The 2026 Guide to Data Management 

Data management strategy helps organizations ensure that data is always available, integrated, governed, secure and accurate. It forms a foundation for digital transformation, AI initiatives and better business outcomes.

In essence, data is any collection of facts, numbers, words, observations or other useful information. But data comes in many different forms, each defined by its unique characteristics, sources and formats.

There’s a database for virtually every data management or data processing application. Explore relational databases, vector databases, distributed databases, query engines—they’re all here.

Data platforms—including data warehouses, data lakes and data lakehouses—enable collection, transformation, analysis and governance of data for specific tasks.

Data architectures describe how data is managed—from collection through consumption—and set the blueprint for how it flows through the organization. They’re also foundational to data processing operations and artificial intelligence (AI) applications.

Data engineers design systems for the aggregation, storage and analysis of data at scale, and empower organizations to get insights in real time from large datasets.

Explore ways to move digital information between systems, devices and locations, including file transfer, data streaming and data migration.

Data integration brings together data from disparate sources, transforming it into a consistent structure and making it accessible for processing, analysis and decision making.

Data processing is the conversion of raw data into usable information through structured steps such as data collection, preparation, analysis and storage. Today, machine learning (ML), AI and parallel processing—or parallel computing—enable large-scale data processing.

Big data encompasses massive, complex datasets in various formats, including structured, semi-structured and unstructured data, that demand advanced analytical approache for extracting meaningful insights.

Enterprise data management (EDM) is data management at scale—organizing, governing and optimizing enterprize data throughout its lifecycle, from creation and collection to storage, integration, usage and eventual archiving or disposal.

Data quality measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness and fitness for purpose. It’s critical to all data governance initiatives within an organization.

Data governance helps ensure data availability, security, and integrity by defining and implementing policies, standards and procedures for data collection, ownership, storage, processing and use.