With the rise of digitalization, billions of people have access to the Internet and browse the World Wide Web at their own convenience. Basically, every action they take online generates new data. 

Billions of people interact with one another and with brands every single day, leading to the generation of data that goes beyond the capability of traditional technology to process it, and we call this big data. According to reports, approximately 402.74 million terabytes of data are created everyday, and 181 zettabytes of data will be generated in 2025. 

Big data encompasses data generated from different sources, including sensor data from IoT devices, medical data, and financial transactions. This is what makes handling data complicated for any organization’s data team. 

From managing data from diverse sources to upholding data integrity, securing the data access, eliminating the silos, and all this while ensuring regulatory compliance. That’s why a standardized set of rules, frameworks, and processes, which is called data governance, helps streamline to establish effective management, quality, security, and utilization of data. 

With AI introduced in every industry and every aspect of industrial operations, imagine implementing advanced AI and ML algorithms in big data compliance to streamline some aspects of data governance. AI in data governance involves implementing a systematic and automated approach to ensuring data quality and integrity. 

In this AI-driven world, organizations must build robust data governance strategies to address the challenges posed by big data. Implementing AI can automate some tasks, like data cleansing and identifying anomalies, making it easier for the data teams to meet regulatory compliance. 

Challenges Faced in Data Governance

Big data is characterized by 5Vs: Volume, Velocity, Variety, Veracity, and Value. All these factors play a significant role in increasing the complexity of managing big data. Let’s understand some challenges that a data team faces day-to-day to manage data and achieve big data compliance: 

1. Data Silos 

 According to Industry Study 2023 commissioned by XPLM, around 76% of respondents agree that data silos hinder cross-departmental exchange. Data silos have increased in more than 40% of the companies, and data silos can cost a company upto 30% of its annual revenue, as IDC Market Research reports. 

Data silos are collections of data that can't be accessed by every department of an organization and are kept exclusive to one or a few departments. It creates problems like integration issues, makes the data non-collaborative, and even makes it hard for the C-suite to have a look into it. 

2. Inefficient Management of Data Inventory

The velocity of data production makes data management almost impossible. All the new data coming in has to be processed and stored in real time, so allocating inventory based on the type of data can be very difficult. 

3. Third-Party Risks, like Data Breaches, Data Control 

Sharing data with third-party organizations is a big concern in data governance. This activity risks the security of the data, introducing risk factors like data breaches that can threaten your organization’s trustworthiness. For instance, Bank of America announced that its customer data was compromised through an Infosys McCamish cyber incident in February 2024. Infosys McCamish reveals that data of around 6.5 million individuals was subjected to unauthorized access and exfiltration. 

4. Complex data privacy, storing, and security regulations

With the growing concerns about data security, it has not been easy to maintain people's trust in your capabilities of storing their data and keeping it private. That's why security regulations and compliance are harder than ever. Now, for a data set with characteristics like being large, exponentially increasing, variety, and many more, security and compliance become an ache. 

5. Maintaining the quality of data

With the large amount of data to handle, it becomes hard for organizations to maintain the quality of the data. Moreover, the “variety” characteristic of big data elevates the burden even more, as the more types of data there are to handle, the harder it will become to manage. 

6. Assigning roles and responsibilities

We can't overlook the fact that big data is not for an individual in an organization. It has to be accessed by multiple departments, which is why the need for well-defined roles and responsibilities arises. 

These are the challenges in data governance that are here because of the characteristics of big data. Is there any solution for these challenges available currently? Well, yes, and it actually involves the hot topic of this decade: Artificial Intelligence. So, let's not move on to learn about how AI is helping in the governance of big data. 

How AI Helps in Data Governance? 

Data governance is about establishing a framework or system of decisions that govern the rights and accountabilities regarding the storage and management of data. Hence, three important pillars form the foundation of a successful data governance strategy: People, Process, and Technology. 

Effective data governance includes creating a data governance team that fosters a culture of ownership in the organization. Then, it involves setting up documented policies that clarify how data should be collected, stored, processed, and shared. 

The last pillar is technology, where advanced technology, like AI in data governance, is used to enhance efficiency and maintain the effectiveness of implemented data governance policies. Let’s see how AI helps streamline data governance and how it enables organizations to comply with regulatory compliances like GDPR and CCPA: 

1. Improve Data Quality 

With AI tools and models capable of automated data cleansing, standardization, and validation, we can ensure the data being stated and used is of high quality. For instance, Trajektory, Sweephy, and causaLens are some companies that offer AI-based data cleaning and aggregation software. 

Moreover, we can also deal with duplicate data, which will significantly impact the issues raised by data volume and velocity. With the development of AI, it's helpful to feed these models the right and accurate data for accurate outcomes. 

