What is AI data management?

Both traditional, rules-based AI and more advanced generative AI models can help with data management.

Modern enterprises own vast amounts of data on everything from financial transactions and product inventory to employee records and customer preferences. Organizations that use this data to inform decision-making and drive business initiatives can gain significant advantages over their competitors.

However, the challenge comes from making these large datasets accurate, reliable and accessible enough for people to use them in practice.

The IBM® Data Differentiator reports that 82% of enterprises experience data silos that stymie key workflows. As much as 68% of organizational data never gets analyzed, meaning the business never realizes the full benefit of that data.

AI and ML tools can help organizations put their data to use by optimizing tasks such as integrating data sources, cleaning data and retrieving data. As a result, businesses can make more data-driven decisions.

AI data management also helps organizations build the pipelines of high-quality data they need to train and deploy their own AI models and machine learning algorithms.

Many types of data management tools—such as data storage solutions, data integration tools, master data management tools, governance solutions and others—now incorporate ML and AI capabilities. These tools can use both traditional AI algorithms and generative AI systems.

• Traditional AI systems perform specific, rule-based tasks—for example, a database management system that automatically categorizes data based on predefined criteria.

• Generative AI systems, such as Microsoft Copilot, Meta’s Llama and IBM Granite™, respond to natural language and create original content. For example, a database management system with an integrated large language model (LLM) can create summaries of data and accept queries in plain English instead of SQL.

AI and ML can fit into nearly any part of the data management process, but some of the most common use cases include:

• Data discovery
• Data quality
• Data accessibility
• Data security

Organizations today work with a lot of data, which comes to the business from multiple different sources, in multiple formats. This data is handled by various users and ends up scattered across public and private clouds, on-premises storage systems and even employees’ personal endpoints.

It can be hard to centrally track and manage all of this data, which raises two problems.

First, an organization cannot use a dataset if it does not know that the dataset exists.

Second, this undiscovered and unmanaged “shadow data” poses security risks. According to IBM’s Cost of a Data Breach Report, one-third of data breaches involve shadow data. These breaches cost USD 5.27 million on average—16% more than the overall average breach cost.

AI and ML can automate many aspects of data discovery, granting organizations more visibility into, and control over, all their data assets.

Examples of AI in data discovery include:

• AI-powered data discovery tools can automatically scan network devices and data storage repositories, indexing new data in nearly real time. 

• Automated data classification tools can tag new data based on predefined rules or machine learning models. For example, the tool might classify any nine-digit number in the XXX-XX-XXXX format as a US social security number. 

• LLMs and other natural language processing tools can extract structured data from unstructured data sources, such as pulling job candidates’ contact details and past experience from text-document resumes with varying formats

Bad data can cause more problems than no data at all. If an organization’s data is incomplete or inaccurate, then the business initiatives and AI models built on that data will also be subpar.

AI and ML tools can help identify and correct errors in organizational data, meaning users don’t need to do the time-consuming work of manual data cleansing. AI can also work more quickly and catch more errors than a human user.

Examples of AI in data cleaning include:

• AI-enabled data preparation tools can perform validation checks and flag or correct errors such as improper formatting and irregular values. Some AI-powered data preparation tools can also convert data to the appropriate format, such as turning unstructured meeting notes into structured tables.

• Synthetic data generators can provide missing values and fill other gaps in datasets. These generators can use machine learning models to identify patterns in existing data and generate highly accurate synthetic datapoints.

• Some master data management (MDM) tools can use AI and ML to detect and correct errors and duplicates in critical records. For example, merging two customer records with the same name, address and contact details.

• AI-powered data observability tools can automatically generate data lineage records so that organizations can track who uses data and how it changes over time.

Data silos prevent many organizations from realizing the full value of their data. AI and ML can streamline data integration efforts, replacing siloed repositories with unified data fabrics. Users across the organization can access the data assets they need when they need them.

Examples of AI in data access include:

• AI-enabled data integration tools can automatically detect relationships between different datasets, allowing the organization to connect or merge them.

• Metadata management tools with AI capabilities can help automate the creation of data catalogs by generating descriptions of data assets based on tagging and classification.

• Databases and data catalogs with LLM-powered interfaces can accept and process natural language commands, allowing users to find data assets and products without writing custom code or SQL queries. Some LLM-powered interfaces can also help users refine queries, enrich datasets or suggest related datapoints.

• AI-enabled query engines can use machine learning algorithms to improve database performance by analyzing workload patterns and optimizing query execution.

There is a business case to be made for prioritizing data security. The average data breach costs an organization USD 4.88 million between lost business, system downtime, reputational damage and response efforts, according to the Cost of a Data Breach report.

AI and ML can help enforce security policies, detect breaches and block unauthorized activities. Examples of AI in data security include:

• AI-driven data loss prevention tools can automatically detect personally identifiable information (PII) and other sensitive data, apply security controls and flag or block unauthorized use of that data.

• Anomaly-based threat detection tools such as user and entity behavior analytics (UEBA) and endpoint detection and response (EDR) use AI and ML algorithms to monitor network activity. They detect suspicious deviations from the norm, such as a lot of data suddenly moving to a new location.

• LLMs can help organizations generate and implement data governance policies. For example, in a role-based access control (RBAC) system, an LLM can help the security team outline the different kinds of roles and their permissions. The LLM might also help convert these role descriptions into rules for an identity and access management system.

• AI-enabled fraud detection tools can use AI and ML to analyze patterns and spot abnormal transactions.

AI can help transform data management by automating arduous tasks such as data discovery, cleaning and cataloging, while streamlining data retrieval and analysis. Organizations can build more efficient data management processes that are less prone to errors and more conducive to data science, AI initiatives and data privacy.

In AvePoint’s AI and Information Management Report, 64% of surveyed organizations said they managed at least one petabyte of data.¹ For perspective that’s equal to roughly 9 quadrillion bits of information. And much of it comes in unstructured formats, such as text files, images and video.

All this data can be a boon for data scientists, but it is impossible to manually manage such complex data in such massive quantities. AI and ML tools can make this data usable by automating critical tasks such as discovery, integration and cleaning.

When data is clean and accessible, organizations can use it for advanced data analytics projects, such as a predictive analytics initiative that uses historical data to forecast future trends in consumer spending.

AI technologies can also make data more accessible to users without data science backgrounds. User-friendly data catalogs with LLM-powered database interfaces and automated visualizations enable more users throughout the business to use data to inform their decisions.

59% of CEOs surveyed by the IBM Institute for Business Value believe that an organization’s competitive advantage in the future depends on having the most advanced generative AI. To build and deploy those AI models, organizations need steady streams of good, clean data.

By streamlining data management, AI tools help build the trustworthy, high-quality data pipelines organizations need to train their own AI and ML models. And because these models can be trained on the business’s data, they can be trained to perform tasks and solve problems specific to the business and its customers.

AI-enabled security and governance tools help fend off cyberattacks and data breaches, which can be costly. They also allow enterprises to use the data they have while complying with data privacy and protection regulations like GDPR and the Payment Card Industry Data Security Standard (PCI-DSS).

According to the Institute for Business Value, 57% of CEOs say that data security is a barrier to adopting generative AI. 45% say that data privacy is also a barrier. These barriers can be especially challenging in highly regulated industries, such as healthcare and finance.

AI-enabled data management can help by automatically applying appropriate protections and data use policies. That way, only authorized users can access the data, and they can use it only in ways that industry regulations and company policy allow.

Synthetic data generators can also help by generating datasets that accurately reflect overall trends while removing sensitive personal data that an organization might not be allowed to use in certain ways.