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  • Writer's pictureKarol Tajduś

Gen.AI & Data Governance. Good Idea?

There is hype on Gen.Ai right now, you know it I know it. It looks like we are forgetting about other data concepts right now and focusing on what Artificial Intelligence can do for us and how to harness it. But maybe Gen.AI can help us with this part of Data Transformation with which we are all struggling the most.


Just imagine it - you are Data Steward in the company, and you do not have to spend time figuring out why your data are corrupted, where are your data, or what to do with them. You have your Artificial companion that guides you and is doing all these tasks that you hate, without any complaint.




A little context and what is what:


Generative Artificial Intelligence (Gen.AI): This field of AI focuses on creating something new based on the patterns and structures it learns from the provided data. It's used widely in creating music, images, text, and other forms of media or data. Deep learning methods, such as Generative Adversarial Networks (GANs), are popular techniques in generative AI. They can produce highly realistic outputs by having two neural networks contesting with each other, resulting in continuously improving output.


Data Governance: This encompasses the principles and practices that ensure the quality, consistency, availability, and security of the data in an organization. Key elements include data quality management, data integration, data privacy, data lifecycle management, and regulatory compliance. Effective data governance provides trustworthy and actionable data, leading to informed strategic decision-making, increased operational efficiency, and ensured regulatory compliance.


Now the ideas for using Gen.AI and other AI algorithms in the Data Governance Domains:


The integration of AI into data governance promises numerous potential benefits, acting as a transformative force in data management, quality assurance, classification, security, and more.

  1. Improved Data Quality: AI's machine learning capabilities enable it to learn from patterns and correct errors in data automatically, ensuring higher levels of data accuracy and consistency. It can flag anomalies, validate data entries against predefined rules, and cleanse data, thereby significantly improving data quality and reliability.

  2. Streamlined Data Classification: AI can be trained to understand complex data taxonomies and assist in classifying data more accurately and quickly. By analyzing patterns in data and applying learned categories, AI can autonomously categorize data into appropriate classes, making it easier for organizations to retrieve and utilize their data effectively.

  3. Enhanced Data Security: AI can play a pivotal role in enhancing data security. By learning from historical data about security breaches and threats, AI can predict and identify potential security risks, bolstering an organization's defense mechanisms against data breaches. Additionally, it can ensure adherence to data privacy regulations by tracking and controlling who accesses what data and when.

  4. Optimization of Data Lifecycle Management: AI can oversee and manage the entire data lifecycle, from creation or acquisition to archival or deletion. It can automate various processes, such as data transformation, loading, storage, and archiving, thereby increasing operational efficiency.

  5. Better Compliance Management: Regulations around data usage and storage are constantly changing and can be difficult to keep up with. AI can be programmed to stay updated with the latest regulations and ensure that the organization's data practices comply with these regulations, reducing the risk of non-compliance penalties.

  6. Enriched Decision-Making: With AI's ability to analyze large datasets, it can provide meaningful insights and predictions that aid strategic decision-making. It can identify patterns and trends that humans might overlook, facilitating data-driven decision-making and thus contributing to business growth and competitiveness.

Generative AI:

Synthetic data is artificial data generated via algorithms, instead of being collected from real-world events. The application of Gen.AI in synthetic data generation and usage can indeed bring significant benefits to data governance:

  1. Data Privacy Compliance: Synthetic data can help overcome privacy restrictions and compliance issues associated with the use of real-world, sensitive data. By creating a synthetic dataset that maintains the statistical characteristics of the original data without identifying individuals, Gen.AI can enable secure data analysis while complying with privacy laws.

  2. Data Augmentation: In situations where the available real-world data is insufficient for analysis or training machine learning models, Gen.AI can generate synthetic data to augment the existing data. This can improve the performance and accuracy of AI models, particularly in fields where data is scarce or hard to collect.

  3. Scenario Testing: Synthetic data can be useful for creating specific scenarios to test the robustness of machine learning models. Gen.AI can generate data that simulate rare but crucial events, allowing organizations to better prepare for various circumstances without waiting for them to occur in the real world.

  4. Improved Data Quality: Synthetic data generation by Gen.AI can help mitigate problems with real-world data, such as biases, noise, and missing values. By creating balanced and complete synthetic datasets, Gen.AI can enhance data quality, ensuring more reliable and fair outcomes from data analysis and machine learning models.

  5. Cost-Efficiency: Collecting and annotating real-world data can be expensive and time-consuming. Synthetic data, on the other hand, can be produced quickly and efficiently by Gen.AI, saving both time and resources.

Seems interesting? Well this is already being achieved by a couple companies and vendors - here is link to Google help: https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance


Generative Artificial Intelligence (Gen.AI) presents a transformative potential in data governance. It's capable of improving data quality by learning from patterns and correcting errors, streamlining data classification, enhancing data security, optimizing data lifecycle management, ensuring compliance with ever-changing regulations, and facilitating data-driven decision making. The capacity of Gen.AI to generate synthetic data opens further possibilities. It allows for privacy-compliant data analysis, augmentation of existing data sets, scenario testing for machine learning models, and cost-efficient data collection and annotation. By integrating Gen.AI into data governance, organizations can streamline their processes and enhance the efficiency and effectiveness of their operations.


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