Artificial intelligence is already solving problems in all aspects of our lives, from animation filmmaking and tackling space exploration, to fast food recommendation systems that improve ordering efficiency. These real-world AI systems examples are just the beginning of what is possible in an AI Everywhere future and they are already testing the limits of compute power.
Required fields have been a hot topic at FireHydrant. Choose too many (or the wrong ones), and you unnecessarily annoy your team during an incident or encourage sloppy data entry that someone has to come back and clean up manually. Don't use them at all and risk insufficient data to efficiently propel an incident toward resolution.
The debate of cloud vs data center looms large for businesses when determining how to best store data. In past years, the trend has leaned more toward the growth of remote solutions using the cloud. Yet, data centers are still relevant. Gartner states that spending on data center infrastructure is expected to grow 6% by the end of 2021. Cloud storage and data centers both possess unique features that allow you to achieve your organization’s data goals.
The amount of data volume and complexity within tech stacks is continuing to increase with no sign of slowing down. As a result, many organizations are facing significant challenges related to tool sprawl and the overwhelming amount of data that needs to be exchanged between all the different systems. The result is this new rapid pace of data which brings a need for faster flow and exchange of information.
Now, more than ever, the employee experience (CX) and customer experience (EX) are intertwined. Improving CX has a positive impact on EX, and vice versa. “Companies that excel at experience delivery don’t think of the two as separate,” notes Dave Wright, chief innovation officer at ServiceNow, in the Summer 2022 issue of Workflow Quarterly. “Instead, they align EX and CX to multiply the effects of each.
Organizations leverage many different cybersecurity and observability tools for different departments. It’s common to see the IT department using Splunk Enterprise, while the SOC uses Exabeam. Both of these tools use separate agents, each feeding different data to their destinations. Normally this isn’t a problem unless you’re talking about domain controllers. Domain controllers only allow a single agent, meaning you can’t feed two platforms with data.
One year ago, we launched Cribl.Cloud as a cloud-hosted option for our industry-leading data pipeline product, Cribl Stream. Customers had a choice of either deploying on-premises with a subscription-based tiered license model or opting for our cloud service with a similar tiered billing model. Fast-forward one year, and Cribl is now a multi-product company with several unique observability products (Stream, Edge, AppScope, and soon Search) to offer our customers.
Usually, maintenance is performed based on two factors first is when asset failure has occurred and second is scheduled maintenance. In both cases, asset maintenance is done when the asset is in good condition or when it is too late. This is where condition-based monitoring maintenance comes into play. This maintenance is proactive, and maintenance is given to assets when it is required. For this purpose, assets are inspected regularly, several asset tracking techniques are used such as IoT.
