Businesses are handling more data than ever, and by 2025, global data is expected to reach 182 zettabytes.
That kind of growth brings messy formats, scattered systems, and mounting pressure to meet stricter rules. When things become disorganized, it’s challenging for teams to locate what they need. Silos build up and manual management eats away precious time.
Luckily today, organizations use AI to power through these challenges. That's because AI can clean, tag, and organize data in seconds, with fewer errors and a lot less stress.
In this article, we’ll explore how AI is helping organizations tackle these challenges and why it’s becoming essential for modern data management.
The key challenges in traditional data management
Even as data grows rapidly, many companies still rely on outdated systems, and that brings serious problems. These issues slow everything down and make it harder to use AI effectively. In fact, 68% of Chief Data Officers report that poor data quality is one of the primary reasons AI projects fail.
Here’s what’s getting in the way:
Data silos: When data is stored in isolated systems (siloes), it becomes harder for people to get the information they need because they have to jump to different locations just to access it. This slows down processes as knowledge workers spend an average of 12 hours a week “chasing data,” in siloes and makes it hard for teams to collaborate, which delays decision-making.
Inconsistent data quality: Without proper rules and structure, data becomes unreliable. That produces errors in analysis, and as a result, decision-makers lose trust in the information they’re using.
Manual data governance: Manual data entry can eat up 1–3 hours a day, and that’s without fixing mistakes. It also increases risks around security and compliance.
Lack of scalability: As the data grows, so do performance issues and costs, as maintaining data quality becomes increasingly challenging. And traditional management procedures aren’t enough for that.
Difficulty in data search: Without proper metadata and documentation, it's challenging to locate the relevant information when needed. 48% of respondents in a survey reported struggling to find documents quickly due to their company’s manual online filing system.
The advantages of data management
Organizations are now realizing that having only a large amount of data isn’t enough. What matters is how quickly and smartly we can use it. That’s why AI-powered data management is gaining ground. Let's see how it can benefit us:
AI-powered data discovery and cataloging
AI analyzes complex data and automates metadata management tasks, such as tagging and sorting data. Instead of typing the exact keyword to search for something, we can ask a question, and the AI will find the most relevant answers. In addition, it provides us with new insights that we may not have known or may have overlooked.
Intelligent data integration and unification
AI unifies disparate data sources (e.g., on-site, cloud, hybrid, etc) through schema matching and data transformation. Schema matching aligns data structures from different sources, while data transformation converts data into a consistent format for analysis or integration.
It also shows the entire journey your data takes, including where it originates, how it evolves, and what it becomes. And with federated querying, you can work across different systems without having to shuffle or migrate anything around. This way, everything stays connected and ready to use.
data.world’s new generative AI-powered governance application is helping organizations put these capabilities into action with Eureka™ Bots. Discover how GenAI automates governance workflows and enhances data team productivity.
Automated data governance and compliance
Instead of relying on manual oversight, AI monitors who access what data, flags risks instantly, and ensures that only authorized personnel can view sensitive information. This proactive approach protects against breaches and simplifies the complex global compliance requirements.
AI also automatically classifies sensitive data, such as personal identifiable information (PII), making it easier to manage securely without unnecessarily restricting access. It maintains full audit trails and generates compliance reports on demand to reduce the heavy manual burden on governance teams. This means that if you have AI on your side, it becomes so much easier to stay compliant.
AI-driven data quality and trust
AI continuously monitors and cleanses data by detecting anomalies and duplicates in real-time. For example, machine learning systems like Anodot’s anomaly detection platform learn what normal looks like by analyzing historical trends, then instantly flag anything unusual as it happens. This always-on vigilance keeps data cleaner and gives teams confidence that the information they rely on is accurate and up-to-date.
Beyond cleaning, AI builds trust by assigning confidence scores and surfacing trust indicators that show how reliable a dataset is. It also enriches raw data automatically by linking it to related insights to create more complete and contextualized datasets for analysis. With smarter, self-healing data pipelines, organizations can allocate less time to problem-solving and more time to making strategic decisions based on trusted information.
Enhancing data usability for AI and analytics
Machine learning models require a substantial amount of clean, high-quality data to function effectively. AI makes this easier by sorting and linking related data points so the models can learn more from the information they are given.
That’s why, with automated data preparation tools, teams spend more time identifying useful trends and less time cleaning up messy data.
