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 controls is to adopt a GRC tool that can be integrated with data services platforms. For instance, Sprinto can be integrated with Mongo Atlas or DataDog, which solves inventory management while automating governance workflows. 

4. Third-party risks

Steering clear from third-party risks is impossible with today’s interconnected business environments and complex supply chains. However, third-party data sharing introduces several data governance issues in the form of:

• Data breaches: Attackers do not really need to break into your systems to steal sensitive information. They can just target third-party entities if they feel their security systems are already compromised.
• Data control: Companies give up control of their data by relying on the uptime, policies, and practices of their third-party providers-which may not necessarily be as good as their own.
• Inconsistent governance: Third-party governance of data may be poor and inconsistent, leading to disparities in the treatment and protection of data.

How do you protect your data from third-party risks?

Third-party risk management is quite vast. It includes conducting thorough due diligence, implementing strict access controls, and ensuring that they have access to the minimum data possible. 

On your part, you can establish clear contracts that outline data protection responsibilities and protocols along with encryption and anonymization of data while sharing. However, to monitor their access constantly, you need to rely on risk management software that gives you real-time insights into how your data is being used and when. 

5. Regulatory compliance requirements

Compliance requirements with industry regulations and frameworks for information security can be a benefit as well as a challenge in the SaaS industry. 

While regulatory compliance ensures that your business establishes data security standards, which can enhance your reputation, build customer trust, and reduce the risk of data breaches, it can also strain your resources and limit flexibility in how your organization handles data.

For instance, regulations like GDPR, CCPA, and HIPAA are often complex and multi-faceted. They require intricate processes with constant vigilance and adaptability in data governance practices.

How do we cope with compliance requirements alongside data governance?

The answer here is pretty clear: A GRC automation solution designed to streamline governance, risk management, and compliance. 

A GRC software will make your data governance practices not only aligned with regulatory requirements but also optimized for efficiency. It centralizes your data management efforts and provides real-time oversight and automated reporting to maintain data integrity and compliance without the need for constant manual intervention.

6. Maintaining quality of data

According to Gartner, poor-quality of data can cost an organization more than $12.9 million. But why?

Poor data quality (DQ) can also increase operational costs and compliance risks while reducing customers’ trust and brand loyalty.

How to identify and address poor data quality?

There are several methods that identify as well as address poor data quality:

• Data profiling: Spots issues like missing values, duplicates, or inconsistencies by analyzing patterns and anomalies within your datasets.
• Completeness check: Ensure every necessary data field is filled; gaps in data can severely impact quality and usability.
• Timeliness audit: Keep your data up-to-date. Relying on outdated information can skew insights and lead to poor business decisions.
• Automated monitoring: Implement tools like DataDog, Oracle, SolarWinds that track metrics such as error rates and data freshness, offering real-time alerts when quality drops.

7. Assigning roles and responsibilities

The problem of assigning roles and responsibilities for data governance roots from not having a dedicated governance team. While enterprise businesses can afford to have a GRC team, such a thing may not be suitable for a smaller or even mid-sized SaaS organizations.

Even when employees from across departments are assigned roles, there’s a challenge in maintaining permissions, cross communication, and documenting the whole process. 

How to assign roles effectively for data governance? 

First of all, know what roles you need to assign; the list is inexhaustive, depending on the size and structure of the organization. 

Once you identify the roles, you need to define clear responsibilities for data governance while matching skillset and experience. Following that, provide training and set accountability standards. 

Alternatively, rather than having multiple employees work towards the common goal of data governance, you can adopt a GRC tool and have just one role (For instance, GRC Lead). This person along with the tool will have bird’s eye view of your security posture and the necessary controls for governing data. 

8. Data literacy

“82% of leaders expect all employees to have basic data literacy, but only 40% of employees say they are being provided with the data skills their employers expect.” – Tableau.

Data literacy has remained one of the key areas of Data Governance. But, most governance teams are not equipped enough with the knowledge required. 

Without data-literate employees, even if a proper definition of roles and responsibilities could be made, they might find it difficult to really understand the specifics of GRC. This could have a serious impact on their decision-making and could lead to non-compliance. 

How to build a data-literate workforce?

A data-literate workforce will default to a culture of data security. Offer targeted training programs that cover essential data concepts like data quality principles, data privacy and security, and basic data analysis techniques.

Employees should also be familiar with interpreting data visualizations, using data tools, and understanding how data-driven decisions impact business outcomes. These foundational concepts enable employees to work more effectively with data and contribute to a more sincere data governance culture.

Overcoming data governance challenges on a budget

Tackling the above data governance challenges isn’t about ticking boxes – it’s about building trust and staying competitive in the fast-paced SaaS world. The good news? You don’t need a Fortune 500 budget to get it right.

Smart leaders are turning to integrated platforms that do the heavy lifting without breaking the bank. These tools bundle everything from continuous security monitoring to compliance training, cutting through the complexity and cost of juggling multiple solutions.

Take Sprinto, for example. It comes with pre-built policies that can be customized to suit your data governance needs. To enforce policies, it lets you federate accountability to stakeholders, automatically maps controls to frameworks, and continuously monitors them for compliance or governance drift, automatically collecting audit-grade evidence along the way. 

The bottom line? Effective data governance is within reach, even for lean teams. With the right approach and tools, you can turn these challenges into opportunities to stand out in the crowded market.

Frequently asked questions

1. What is data governance?

Data governance refers to the framework and processes that ensure the proper management of an organization’s data. It establishes policies, standards, and controls to ensure data quality, consistency, security, and accessibility across the organization. The goal is to ensure that data is accurate, available, and used appropriately to support business decisions.

2. What are some common enterprise data governance challenges?

Three of the most common enterprise data governance challenges include:

1. Compliance: Navigating complex regulatory requirements (e.g., GDPR, HIPAA) across various jurisdictions can be challenging.
2. Stakeholder buy-In: Gaining organizational support and understanding for data governance initiatives is often a significant hurdle.
3. Resource allocation: Implementing and maintaining data governance programs according to a data governance maturity model requires substantial time, budget, and skilled personnel.

3. What are some data governance challenges in healthcare?

The four most common data governance challenges in healthcare organizations are:

Regulatory Compliance: Healthcare organizations must comply with stringent regulations like HIPAA, which can complicate data governance efforts.

Data Sensitivity: Handling sensitive patient data requires robust security measures, adding complexity to governance frameworks.

Data Integration: Integrating data from various systems (e.g., EHRs, billing systems) while maintaining consistency and accuracy is a significant challenge.

Interoperability: Ensuring that data can be shared across different healthcare systems and platforms is often difficult due to differing standards and formats.

4. What kind of challenges are faced while implementing data governance? 

Employees may resist changes in data management practices, preferring to stick with established methods. Plus, integrating new data governance tools with existing IT infrastructure can be technically challenging. Another challenge is that the financial investment required for technology, training, and ongoing management can be significant.