2. Reveal Data Lineage 

While it's not humanly possible to track the origin of data along with all the transformations that happened to it until it is submitted to the final data set, AI can do it with more precision. With this capability, we can get the full traceability of the big data that you are using in the organization. 

3. Automate Data Classification 

Data classification can be automated with AI to deal with a variety of data formats in big data. It helps to classify data into structured and unstructured and further classify it into a particular format like image, video, or text. Hence, asset tagging becomes easier, leading to not only better organization of data into various types but also accurate tracking of the respective companies. 

4. Build a Data Glossary

To combat data centralization and easy accessibility, AI can be used to tag data assets with auto-generated descriptions. Since the descriptions will follow a specific pattern, it will be easier to access the data from a centralized database, making data governance top-notch. 

5. Enhance Privacy and Security 

Big data is a mix of various data types, which we already discussed. But there's one more thing to add: the mixture of sensitive data in big data. Yes, there can be a lot of sensitive data with a big data set that needs to be filtered out at the right point. AI can do this by detecting a difference between the pattern of sensitive and non-sensitive data. So, issues like data breaches can be controlled during third-party access. 

6. Monitor the Data in Real Time 

And now to the most important challenge: real-time monitoring. AI systems can do it better than humans. The significant difference between us and AI is that it may flag a possible issue even before its occurrence. 

For instance, Mastercard has launched Decision Intelligence Pro, which is a Gen AI-powered transaction risk assessment tool. It scans an unprecedented one trillion data points to predict the likelihood of genuine or false transactions in real time. It can monitor unusual spending patterns, and its initial modelling shows that the AI tool can enhance fraud detection rates by 20%.

AI Use Cases in Improving Data Governance and Compliance

AI in data governance isn’t limited to theoretical benefits—it’s already transforming key business functions. So, let's look at some of the implementations of AI that are improving data governance and compliance. 

1. Sales Optimization

According to Gartner, 65% of B2B sales will become data-driven instead of intuitive by 2026. What does that mean? In sales today, pitches are created on the go with intuition, making it more of a luck-based strategy. 

But with AI real-time data processing, the sales department can have access to insights that can help them create data-backed pitches in real-time. 

2. Predictive Maintenance

Predictive maintenance helps prevent unwanted events in industries that run on manufacturing or depend on heavy machinery and vehicles. Let's understand this one with an example. 

If only a single machine stops in a manufacturing unit, it will affect the whole unit. But what if you already know which machine can fail? Predictive maintenance is what it is and works with the help of ML and IoT-like concepts. 

3. Personalized Marketing

With AI, marketers can now create targeted campaigns while adhering to GDPR and other privacy regulations. This means targeting the customer through marketing by making the campaigns more aligned with what customers actually want. Around 44% of consumers actually don't have any problem if an AI recommends things to them. 

4. Project Management

Last but not least, AI tools help track data dependencies and compliance metrics in large-scale projects, reducing risks. Project management is beyond just getting it completed. It extends to the compliance of laws and regulations as well. AI will exactly help you with that while also dealing with common issues like time allocation, budget constraints, and efficient workforce allocation. 

Future Trends of AI in Data Governance 

AI technology is ever-evolving because there are gaps in the current technology that need to be bridged. For instance, AI models are now trained to provide recommendations, like predicting the risk of developing diabetes in a patient by analyzing the patient’s medical records, history, reports, and lifestyle factors. However, how will the doctor understand on what basis the decision has been made if the AI model labels the patient as high-risk?

This lack of transparency needs to be resolved so we can trust AI models' decisions. This introduces explainable AI. 

Explainable AI can help in meeting data governance compliance, ensuring all the features used by AI in data governance are well-documented and not based on any bias. It can maintain records of AI models, data versions, and decision-making processes to support the auditing process. 

Besides, as big data grows, high-performance computing will be required to enable the development of large-scale models capable of handling increasingly complex datasets. Thus, the boundaries that currently limit AI in data governance will be stretched further. 

Another significant trend will be focusing on producing synthetic data to address privacy concerns and data scarcity. Hence, with the use of less real data, a lot of synthetic data will be produced with similar outcomes to those expected from real data.

Soon, AI models will be trained on decentralized data, meaning they will have a separate knowledge base. This is great for ensuring privacy and security while collaborating without compromising sensitive information.

Conclusion 

The importance of data governance cannot be overstated for big data. The challenges mentioned above need innovative solutions, and AI provides the tools needed to navigate this evolving landscape. While we are already using AI for multiple tasks and are set to elevate its use, the future of AI in data governance is even brighter. AI is going to impact the technological constraints of data governance and make it easier to handle big data.