Without proper governance, enterprise data can quickly become chaotic, leading to costly mistakes, compliance headaches, security concerns, and missed opportunities.
That’s where data governance comes in: Setting the rules, policies, and standards for how data is handled. It defines who is responsible for data, who can access it, and how it should be protected.
It’s like creating a playbook about how data is used across your organization.
Table of contents:
- The impact of poor data governance
- 6 examples of enterprise data governance challenges
- Enhancing Data Security and Compliance with Samsung Knox
The impact of poor data governance
Before getting into specific data governance examples and challenges commonly faced by enterprises, it's important to drill down on the impact of poor data governance.
Without governance, data becomes disorganized, inconsistent, and unreliable. This can cause a domino effect of problems within your enterprise and ultimately lead to compliance risks, operational inefficiencies, security issues, and loss of revenue.
Imagine a hypothetical SaaS business collecting user data from various sources like sign-up forms, in-app activity, and customer support tickets. Without proper governance, system silos emerge, creating the risk of conflicting information. For example, one platform may show a customer is on a premium plan, while another shows the same customer is on a free trial, leading to billing and service disruptions.
Compliance becomes a nightmare if the company can’t demonstrate how and where user data is stored, exposing them to potential violations and legal battles. Additionally, poorly secured data is attractive to hackers, increasing the probability of a data breach that puts the customer's sensitive data at stake.
This example shows just how chaotic data management can get without proper governance.
6 examples of enterprise data governance challenges
Below are six examples of common data governance challenges that enterprises face every day.
1. Lack of leadership and accountability
Lack of accountability and leadership is a common data governance challenge because, when leaders do not provide direction, no one assumes responsibility for keeping data accurate, secure, and compliant. The outcome is hit-or-miss practices, interdepartmental misunderstandings, and data silos. Without someone to take responsibility, choices about data quality, security, and usage come ad hoc decisions, which increases the chances for errors, intrusions, and compliance problems. In short, strong leadership and accountability are needed to establish clear data policies, implement standards, and make sure that everyone has the same understanding.
2. Fragmented data landscapes
Broken data landscapes refer to data that is scattered across different systems, platforms, and departments, making it hard for employees to have a unified view. When data is stored in multiple places—like on-premises servers, cloud storage, and third-party applications—it's easy for inconsistencies to happen. Without proper care comes duplicate records, conflicting data, and a lack of data integrity. On top of that, dealing with compliance, security, and access is a nightmare when you lack central control.
Ultimately, fragmented data makes it hard to maintain accuracy, consistency, and security across the organization. The impact of poor data governance is evident when your data landscape is fragmented, making it harder to track and trust your information.
3. Siloed data
Unlike fragmented data, which refers to data scattered amongst all different parts of your organization, siloed data is a data governance example that refers to data that is stored separately and isn't easily shared or accessed across departments. The data is often consistent and complete within its silo but disconnected from other parts of the organization. For example, customer information might be stored separately in marketing, sales, and support systems, with little integration between the two.
4. Data quality issues
Without proper systems put in place to monitor data quality, enterprises are left with inaccurate insights, poor decision-making, and operational inefficiencies. Incomplete, outdated, duplicated, or inconsistent data is untrustworthy, giving leadership less confidence in reports and analytics.
It can also lead to mistakes like the wrong customer segment being targeted or financial reports being misestimated.
5. Poor data and metadata management
Poorly managed data and metadata cause enterprises headaches and lead to major inefficiencies. It’s very straightforward. If your organization holds important data that can help you make smart decisions to move your business forward, but your employees can’t find it because it’s mislabeled, saved in the wrong spot, or poorly structured, it does the opposite of what it’s supposed to.
6. Compliance concerns
There are many laws and regulations related to data privacy and protection, and they are constantly changing. Proper data governance ensures that an enterprise's data is securely stored, managed, and used according to a country’s privacy laws. If businesses don’t comply due to poor governance, they face hefty fines, legal action, and reputational damage. As regulations continue to evolve, businesses have to stay vigilant and maintain proper documentation to avoid costly penalties.
Enhancing Data Security and Compliance with Samsung Knox
Data privacy laws and regulations are constantly growing and in addition to proper data governance, enterprises need robust security solutions to ensure their data is safe and serving them.
Samsung Knox offers an array of enterprise security tools that can help your organization maintain control over your data while complying with privacy regulations. With features like secure folder, data encryption, and real-time threat detection, Samsung Knox helps companies protect sensitive information, reducing the risk of breaches, fines, and reputational damage.
By incorporating Samsung Knox into your data governance strategy, you can ensure both reliable security and compliance. Addressing enterprise data governance challenges with the right tools is essential for safeguarding your data.
