AI for Data Management in 2025: Best Practices, Tools, Use Cases

Key takeaways

• Effective data management combines the identification, collection, access, and governance of organizational data to ensure this valuable asset drives informed decision-making.

• Organizations face many data challenges, which data management aims to mitigate by using a structured approach to data discovery, access, governance, compliance, and more.

• AI effectiveness requires well-managed data to avoid hallucinations and inaccuracies, and AI itself is becoming crucial to data management for security, automation, and more.

• Data management fundamentals include creating a single source of truth for data, eliminating data silos, ensuring data cleanliness, and improving the reliability of data for data consumers.

• As more regulations like GDPR and CCPA govern the use of data, data management strategies provide the necessary analytics, reporting, and auditing capabilities to streamline compliance.

• Organizations using data management practices benefit from easier data accessibility, faster and more accurate decisions, increased productivity and customer satisfaction, and improved business performance. 

Introduction

The importance of effective data management is paramount to growth and competitiveness, especially in today's environment of hyperspeed, volatility, and uncertainty As organizations increasingly rely on data to drive business decisions and power AI-driven innovations, the sheer volume and complexity of data can present significant challenges.

Data management encompasses a broad spectrum of practices and processes aimed at identifying, collecting, securing, governing, and enabling access to data in a structured and efficient manner. This article explores the fundamental principles of data management, strategies and techniques employed to navigate the growing data landscape, and many benefits of adopting modern data management practices. 

From enhancing security and compliance to improving decision-making and customer satisfaction, effective data management is not just a necessity for every organization today; it is a strategic imperative as organizations work to harness the true value of data.

What is data management? 

As organizations continue to collect more and more data, it’s not enough to store the data and offer access. Organizations must understand current and oncoming data sources, how teams use data, how data is secured, and how it’s all stored, moved, processed, and secured efficiently and effectively.

This is data management: the combined processes for collecting, processing, and ensuring the security of data to drive business decisions and outcomes.

Data management further encompasses data quality, consistency, governance, modeling, and architecture. Data management is a daunting task for most organizations, especially as they scale operations and deal with the explosion of data sources and volumes.

Of course, data is the lifeblood of all modern organizations, regardless of size or industry. It’s a highly valuable resource that must be nurtured, managed, and supported with appropriate investments to maximize value. However, those investments are expected to return value in increased revenue, profits, and growth. Some benefits of effective data management are increased productivity, agility, forecasting, security, compliance, and customer satisfaction.

Using data management techniques and strategies

As the digital age dawned and cheap cloud storage eventually enabled the collection of nearly limitless data, organizations faced the challenge of duplicate, conflicting, incomplete, and otherwise “dirty” data. Worse yet, as data is enhanced, updated, and/or changed over time, data becomes stale, inaccurate, inconsistent, and unreliable.

To combat these and other data challenges, data management efforts seek to find, collect, clean, connect, secure, and enable access to the correct data by the right workers. Creating a data management practice helps organizations develop and apply best practices for the storage, use, backup, cleansing, governance, and eventual retirement of data across its lifecycle. 

Organizations could then work strategically to reduce data silos, conflicts, and misuse. Master data management (MDM) has emerged as a comprehensive approach to managing and organizing data to avoid duplication, inconsistency, and other issues by creating the so-called “single source of truth” for organizational data. 

Beyond MDM, accompanying data management processes have been supported by approaches and architectures like data warehouses, data lakes, and data lakehouses to consolidate and streamline data management and usage. Amusingly, when things go wrong in a data lake, it turns into what many call a “data swamp”.

Data management teams use MDM, data lakes, and other on-premises and cloud-based approaches and technologies to integrate data from various sources strategically. This enables a broad view of data with streamlined governance, access, and analytics to ensure proper usage and accurate insights. That trusted data then becomes the source for data consumers as they use business intelligence and analytics tools, build AI models, and more.

Data governance takes on additional importance for modern organizations, especially with regulations like the General Data Protection Regulation (GDPR), California’s Consumer Privacy Act (CCPA) and Privacy Rights Act (CPRA), and potential new legislation. These rules generally cover how consumer and personal data is managed and processed. Data management practices must consider these and other regulations, providing analytics, auditing, and reporting tools that streamline and simplify compliance.

Benefits of data management

When data is managed effectively, downstream data consumers, business analytics, and other tools and systems generate results, recommendations, forecasts, and other outputs with accuracy that drives confidence. 

Here are a few common use cases and resulting benefits for data management techniques:

Enhanced security and compliance

When data is easily understood and accessed, it’s also easier to secure. Data management processes enable organizations to easily place guardrails around data, such as access controls. These guardrails help insulate organizations from potential fines and brand damage, while easy access improves auditability and reporting capabilities to streamline compliance with privacy and security regulations and policies.

Improved data access for better decision-making

More systems, applications, and solutions lead to more potential data silos, where data is confined to a single tool or incomplete data is aggregated in various systems. Eliminating data silos is a challenge for nearly every organization. However, a good data management strategy using tools like data lakes brings data into a central repository that overcomes siloed data. Data management also helps identify siloed data across departments and tools to improve the comprehensiveness of the aggregated data. Ultimately, access to more and complete data improves analysis so leaders can make better and faster decisions with more confidence.

