Data governance is critical for data-driven organizations. As data continues to become more central to business operations, governance measures are needed to keep data secure, accurate, and updated.

Organizations need to update and check their data governance strategy and policies continually. Staying on top of data governance isn’t easy, but reading up on the current trends can help. In this blog post, we’ll take a look at some of the top data governance trends and predictions for 2024, so you can stay ahead of the curve.

Real-time data processing

Real-time data processing is a key trend that will shape the data governance landscape in 2024. Organizations that want to make frictionless, efficient data-driven decisions need to process data in real time. This can be costly and resource-intensive and isn’t always necessary for every business, but as technology evolves, more and more tools are making real-time data accessible to businesses of all sizes.

Typically, data processing is done in batches, which can still be effective. However, real-time data will quickly become king in 2024 and beyond. Real-time data allows businesses to be fleet on their feet and get the insights they need faster, allowing them to maintain a competitive edge.

However, real-time data needs to be protected and secured just as any other important or sensitive data you collect. To make the most of real-time data, robust data governance policies and processes need to be in place. Real-time data will need to be accessed by the right people at the right time and managed properly, so businesses can make the most of this advantageous technology.

Automation as a way to efficiently scale

Automation is more prevalent than ever, and automating routine data governance tasks can save businesses time, money, and risk. Automation can help remove the human error-prone element of tedious data management tasks, allowing data stewards to focus on more complex tasks and the big picture.

Automation can also help with self-service analytics by automatically granting users the right permissions, monitoring data usage, and alerting the system when there is a problem. Automation also helps to improve data quality and consistency, which helps with compliance and governance.

The use cases for automation in data governance are vast, allowing organizations to automate workflows for data issue resolution, data lineage, data policy management, and more.  

Overall, automation will play a crucial role in the future of data governance. Organizations that invest in automation will not only streamline their data governance processes but also enhance their data quality, security, and compliance.

Increased adoption of Artificial intelligence (AI) and machine learning

AI and machine learning are becoming prevalent technologies in numerous industries, and they are being used in data governance as well. In 2024 and beyond, the adoption of AI and machine learning will continue to become more widespread in data governance and other key business processes.

Artificial intelligence and machine learning offer numerous benefits for data governance, allowing data governance policies to be implemented more efficiently and effectively. AI and machine learning can help organizations quickly identify anomalies and inconsistencies in data that may have gone unnoticed otherwise. These systems being able to detect data errors helps improve the accuracy and reliability of data overall.

AI and machine learning are also becoming extremely useful for data mapping and pattern recognition. These technologies allow businesses to automate the data discovery process.

In the data governance sense, AI and machine learning can also help detect and prevent data breaches. AI and machine learning algorithms can be trained to detect suspicious behavior and report to data teams to take action.

An example of a useful AI tool to help with governance is Secoda AI, which stacks OpenAI on top of your metadata, allowing users to get contextual search results from across your tables, columns, dashboards, metrics, etc, and generate queries from those answers. Other features allow users to auto-generate documentation, auto-tag PII data, and auto-tag columns. It puts more time in the hands of users without compromising your data governance.

Overall, AI and machine learning are set to transform data governance practices in 2024 and beyond. As the adoption of these technologies continues to grow, businesses that embrace them will gain a competitive advantage by being able to make better decisions faster and more accurately than their competitors.

Data governance for unstructured data

As businesses collect increasing amounts of unstructured data, there is a burgeoning need to establish governance frameworks to manage this information effectively.

Unstructured data can sometimes be more difficult to process and analyze compared to structured data, and it can contain sensitive information. Businesses must establish robust data governance strategies and policies for the collection, storage, usage, and access of unstructured data in 2024 and beyond. Many automated tools can help make this process simpler and reduce the chance of errors or breaches.

Data democratization

Data democratization is becoming an increasingly popular concept in data-driven organizations. With data democratization in place, data is more accessible and usable for everyone, even non-technical users. In the past, most data requests would have to be filtered through the data team, leading to bottlenecks and data silos. With data democratization, users are empowered to make data-driven decisions and glean insights without having to rely on the data team.

Self-service analytics is a prominent trend in data democratization, as these platforms allow users to easily make requests and get feedback without having to have extensive technical knowledge or expertise. Data discovery platforms like Secoda help to enable self-service analytics, making data discovery as easy as conducting a Google search.