Real-world applications of AI in data management
Because of these benefits, AI data management is being used across multiple industries. Let’s see how:
Financial services
In finance, AI identifies fraud and manages risk by continuously analyzing vast amounts of data, including spending patterns, transaction history, and social activity. It identifies anything unusual and alerts the teams to detect fraud more quickly and make informed decisions.
Take Visa, for example — it has spent $3.3 billion on AI and data tools in the last 10 years to detect fraud and authorize payments so customers feel more secure while shopping.
Healthcare
Healthcare companies use AI to manage patient records and stay compliant with privacy laws like HIPAA. AI systems monitor electronic health records (EHRs) in real-time and instantly alert compliance officers if they detect anything suspicious. This way, they can protect patient data and ensure hospitals stay within regulatory standards.
On 15 April 2025, Tempus AI and Illumina partnered to develop precision medicines. Tempus brings its comprehensive multimodal data platform and AI capabilities, while Illumina contributes its expertise in developing DNA sequencing technologies. Together, they’re working to give personalized care using genetic data and make treatments more accurate for each patient.
Retail and e-commerce
Retailers use AI to closely monitor their stock levels. This way, they avoid overstocking and stockouts. What AI does is it analyzes past sales data and market trends to predict customers’ future demand accurately. That’s how they plan effective inventory management to reduce the risk of lost sales due to stock imbalances.
Amarra, a clothing wholesaler based in New Jersey, uses AI to manage inventory and generate product descriptions. AI has helped them reduce excess stock by 40%, speed up content creation by 60%, and chatbots handle 70% of customer inquiries.
Manufacturing
In factories, manufacturers use AI supply chains to predict demands, manage stock, and keep costs low, all while improving production speed. It also helps them automate assembly lines and achieve faster production at lower costs.
As a result, the AI market in the supply chain sector was valued at $4.5 billion in 2023 and is projected to grow at a 42.7% CAGR. Even global giants like Siemens' Electronics Factory in Erlangen use AI-enabled robots to automate assembly lines, which reduces automation costs by 90% and boosts efficiency.
The future of AI in data management
The AI data management market is projected to grow at a CAGR of 22.8% and is expected to reach $70.2 billion by 2028. Let’s see what major trends these intelligent and adaptive AI systems will introduce:
Autonomous data governance
As AI technology advances, we're moving toward fully autonomous data governance, where policies are enforced automatically without human intervention. AI systems will be able to monitor data access, detect risks, and apply compliance rules in real time, all on their own. This shift will help us maintain consistent, accurate governance across massive datasets and minimize the risk of human error.
In the future, autonomous governance will be a key foundation for building more scalable and trustworthy data ecosystems. In fact, AI governance software spending is expected to reach $15.8 billion by 2030, which is four times the amount in 2024.
Self-learning data catalogs
Modern self-learning data catalogs use machine learning to analyze how users interact with data and spot usage patterns over time. This means they are smart enough to help us find the right data and uncover valuable insights faster.
For example, AWS has now added generative AI to its Glue Data Catalog using Amazon Bedrock. This catalog will automate metadata creation using large language models (LLMs) and techniques such as in-context learning and retrieval-augmented generation, taking intelligent cataloging to the next level.
Increased explainability and transparency
As AI plays a bigger role in decision-making, we must understand how those decisions are made. And that’s precisely what Explainable AI (XAI) does. It provides clear justifications for the outcomes AI systems produce. That way, we can build greater trust in how AI works and ensure it meets regulatory standards, too, especially in sensitive areas such as finance and healthcare.
For example, in healthcare, XAI is helping detect potential malignancies in breast cancer screenings. It generates detailed heatmaps that highlight areas of concern in mammograms. This way, radiologists can validate AI findings and make more confident choices about patient care.
Knowledge graphs and adaptive AI
Knowledge graphs enable AI systems to connect the dots and make sense of complex relationships between disparate data. They even enhance the accuracy of LLMs by up to three times, providing AI with a much richer understanding of the world around it. And when we pair knowledge graphs with adaptive AI, it becomes easier to spot hidden insights and view our data in a new, more meaningful way.
How data.world supports AI-driven data management
Everything we’ve talked about — smarter discovery, better governance, and seamless unification — comes together with data.world. It’s a platform built around AI-driven data management principles to help teams find, organize, and trust their data faster. With features like AI-powered discovery, intelligent governance, and automated unification, data.world makes it easier to build a knowledge-first strategy. That way, your AI and analytics teams always have access to clean, high-quality data they can rely on.
Schedule a demo now and see how data.world’s AI data management leads to smarter decisions.
