Real-time analytics presents challenges to unlock benefits
Real-time analytics enables organizations with faster decision-making. However, bad data, or a culture that isn’t ready for real-time analytics, can undermine the benefits.
Real-time analytics enables organizations with faster decision-making. However, bad data, or a culture that isn’t ready for real-time analytics, can undermine the benefits.
We explore the benefits and dangers of cybersecurity professionals using AI tools like ChatGPT in the data center space – as well as how they’re already being leveraged by bad actors.
To become data-driven, companies need a democratization strategy that’s equal parts disciplined and diverse. Data collection, selecting a platform and employee training are just the beginning.
Companies embarking on their automation journeys need to figure out the right process to automate, deal with organizational change, and watch out for security and compliance issues.
Data quality challenges pose a threat to organizations’ decision-making. Inaccurate, inconsistent, missing and duplicate data poses threats to cultivating trustworthy data sets.
Generate accurate data analysis and predictions by mastering the six dimensions of data quality — accuracy, consistency, validity, completeness, uniqueness and integrity.
Taking stock of AI ROI is challenging but essential. IT leaders and industry observers lend insights on how to get a clear idea of whether your AI efforts are paying off.
Companies struggling to hire new talent are turning to training existing employees in internal “talent factories.”
Enterprises, governments and other organizations now have access to more powerful weapons in their defensive arsenal with AI – but so do malevolent hackers.
The chances are that most of the data you collect — from human communications to machine logs — is piling up with little plan for actualizing its potential. Good governance and AI can help.
Blending the structure of data warehouses and the flexibility of data lakes, data lakehouses are proving to be versatile tools for making the most of any data you want to collect.
Companies across a range of industries are deploying image- and video-based artificial intelligence to improve and optimize key business processes and products.
Machine learning algorithms will improve security solutions, helping human analysts triage threats and close vulnerabilities quicker. But they are also going to help threat actors launch bigger, more complex attacks.
To maximize the business value of artificial intelligence, AI teams require a diverse range of skills and roles, from data scientists to domain experts to strategic decision-makers.
Traditional BI vs. self-service BI isn’t a choice organizations need to make, but rather a partnership that requires elements from both to bring effective data use to users.