8 Data Governance Challenges That Can Derail Your Business Success

As cloud-based solutions multiply, so do the complexities of managing sensitive information. From regulatory compliance to data security, SaaS companies face a minefield of governance issues that lead to breaches, costly fines, and operational inefficiencies. 

This article cuts through the noise and identifies eight critical data governance challenges. But that’s not all; we’re pitching impactful solutions that address specific problems. 

We understand that for SaaS leaders, comprehending such hurdles isn’t just about risk mitigation—it’s about turning data governance into a competitive advantage.

TL;DR Data governance forms a set of policies, processes, and standards to secure a business’s data management system.  Common data governance challenges include siloed data, third-party risks, poor quality of data, lack of data literacy, resource or budget constraints, etc.  Data governance issues can be solved using an efficient data governance tool or even a GRC (Governance, risk, and management) tool for a more unified approach.

1. Existence of data silos

Data is said to be siloed when it’s available to only a specific group or stakeholders and not to anybody else. It’s usually a result of the absence of cross-department collaboration in SaaS companies. 

An HBR survey reveals that 84% of executives experience the negative impact of data silos.

Data silos hinder data governance by creating isolated data sets that are difficult to access, manage, and standardize. This fragmentation leads to limited data visibility, duplication or redundant efforts, and delayed decision-making, thus affecting efficient data governance. 

How to combat data silos?

Data silos can be eliminated using a structured data management system usually facilitated by a data governance tool. They establish data governance policies, procedures, and standards for consistent and accurate data handling throughout its lifecycle. 

Governance software provides security controls that facilitate effective communication and information flow across departments. This prevents data from being isolated within specific teams and ensures it is accessible organization-wide.

Examples of such controls include:

Controls	Action to be completed
Organizational Structure	Entity maintains an organizational structure to define authorities, facilitate information flow, and establish responsibilities.
Production Databases Access Restriction	Entity ensures that access to the production databases is restricted to only those individuals who require such access to perform their job functions.
Security & Privacy Attributes	Entity performs physical and/or logical labeling of information systems as per the guidelines documented policy defined for data classification
Role-Based Access Control	Entity enforces a Role-Based Access Control (RBAC) policy over users and resources that applies need-to-know and fine-grained access control for sensitive/regulated data access.

2. Resource constraints

Resource constraints come in different forms, like inadequate staffing, budget limitations, skill shortages, and insufficient training. In most cases, these examples are usually related to each other. 

Insufficient funding can restrict the acquisition of essential data governance tools/software and skilled employees needed to implement and maintain governance practices.

A lack of dedicated staff means fewer resources to manage, monitor, and enforce data policies. This can also lead to gaps in data oversight and inconsistent application of governance standards across the SaaS business.

How do we move around resource constraints?

Alexander Coelho, Certified Information Privacy Manager (CIPM), suggests five key ways to optimize resource budgeting and allocation for data governance:

• Enabling consistent data reports to know where the budget is being spent.
• Analyzing how resources are being used.
• Conducting cost-benefit analysis and forecasting to stay within budget.
• Being proactive in mitigating risks before they impact resources.
• Planning strategically for upcoming resources

3. Management of data inventory

A well-structured data inventory should contain a comprehensive catalog of an organization’s data assets, including their locations, types, sources, and ownership. It should provide a structured overview of the data collected and used by the organization according to a data governance policy. 

Data should be tagged with definitions, formats, and relationships and properly classified with pre-defined categories.

Challenges in governing a data inventory can root from data silos, regular updates, not having policies in place (like data classification policy), pulling information from integrations, or even large volumes of data. This could impact the business, causing non-compliance with frameworks like ISO 27001 or even GDPR. 

How do you keep your data inventory updated?

We’re assuming that you have an updated and documented list of all your data sources with information tagged with attributes like data type, owner, source, format, how it’s used, etc. The next step is to draft a data classification policy. 

Here’s a template for you:

After you’re done categorizing your data, you need to record and maintain your metadata and regularly update your inventory. This sounds simple, but practically, it is the hardest part. 

The best way to proceed with managing a data inventory with effective governance con