2021: Customer Experience the Focus of Service Makeovers
Prioritizing the customer experience is increasing in popularity, but in doing so, vast cultural, technological, and operational changes are
Topic: Artificial intelligence technologies
Looks at issues related to artificial intelligence technologies, including cognitive computing, deep learning, and machine learning. Considers also supervised and unsupervised learning and natural language processing.
The Industrial Data Scientist Leads the Digital Revolution
To set Industrial Data Scientists up for success, organizations need to invest in a robust, scalable, and secure Industrial AI
AIPA: Report Redefines AIOps Market as Use Cases Evolve
As AIOps use cases expand and evolved, Research in Action GmbH suggests a more apt meta-market descriptor is Artificial Intelligence Predictive Analytics …
Covid Lessons Learned: Focus on Real-Time Supply Chains
Manufacturers recognize that their supply chains, which market volatility tested hard in 2020 and the first half of 2021, must be
Podcast: Understanding Continuous Intelligence
RTInsights editors Joe McKendrick, Jim Connolly, Lisa Damast, and Sal Salamone discuss continuous intelligence (CI). What is it? Why is it getting so much …
4 Key Trends in Monitoring and Observability
We’re in the midst of a monitoring revolution, which will probably continue to play out over the next decade as newer and better tools and methodologies …
Real-time Analytics News for Week Ending July 30
In this week's real-time analytics news: Sumo Logic adds new capabilities to its observability solution, multiple open-source projects move forward, and
How Continuous Intelligence Cuts Through Complexity
Continuous intelligence can help enterprise leaders deal with complexity in operations, security and business
The World Begins to Double Down on Digital Twins
Digital twins are already in use across a number of sectors, including construction, energy, healthcare, automotive, and
What RPA Is, What It’s Not, and Why It Matters
The grand promise of RPA so frequently falls short due to over planning, the inability to identify key automation opportunities, and a disproportionate focus …