AI Data Management: How AI Can Improve Data Operations

Nowadays, information is more than just a resource, it’s a strategic advantage. Yet as businesses generate and store more data than ever before, traditional data management methods are struggling to keep up. Manual processes often lead to costly delays, human error, and compliance challenges. Enter AI: a powerful tool that’s transforming how organizations collect, organize, and leverage their data.

In 2024, the size of the global AI data management market hit $31.42 billion, and it’s just getting started. By 2030, it is projected to surge to $107.92 billion, growing at a CAGR of 22.8%. This explosive growth highlights the increasing demand for intelligent, automated data solutions.

In this article, we invite you to learn more about the use of AI for data management and why it’s becoming an essential part of modern business operations.

What is AI Data Management?

Such an approach enhances scalability, automation, and precision across data operations. This enables organizations to treat data as a dynamic, strategic asset.

In traditional data management, all data-related processes are governed by human-defined rules. In AI data management, AI and ML models actively learn from patterns in data, adapt to changes, and optimize these processes with minimal manual intervention.

Why does AI data management matter?

• Larger scale. AI can efficiently handle significantly bigger data volumes than people can do manually.
• Higher speed. Automated pipelines help teams reduce latency at all the steps from data creation to insight delivery.
• Trust and data quality. AI-powered software tools can improve data reliability and transparency by validating data sets and detecting anomalies in them.
• Productivity. Business users can rely on AI-driven analytics and insights in decision-making and planning.
• Adaptability. AI systems continuously learn from data behavior and evolve in real time.
• Cost-efficiency. Artificial intelligence for data processing helps eliminate a huge part of manual tasks, duplicate work, and errors. This greatly cuts operational costs.

AI and Data Management: Use Cases

Let’s consider the role of AI in data management based on the real-life applications of this technology.

AI data quality monitoring and data cleaning

ML algorithms are good at learning typical data patterns and detecting anomalies, duplicates, and missing values. They can immediately alert teams when deviations occur.

ML-based tools can fill in missing data and auto-correct formatting issues using statistical and contextual analysis.

Data integration

AI can efficiently combine structured and unstructured data from multiple sources into a unified system.

It automatically maps fields between disparate datasets, identifies conflicts during merge operations, and dynamically deals with all changes in source systems. This ensures faster onboarding of new data sources and reduces errors in consolidated data.

Data classification and tagging

AI models extract context from unstructured data in different sources and auto-tag it. Moreover, they can classify data into categories like finance, HR, or marketing based on patterns and update tags as data evolves. Such steps are crucial for effective search, filtering, and policy enforcement.

Data storage optimization

How data is stored is one of the aspects that has a direct impact on its performance and your operational costs.

AI algorithms can predict storage growth and usage trends for scaling. They can recommend data tiering according to access frequency, as well as detect redundant data that can be deleted. This will help you avoid overpaying for storing the data that you don’t need anymore.

Data search

AI data management systems allow users to find the necessary data via natural language search interfaces. It greatly streamlines and facilitates the processes of obtaining access to the right data sets or values. Moreover, AI can rank the results based on their relevance and suggest related data or queries.

Data analytics

AI can turn raw data into actionable insights with the help of visualizations and models. It can generate dashboards automatically based on frequently asked questions and detect direct trends or correlations across large data sets. This allows teams to significantly enhance decision-making across departments.

Backup and recovery

Artificial intelligence helps ensure data is protected and recoverable in the case of a failure or breach. AI systems can predict failure-prone datasets and prioritize them for backup.

Apart from this, they can accelerate recovery by recommending the most relevant restore points. All this minimizes data loss risks and downtime during recovery.

How Does AI Affect Data Governance and Data Security?

Traditionally, data governance and security involve manual checks, static rules, and periodic audits. Today, AI is transforming the way organizations manage and protect their data.

AI can be used for:

• Smart data classification. AI can automatically identify and label sensitive data, even that data that is hidden in emails, documents, or images.
• Faster threat detection. AI is applied to monitor user behavior and system activity to detect unusual patterns.
• Automated policy enforcement. AI will help you make sure your company’s workflows and data management are compliant with regulations.
• Real-time access control. While traditional access control tools usually follow fixed rules, AI can adjust access permissions based on different parameters, like location or time.
• Better visibility and lineage. AI tracks the entire journey of your data. It means that it monitors where data comes from, how it’s transformed, and who has used it. All this helps detect risks as early as possible.
• Automation. AI automates repetitive tasks. As a result, it ensures more cost-efficient audits, better user experience, and more scalable operations.

The Best AI Data Management Tools

Today, there is a wide range of off-the-shelf software solutions for data management. We will mention just a few of them that have already managed to earn an excellent reputation in the market.

Google Cloud’s BigQuery

It is a fully managed, serverless data warehouse that can analyze massive volumes of structured and semi-structured data.

BigQuery is often used by enterprises that need to consolidate multiple data sources and power their workflows and dashboards with data processed in real time.

https://cloud.google.com/bigquery

With this solution, organizations do not need to handle any infrastructure management tasks on their own and can benefit from advanced AI/ML and governance features. Nevertheless, this tool must be used within the Google Cloud ecosystem. This can limit its interoperability with other platforms.

Ataccama ONE

It is a data management platform built for data quality, cataloging, and governance. It enables organizations to build a reliable, governed data environment across hybrid and cloud deployments.

https://www.ataccama.com/platform

Complex tasks are automated with the platform’s AI engine dubbed ONE AI. Artificial intelligence generates data quality rules from natural language, detects anomalies, suggests corrections, as well as automates catalog tagging and classification.

Collibra

Collibra is an enterprise-grade platform for data governance, cataloging, lineage, and quality. It provides unified governance across data assets and AI systems, as well as offers automated compliance tooling for regulatory alignment and audit readiness.

https://www.capterra.pl/software/145719/collibra

With the help of this AI management software for data, organizations can execute enterprise-scale lineage, cataloging, and policy enforcement across multi-cloud sources.

Final Word

Today, AI and data management are closely tied. Artificial intelligence is changing how businesses work with their data. By automating routine tasks and applying ML to optimize decisions, organizations can successfully scale their data operations.

Thanks to the new opportunities it opens, from being an innovation, AI is gradually becoming a golden standard and a must in data management.