However, it’s important to consider the data governance implications of data democratization. While more users having the ability to access data easily is great for efficiency and effectiveness, it also requires strict access management protocols. Data governance policies should be in place to ensure data is being used responsibly and by the right people.

Overall, data democratization is an important trend that can help organizations unlock the full potential of their data. By making data accessible to everyone, organizations can make better decisions, drive innovation, and stay ahead of the competition. With proper governance in place, data democratization can be a secure innovation as well.

Data monitoring and data ineage

There is a growing emphasis on data monitoring and data lineage, ensuring the quality, integrity, and security of organizational data. Data monitoring and data lineage are closely interconnected in the context of data governance. Data lineage, which involves tracking the flow of data through different systems and transformations, plays a crucial role in ensuring data quality. It enhances data quality by revealing how data is transformed and detecting errors or inconsistencies. By providing a clear understanding of where the data originated, how it has changed, and its ultimate destination within the data pipeline, data lineage supports data governance efforts by tracking data flow and maintaining policy adherence

Data lineage enables impact assessment, which predicts the effects of changes on downstream processes, making troubleshooting more efficient and supporting lifecycle management and change management. Therefore, data lineage is instrumental in ensuring data quality and supporting overall data governance efforts.

Ethical considerations

As the ability to collect, access, and use data becomes more widespread and accessible to businesses of every size, ethical considerations are a bigger part of the conversation than ever. When customers trust businesses with their data, these businesses need to ensure this trust is well placed.

Ethical considerations can encompass a wide range of issues when it comes to data governance, including privacy, security, consent, and usage. Businesses need to be careful to strike the right balance between using data for insights and each individual’s right to privacy. As data breaches and misuse are on the rise, it’s more important than ever for businesses to ensure their customers are protected. 

Another important ethical consideration in data governance is ensuring data collection is transparent. Letting customers know about data collection and what it’s used for is crucial, as is giving them the option to opt out of data collection. Being more transparent about collection practices will create a more trusted relationship between businesses and the customers they rely on for data.

Finally, it’s worth considering the ethical implications of AI and machine learning when building your data governance strategy. As AI continues to evolve, some kinks are still being worked out. Depending on the model, algorithms have the potential to perpetuate biases if they aren’t trained and tested properly. Businesses should make sure to implement monitoring and audits in their AI and machine learning governance policies to ensure the use of these technologies is ethical and fair.

Privacy and security concerns

Though it may be obvious, privacy and security concerns still need to be continually reevaluated and assessed in 2024 data governance strategies. With companies collecting more personal data than ever before, security needs to be prioritized to protect the data and ensure it’s used ethically.

The increasing importance of data privacy is evident in the introduction of stricter regulations in recent years in the form of measures like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations have increased the level of accountability for companies and have forced them to be more transparent about the way they collect, process, and store data.

Fortunately, data security and privacy measures have evolved as well, and measures such as encryption, access controls, and monitoring can help reduce data breaches and ensure proper usage of data.

Overall, companies need to prioritize privacy and security concerns in their data governance policies. By doing so, they can ensure that they are not only complying with regulations and avoiding fines but are also protecting their customers and their reputation.

Cloud-based governance

More companies are moving their data to cloud environments, so cloud-based governance will become a much more prevalent concern in the coming years. 

The same benefits that come with moving to the cloud need to be considered when building data governance policies. Since cloud-based data can be accessed from anywhere, data governance should make sure security measures protect this data and access controls are properly implemented. Cloud environments are also highly scalable, so organizations need to be able to keep up with security and privacy concerns as their databases grow and take in more data.

In summary, cloud-based governance is becoming increasingly important as more data is moved to the cloud. It allows for greater scalability, accessibility, and collaboration, but it's important to consider privacy and security concerns carefully. With the right governance strategy in place, companies can confidently move their data to the cloud and reap the benefits of this modern solution.

Try Secoda for Free

As mentioned, Secoda is an ideal solution for improving your data governance measures. With Secoda’s AI tool, automated data lineage, and other features, you can automate many of the tedious, error-prone data governance tasks. Secoda is also an all-in-one data management platform with tools for data discovery, data cataloging, data dictionaries, and much more. Schedule a demo for Secoda or try it out for free today to see how it can improve your organization’s data governance.

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