Analytics

Data curation is the process of creating and managing datasets so that people can find, access, use and reuse data as necessary.

Real-time data integration involves capturing and processing data from multiple sources as soon as it's available, and then immediately integrating it into a target system.

Companies that embrace real-time streaming data integration are outpacing their peers, unlocking faster insights, optimizing operations and delivering superior customer experiences.

Metadata management refers to organizing, optimizing and using metadata to improve the accessibility and quality of an organization’s data.

A data product is a reusable asset that contains ready-to-use, processed data for various business use cases.

A primary key is a column or columns in a database table with values that uniquely identify each row or record.

Data processing is the conversion of raw data into usable information through structured steps such as data collection, preparation, analysis and storage.

A directed acyclic graph (DAG) is a type of graph in which nodes are linked by one-way connections that do not form any cycles.

Learn how data intelligence can help organizations get the most out of their data

Modern data stack (MDS) refers to integrated, cloud-based tools and technologies that enable the collection, ingestion, storage, cleaning, transformation, analysis and governance of data.

An AI data center is a facility that houses the specific IT infrastructure needed to train, deploy and deliver AI applications and services.

Data silos are isolated collections of data that make it hard to share data between different departments, systems and business units.

A data strategy is a detailed plan for leveraging data to improve decision-making, optimize business processes and achieve business goals.

A look into structured and unstructured data, their key differences, definitions, use cases and more.

A Resilient Distributed Dataset (RDD) is an immutable, fault-tolerant collection of elements distributed across multiple nodes for parallel processing.

In this tutorial, you will discover how to perform timeseries forecasting using the watsonx.ai Timeseries Forecasting API and SDK to predict energy demand. This notebook demonstrates the usage of a pre-trained time series foundation model for multivariate forecasting tasks and demonstrates a variety of features available using Time Series Foundation Models.

Delta Lake is an open-source data storage format that combines Apache Parquet data files with a robust metadata log.

For IT and business leaders, understanding the subtle differences between B2Bi and MFT is the key to achieving operational efficiency.

Why maintaining data cleanliness is essential to cybersecurity | IBM

Data products—powered by vertical data platforms—help overcome these challenges, unlocking new opportunities and enabling data-driven business models.

A data lake is a low-cost data storage environment designed to handle massive amounts of raw data in any format.

A data cloud is a data management system that unifies various data sources so they can be used more effectively by organizations.

Milvus is an open source vector database known for its scalable storage for vector embeddings and high-performance similarity searches of vector data.

Streaming data is the continuous flow of real-time data from various sources.

As data cleanliness becomes more critical for your organization's security and efficiency, these five steps help keep your data accurate and safe.

Data intelligence combines core data management principles with AI and other tools to understand how enterprise data is produced and used.

Data synchronization, or data sync, is the continuous process of keeping data records accurate and uniform across network systems and devices.

A dataset is a collection of data typically organized in tables, arrays or other formats for easy retrieval and analysis.

Synthetic data is artificially generated information that can supplement or even replace real-world data when training or testing artificial intelligence (AI) models. To help enterprises get the most out of artificial data, here are 8 best practices for synthetic data generation.

The rise of fast commerce, characterized by rapid order fulfillment and delivery, has further amplified the need for businesses to harness the power of streaming data.