# AI vs. Traditional Data Governance: Key Differences

> Canonical: https://dialzara.com/blog/ai-vs-traditional-data-governance-key-differences  
> Published: 2024-07-16  
> Updated: 2024-12-10  
> Summary: Explore the key differences between AI and traditional data governance, challenges, and best practices for effective AI data governance.

_AI data governance requires new approaches beyond traditional methods. Here's what your organization needs to know to adapt successfully._

## Key points

- Build hybrid frameworks combining traditional data quality with AI-specific protocols
- Monitor for model drift and bias in real-time, not just data accuracy
- Create cross-functional teams with data stewards, scientists, and AI developers
- Address transparency gaps that traditional governance frameworks miss

AI is changing how companies manage data. Here's a quick comparison of [AI and traditional data governance](https://dialzara.com/blog/ai-data-governance-global-trends-privacy-and-security/):

| Aspect | Traditional Data Governance | AI Data Governance |
| --- | --- | --- |
| Focus | Overall data management | AI-specific challenges |
| Methods | Manual monitoring | Automation, real-time analysis |
| Rules | General data laws | AI ethics, bias prevention |
| People | Data managers | Data scientists, AI developers |
| Risks | Data security, quality | AI bias, transparency |
| Automation | Limited | Advanced |

Key points:

-   AI governance deals with machine learning, bias, and ethical use
-   Traditional governance focuses on data quality, security, and compliance
-   Combining both approaches helps manage AI responsibly
-   Challenges include technical issues, ethics, and skills gaps
-   [Good AI governance](https://dialzara.com/blog/ai-governance-framework-best-practices-and-implementation/) needs clear rules, monitoring, and a responsible culture

This article explains these differences, challenges, and best practices for [effective AI data governance](https://dialzara.com/blog/10-steps-to-ai-compliance-training-and-governance-tips/).

## Related video from YouTube

## 2\. Understanding Traditional Data Governance

### 2.1 What is Data Governance?

Data governance is how companies manage their data. It includes rules and steps to keep data:

-   High-quality
-   Safe
-   Correct

Good data governance helps companies:

-   Make smart choices
-   Lower risks
-   Work better

### 2.2 Main Parts of Data Governance

Data governance has these key parts:

| Part | What it Does |
| --- | --- |
| Data Stewards | People who look after data quality and safety |
| Quality Management | Makes sure data is correct and complete |
| Security | Keeps data safe from people who shouldn't see it |
| Following Rules | Makes sure the company follows laws about data |

### 2.3 Goals of Data Governance

Data governance aims to:

-   Keep data correct and complete
-   Protect data from misuse
-   Make data better
-   Help make good choices
-   Lower risks and work smoother
-   Follow data laws and rules

## 3\. AI Data Governance: A New Approach

### 3.1 What is AI Data Governance?

AI Data Governance is about managing data used by AI systems. It focuses on:

-   Data quality
-   Data safety
-   Data usefulness

[Good AI Data Governance](https://dialzara.com/blog/ai-data-governance-in-media-and-entertainment-2024-guide/) matters because:

-   AI models learn from data
-   The data quality affects how well AI works

### 3.2 New Challenges from AI

AI brings new data challenges:

| Challenge | Description |
| --- | --- |
| Data Quality | Keeping data correct across many sources |
| Privacy | Protecting people's information |
| Security | Guarding against cyber attacks |
| Rules | Following laws about data use |

### 3.3 Goals of AI Data Governance

AI Data Governance aims to:

1.  Keep data correct and complete
2.  Avoid unfair bias in data
3.  Follow ethical rules
4.  Protect user privacy

To do this, companies need:

-   Clear rules for collecting and using data
-   Standard ways to label data
-   Processes to check for and fix bias

These steps help build AI systems that work well and treat people fairly.

## 4\. How AI and Traditional Data Governance Differ

### 4.1 Focus Areas

AI and traditional data governance focus on different things:

| Traditional Data Governance | AI Data Governance |
| --- | --- |
| Overall data management | AI-specific challenges |
| Data quality and security | Algorithmic transparency |
| General compliance | Decision-making processes |
|  | Potential for bias |

### 4.2 Data Handling Methods

The ways of handling data differ:

| Traditional Data Governance | AI Data Governance |
| --- | --- |
| Manual monitoring | Automation |
| Separate policies | Predictive analytics |
| Reactive protocols | Real-time threat detection |

### 4.3 Rules and Laws

Different rules apply:

| Traditional Data Governance | AI Data Governance |
| --- | --- |
| GDPR, CCPA compliance | GDPR, CCPA compliance |
|  | Ethical AI guidelines |
|  | Bias and transparency rules |

### 4.4 Who's Involved

Different people take part:

| Traditional Data Governance | AI Data Governance |
| --- | --- |
| Data owners | Data owners |
| Data stewards | Data stewards |
| Data custodians | Data custodians |
|  | Data scientists |
|  | Machine learning engineers |
|  | AI system developers |

### 4.5 Managing Risks

The risks to manage are different:

| Traditional Data Governance | AI Data Governance |
| --- | --- |
| Data security | Data security |
| Data quality | Data quality |
| Compliance | Compliance |
|  | AI bias |
|  | AI system opacity |
|  | Unintended AI outcomes |

