Articles

Big data initiatives are being affected by various trends. Here are seven notable ones and what they mean for organizations aiming to take advantage of their data assets.

From personalized marketing and prototyping to business workflows and planning, generative AI can augment creativity as a new collaboration tool for humans in enterprise settings.

Automating data governance makes tedious, time-consuming tasks more efficient. The human element remains critical to keeping automated systems in compliance with regulations.

AI inference and training are both critical phases of model development. Learn how to balance their demands to optimize performance, manage costs and scale models effectively.

Visualizations that make sense and are important to your audience's goals help you tell better stories with data. Color, layout and the type of data you display play a role.

Learn about the uses of column-oriented databases and the large data model, data warehouses and high-performance querying benefits the NoSQL database brings to organizations.

Explore strategies for managing sensitive data in enterprise AI deployments, from establishing clear data governance to securing tools and building a responsible AI culture.

Organizations looking to implement metadata management can choose from existing standards that support archiving, sciences, finance and other kinds of digital resources.

AI tools are becoming a key part of BI systems, both to add new analytics capabilities and simplify tasks. Here's what you need to know about using AI in the BI process.

Data literacy skills are the foundation of data-driven decision-making. Identify your current skill level and learn what you must improve to better use data in your work.

Analytics applications need clean data to produce actionable insights. Six data preparation best practices can turn your messy data into high-quality fuel for analytics operations.

Data governance isn't plug and play: Organizations must select which data governance framework best fits their business goals and needs.

Data literacy training is a flexible learning process. Organizations can tailor training for their teams, but individuals can find educational resources to better their skills.

Don't wait until you have a metadata management problem to address the issue. Put a metadata management framework in place to prepare for potential issues.

BI and big data analytics support different types of analytics applications and using them in complementary ways enables a comprehensive data analysis strategy.

Generative AI can't replace data analysts. It can help analysts be more effective, but GenAI lacks human insights and knowledge to do the job.

ESG metrics measure performance on environmental, social and governance issues. Here's how they can benefit companies, plus tips on using them effectively.

Analytics governance might not seem exciting, but it can improve innovation and mitigate risks. It's also critical to responsible data management and analytics practices.

For enterprises looking to scale their AI projects, centralized AI hubs and governance can simplify integration, streamline operations and ensure consistency.

Organizations face various business risks related to environmental, social and governance issues. These are notable ones, with advice on how to manage them.

Prioritizing data curation, preparation and engineering -- rather than tweaking model architecture -- could significantly improve AI systems' reliability and trustworthiness.