Overview of Governance Models: Find the Right Fit

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Recently, a friend reached out to me on LinkedIn with a request. She needed help preparing for a webinar on Data Governance and its various models. She had been assigned to lead this project in her department but felt completely unenthusiastic about it.

I can certainly relate that data governance isn’t typically a subject that sets pulses racing. However, her request prompted some introspection, leading me to delve deeper into the subject.

So, what exactly is a data governance model? Simply put, it’s a structured framework that delineates how data is managed throughout its lifecycle—from inception and storage to ongoing maintenance and disposal.

In this article, we’ll look at different types of data governance models. Some keep control in one place, while others spread it out.

For example, big companies might like a centralized model to keep things consistent everywhere. But smaller ones might prefer a decentralized one for more flexibility.

Let’s dive in…

TL; DR

There are three approaches to data governance—centralized, decentralized, and hybrid, with each offering distinct advantages and challenges.

Centralized: Provides uniformity but can be rigid Decentralized: Offers agility but may lead to disjointed practices Hybrid: Offers balance but requires careful planning and monitoring

Building a good governance framework involves stakeholder alignment, clear goal-setting, thorough data mapping, and establishing a governance council.

What is a Governance Model? 

Governance model is a set of rules and procedures that prescribe how data must be captured, maintained, retrieved, and utilized in an organization. The primary reason is that customer information may be managed poorly without supervision, leading to legal consequences and tarnishing the business’s reputation.

Without a data governance program, it is impossible to guarantee the quality and compliance of data with the requirements of privacy laws. Moreover, if you have a governance model in place, you are protecting your customer’s data from being subject to phishing as well.

The main principles of data governance are:

1. Data accuracy: Aids in ensuring that collected data is accurate and valid and brings out the reliability
2. Data accessibility: Ensures that the clients get the information they need at the right time and from a reliable and secure source, only accessible to the authorized personnel
3. Data consistency: It is responsible for the uniform data definitions, formats, and values across systems
4. Data compliance: Requires adherence to all relevant data laws, regulations, and standards
5. Data transparency: Involves visibility of data handling processes within an organization
6. Data integrity: Ensures accuracy and consistency of data throughout its lifecycle
7. Data Stewardship: Manages and oversees data assets, ensuring quality, accessibility, consistency, and protection

Do the principles seem too high to handle here? Don’t worry, Sprinto is here to help you with all the Governance-related tasks and get up to speed with automation.

Types of Governance Models 

When it comes to data governance, you must consider your options. Before deciding, carefully assess the benefits of centralized and decentralized approaches. After a meaningful conversation with our internal GRC experts, we have consolidated this data in the next section so that you can choose the best framework for your company or department.

Here are the 3 types of governance models:

1. Centralized Data Governance Model

Centralized data governance often becomes a command-and-control setup. Typically, one person is appointed as the data governance lead, making key decisions and steering the program. 

This centralized leader is responsible for establishing policies, standards, and procedures that ensure data quality, security, and compliance across the organization.

Most enterprises opt for this centralized approach, where the IT department consolidates control over IT infrastructure and data management. In this model, the IT department becomes the central hub for all data-related activities, from data collection and storage to processing and analysis. 

Pros and cons of centralized data governance model

Pros	Cons
Centralized control means uniform policies and procedures are in place. This leads to a sense of uniformity in data handling practices.	One central authority can struggle to address the diverse and changing needs of different departments on time.
With a single point of control, it’s simpler to crack the whip on comprehensive security protocols and track compliance.	A one-size-fits-all approach might not suit the unique requirements of various business units, leading to inefficiencies.
The structure ensures all data practices align with legal and regulatory requirements.	Handling all data governance tasks centrally can strain resources and lead to burnout which in turn diminishes overall performance.
Consolidating data management within the IT department allows for better resource allocation and operations.	Employees used to decentralized data management might be reluctant to adopt new centralized processes.
Having a designated governance lead centralizes decision-making and accountability for better oversight.	If the central governance mechanism encounters problems, it can affect the entire organization’s data management capabilities.

2. Decentralized Data Governance Model

Decentralized governance offers a responsive, adaptive model that evolves with technological advancements. Instead of one person calling the shots, a committee typically designs and manages the enterprise data governance strategy.

In this setup, different business units have their own data governance teams, each responsible for managing their data assets.

Pros and cons of the decentralized data governance model

Pros	Cons
With decisions spread among units, this model reacts nimbly to emerging challenges.	Without central coordination, standards and procedures may differ, causing inefficiencies.
Customized approaches mean better data quality and relevance.	Without oversight, there’s a risk of practices diverging from organizational goals or regulations.
Units have a stronger say in governance, representing diverse needs.	Units may duplicate efforts without sharing best practices.
New units or projects can establish governance teams without disrupting existing operations.	Data may become isolated within units.

3. Hybrid Data Governance Model

Hybrid data governance combines the strengths of both top-down and bottom-up approaches. It starts with a centralized framework for governance, technology, and best practices but allows application owners autonomy. 

This means each business area manages its own data and metadata and can develop standards and policies.

The hybrid model can be challenging to implement, but it’s a great option for balancing competitiveness with regulatory compliance. It offers flexibility while ensuring consistency and compliance across the organization.

Pros and cons of the decentralized data governance model

Pros	Cons
Offers the flexibility of bottom-up autonomy while maintaining the structure of top-down governance	Requires meticulous planning and coordination to ensure smooth implementation.
Provides a solid governance framework, technology, and best practices	Without proper alignment, there’s a risk of fragmentation or duplication of efforts.
Allows application owners to drive innovation and decision-making	Requires significant investment in resources, tools, and training.
Can adapt to changing business needs and technological advancements	Complexity may slow down decision-making and agility.
Strikes a balance between competitiveness and regulatory adherence	Needs ongoing monitoring and adjustment to ensure alignment.

Since this is one of the hardest models to implement and requires ongoing monitoring of all your data, you need an easy solution that will get you up to date with managing governance and compliance.

This is where Sprinto comes in. Sprinto is GRC automation software that will help you implement your GRC model within weeks and not months. 

Sprinto connects seamlessly with your systems, doing what is needed to implement the GRC model. And the bonus here is that with a continuous monitoring feature, Sprinto automatically aligns and monitors controls against top security standards like SOC 2 and ISO27001. It continuously evaluates governance, gathers evidence, and triggers remedial actions—all day, every day.

Integrating with your infrastructure, Sprinto fills in the gaps, ensuring your governance and compliance mechanisms run smoothly around the clock. 

This way, you’ll gain a 360-degree real-time view of your compliance status, automate manual tasks such as evidence collection, receive tiered risk alerts, and access other embedded capabilities for effortless compliance and year-round audit readiness.

How To Build a Governance Model? 

Building a governance model might sound complex. And it is complex. However, you need the right guidance tools and planning to get through this. In the below section, we’ve broken down the process into 7 steps to help you create a model that works for your organization.

Step 1: Get the Right Stakeholders on the Board

When initiating the development of a governance model from scratch, the foremost task is to garner approval from the board. This step is indispensable, as without the necessary skills and resources a