Increased customer satisfaction

Data customers, external customers, and internal workers will benefit from improved data accuracy and completeness gained from effective data management strategies. For organizations, having greater 360-degree visibility into customer data and the customer journey helps improve go-to-market efforts, customer experiences, and more. For workers, fast access to accurate customer data enables more effective customer support and service, increasing customer satisfaction and reducing resolution times. 

AI-enabled outcomes rely on data management

AI is everywhere, and for good reason. Goldman Sachs says AI will raise global GDP by nearly $7 trillion. Gartner found that 79% of corporate strategists say AI is critical to success in today’s market. KPMG found that 73% of C-suite executives expect AI to increase profitability.

For AI to be successful, it must be trained on data. Bad, dirty, or otherwise untrustworthy or misused data leads to AI hallucinations and incorrect results. The fundamental data management challenges listed above must all be addressed to ensure AI delivers the value it promises.

But this isn’t as easy as simply giving AI access to clean data. That data must be organized, categorized, and well understood so AI takes full advantage of the data and stays within defined rules, governance, compliance, and other guardrails. The challenges increase as more AI technologies can consume and produce images, audio/video, documents, and other data types that can be tricky to understand and manage appropriately. 

Generative AI requires even more data to deliver value. Furthermore, generative AI must use highly sensitive customer and organizational data, and the generated results will likely include confidential or sensitive information.

Avoiding these innovations is not a viable solution in today’s world. In 2024, 82% of organizations invested in AI, and 65% of organizations regularly used generative AI. Those already high numbers are sure to increase, meaning every delay in improving AI outcomes is time for the competition to race ahead. 

A prerequisite of these AI efforts is, of course, good data management.

Good data management now relies on AI

Executives and decision-makers across the enterprise face many challenges as they transition into an AI-driven world, especially with the complexity of common on-premises, cloud, and hybrid data architectures. As AI innovations require more and more data to deliver effective and accurate outcomes, the right data must be easily identified, trusted, accessible, governed, and prepared for AI usage. Building a data management strategy on trust, collaboration, and openness forms the foundation of modern, AI-ready data management.

Ironically, AI is also becoming crucial to data management itself. Data management has become an AI-enabled capability, and more organizations are taking advantage of advanced automation, analytics, and intelligent data management. AI-powered data management tools provide real-time integrations across on-premises, cloud, and hybrid data architectures and simplify these complex architectures for a streamlined experience for technical and non-technical data consumers. 

Modern AI technologies are available to automate data management processes through rule-based orchestration of tasks, such as identifying potential data sources, highlighting security risks, and categorizing data automatically.

Even more advanced generative AI data management tools use large language models (LLMs) to understand requests and commands spoken or written in plain language. They can further generate plain language outputs to summarize complex concepts, architectures, and datasets. These enable non-technical data consumers to easily and quickly find, use, and extract value from organizational data. 

Here are a few use cases for data management and AI:

Using AI to discover data

Effective data discovery ensures data management efforts identify data so it’s easily qualified, categorized, and included in aggregated data repositories. Data discovery becomes increasingly tricky, especially with today’s easily onboarded cloud-based software applications. The result is more data silos that impede analysis accuracy and decision-making confidence. 

AI automates data discovery by identifying data flows, application subscriptions, and other data clues and scans network traffic, databases, and other components to guide data management efforts, index data sources, and even make preliminary data classification and categorization decisions.

Using AI to improve data quality

Incomplete and inaccurate data leads to incorrect assumptions, erroneous results, and bad decisions. 

AI can automatically seek out data, compare it with standards and existing databases, and correct errors in real-time – all before data is consumed. This reduces the need for slow, manual data cleansing efforts. AI also works around the clock and scans more data incredibly fast than humanly possible, ensuring bad data doesn’t slip through undetected.

Using AI to enhance data security

The cost of a data breach continues to rise as bad actors become increasingly savvy and creative in their efforts to steal data. In 2024, the average data breach cost approached $5 million as organizations spent more than $6 million combating and preventing data breaches. 

AI tools extend data management efforts to work 24x7 to automatically detect potential data security issues, block or mitigate access to potentially sensitive data and enforce data governance and data security policies across the organization. AI is also used to automatically flag and block access to sensitive data, such as corporate financial data, personally identifiable information, and customer data. AI monitors network traffic for such data and related suspicious activity, streamlines reporting, and automates the creation of audit trails to simplify compliance efforts. 

Enhancing your data management approach

Organizations must make better, faster, smarter decisions as the pace of business accelerates and global and economic uncertainty increases. That requires quick and reliable access to the right data to generate accurate insights.

When organizations cannot rely on, find, or understand data, it slows decision-making, saps confidence, and gives competitors a chance to edge ahead.

Proven tools like data catalogs enhance, streamline, and expand data management efforts by surfacing organizational data, offering data descriptions, policies, documentation, and expertise, improving trust in the data through endorsements, quality indicators, and collaboration tools. 

As more organizations depend on AI-driven technologies, modern data catalogs also improve data management by incorporating AI innovations such as:

• Natural language data search for AI-powered data discovery that integrates behavioral, semantic, and keyword ranking capabilities, offering a more intuitive discovery experience.

• Intelligent, AI-enabled data curation to accelerate the population of the data catalog and effectively curate data assets with metadata suggestions that save time, resources, and costs.

• Data management automation using AI that provides suggestions on data curation, refinement, and processes when approving and editing metadata at scale. 

For organizations working to improve data management strategies and applications, seeking a helpful solution like a modern data catalog is a prudent first step. Book a demo with us today to learn more.