classic example of failure to adapt is Blockbuster, which according to this Forbes article, failed to evolve with the digital revolution in the movie rental industry.

In the context of data governance, regular review and adaptation are necessary to keep the strategy effective and relevant. The governance framework should be flexible and adaptable to evolving business needs, technological advancements, and new regulatory requirements.

7. Lack of tools and technology #


Manual processes or outdated tools can hamper the effectiveness of data governance efforts. For example, a company still relying on spreadsheets to track and manage data assets may find the process inefficient and error-prone.

Having the right data quality tools is critical for effective governance. Investing in the right set of tools for tasks like data cataloging, data quality management, metadata management, and more, can greatly enhance the efficiency and effectiveness of data governance efforts.

Bottom line: By understanding these common mistakes, companies can take proactive measures to ensure their data governance initiatives are effective, bring real value to the organization, and remain aligned with the business objectives.

Recap: What have we learnt so far? #


Data governance is paramount in the contemporary, data-saturated world, functioning as the foundation of effectual data management. It provides a set of processes ensuring the formal handling of data assets within organizations and proves crucial for maintaining data quality, compliance, security, privacy, usability, operational efficiency, strategic decision-making, and fostering trust.

However, failure or poor implementation of data governance can lead to problems like decreased data quality, compliance failures, security breaches, operational inefficiencies, and misuse of resources, as seen in the 2017 Equifax data breach.

By acknowledging these issues, companies can craft effective data governance initiatives that align with business objectives and bring value to the organization, underlining the necessity of strong data governance.

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