### 4.6 Data Lifecycle

How data is handled over time differs:

| Traditional Data Governance | AI Data Governance |
| --- | --- |
| Creation to disposal | Creation to disposal |
|  | Data drift management |
|  | Concept drift handling |
|  | Feedback loop monitoring |

### 4.7 Use of Automation

How much automation is used differs:

| Traditional Data Governance | AI Data Governance |
| --- | --- |
| Limited automation | Advanced automation |
| Manual processes | Machine learning |
|  | Natural language processing |

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## 5\. Combining AI and Traditional Data Governance

### 5.1 Why a Combined Approach Matters

Mixing AI and old-style data governance helps companies:

-   Use AI well
-   Handle data responsibly

Old-style data governance:

-   Keeps data good, safe, and follows rules

AI governance:

-   Deals with AI issues like clear decision-making and avoiding unfair bias

Putting them together makes a full plan for managing data with AI.

### 5.2 How to Combine Them Effectively

To mix AI and old-style data governance well, companies should:

| Step | Description |
| --- | --- |
| Make one plan | Create a single set of rules for both old-style and AI data handling |
| Use AI to help | Let AI do some old-style tasks, like checking data quality |
| Work together | Get data experts and AI experts to talk to each other |
| Keep checking | Look at how well the mix is working and fix problems |

## 6\. Problems in Setting Up AI Data Governance

### 6.1 Technical Issues

Setting up AI data governance can be hard. Here are some key problems:

| Issue | Description |
| --- | --- |
| Big data | Hard to handle lots of data for AI training |
| Data quality | Poor data can make AI work badly |
| System integration | Tough to mix AI with current data systems |
| Security | AI systems can be hacked or leak data |

### 6.2 Ethical Concerns

AI data governance also brings up ethical issues:

| Concern | Explanation |
| --- | --- |
| Bias | AI can make unfair choices based on biased data |
| Privacy | AI might watch people too much |
| Responsible use | Making sure AI is used for good, not harm |

### 6.3 Skills Needed

Companies need specific skills to manage AI data well:

| Skill | Why it's important |
| --- | --- |
| Data management | To handle data correctly |
| AI development | To build and run AI systems |
| Ethics knowledge | To make sure AI is fair and good |

Companies also need to:

-   Build a culture that cares about using AI responsibly
-   Make sure AI systems are clear about how they work
-   Hold people accountable for AI decisions

## 7\. Tips for Good AI Data Governance

### 7.1 Setting Clear Rules

To manage AI data well, companies need clear rules. This means:

-   Deciding who does what
-   Making rules about data use
-   Following laws about data

Clear rules help:

-   Stop data misuse
-   Make sure people are responsible
-   Keep AI decisions open

### 7.2 Checking AI Use

It's important to keep an eye on how AI is used. This involves:

| Action | Purpose |
| --- | --- |
| Looking for bias | Make sure AI is fair |
| Checking data quality | Keep data good |
| Watching AI decisions | See how AI chooses things |

By doing these things, companies can find and fix problems early.

### 7.3 Building a Good AI Culture

Companies should create a culture that uses AI well. This means:

-   Making AI that follows good rules
-   Being open about how AI works
-   [Using AI to help](https://dialzara.com/blog/artificial-intelligence-help-desk-essentials/) people

A good AI culture:

-   Builds trust
-   Makes sure AI is used for good
-   Helps the company last longer

To do this, companies can:

-   Train workers about good AI use
-   Reward people who use AI well
-   Talk openly about AI challenges

## 8\. Wrap-up

We've looked at how AI and old-style data governance are different. AI is changing how we handle data, bringing new problems and chances. By knowing the differences in:

-   What they focus on
-   How they handle data
-   Rules they follow
-   Who does what

Companies can mix old and new ways of managing data with AI.

To use AI data governance well, companies should:

| Action | Purpose |
| --- | --- |
| Set clear rules | Make sure AI is used right |
| Check AI use | Find and fix problems early |
| Build a good AI culture | Help people use AI the right way |

By doing these things, companies can:

-   Use AI in a good way
-   Be open about how AI works
-   Follow the rules

As AI gets bigger in data management, it's important to:

-   [Keep learning about AI data governance](https://dialzara.com/blog/ai-data-lifecycle-management-complete-guide-2024/)
-   Use the best ways to do things

By taking care of all parts of data governance, companies can:

-   Get the most out of AI
-   Keep people's trust
-   Follow the rules
-   Be responsible for what they do

## FAQs

### What is AI data governance?

AI data governance is about managing data used by AI systems. It includes:

| Key Areas | Description |
| --- | --- |
| Data Quality | Keeping data correct and useful |
| Following Rules | Making sure AI use follows laws |
| Watching AI | Checking how AI works over time |

These steps help companies use AI well and follow the rules.

### What is the difference between data governance and AI governance?

Data governance and AI governance are both important but focus on different things:

| Data Governance | AI Governance |
| --- | --- |
| Makes sure data is correct and safe | Makes sure AI is used well |
| Keeps data private | Checks if AI is fair |
| Follows data laws | Makes AI choices clear |
| Manages all company data | Focuses on AI systems |

Both help companies use data and AI in good ways that follow rules.

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_By Dialzara Team._