Operations | Monitoring | ITSM | DevOps | Cloud

How AI Can Misinterpret Data and Lead to Errors

While AI systems can analyze vast amounts of data quickly, they may also misinterpret that data and lead to significant errors. Understanding how AI misjudgments occur will improve algorithms and ensure they provide accurate results. From biases in data to linguistic ambiguities, various factors can contribute to an AI's misinterpretation of information. Look closely at how these systems work and reveal why you should address these issues right below.

How AI is Transforming the Way We Analyze Data

In 1956, when IBM's engineers unveiled the first hard disk drive, it stored only five megabytes-an amount dwarfed today by a single high-quality photo on your smartphone. But that wasn't the fascinating part; it was the vision. They anticipated a future where data would not only be stored but also analyzed on an unprecedented scale. Fast forward to the 21st century, and data is growing exponentially. Every second, trillions of bytes are created, tracked, and stored across the globe. But storing it isn't the challenge anymore; making sense of it is.

You Can Solve the Overprovisioning Problem

If you're like most companies running large-scale, data-intensive workloads in the cloud, you’ve realized that you have significant quantities of waste in your environment. Smart organizations implement a host of FinOps and other activities to address this waste and the cost it incurs: … and the list goes on. These are infrastructure-level optimizations.

You Can Solve the Overprovisioning Problem

If you're like most companies running large-scale, data- intensive workloads in the cloud, you’ve realized that you have significant quantities of waste in your environment. Smart organizations implement a host of FinOps and other activities to address this waste and the cost it incurs: … and the list goes on. These are infrastructure-level optimizations.

Global data mesh for public sector organizations

The sheer volume of data, often siloed and lacking interoperability, can make it challenging to get a big-picture, accurate view across complex public sector environments. With a global data mesh, you gain fast access to all potentially relevant information, regardless of source, format, or location.

Introducing the Time Series Buying Guide for IIoT

All machinery and equipment, including their controls and sensors, tell a story through the data they collect. This data, or Industrial Internet of Things (IIoT) data, provides a detailed narrative about the machines, offering actionable insights to improve operations. IIoT data empowers businesses to optimize and enhance industrial processes by detailing operational status, performance metrics, usage patterns, health diagnostics, and environmental conditions.

Announcing InfluxDB 3 Enterprise free for at-home use and an update on InfluxDB 3 Core's 72-hour limitation

Two weeks into the alpha release of InfluxDB 3 Core (our new open source offering) and InfluxDB 3 Enterprise (our newest commercial offering), we’ve received a good amount of feedback that the 72 hour limitation in Core is too limiting. This fell into three categories: For the users in category 1, we’re announcing a free tier of InfluxDB 3 Enterprise for at-home, non-commercial use.

Get Started with the TIG Stack and InfluxDB Core

Time series data is everywhere—from IoT sensors and server metrics to financial transactions and user behavior. To collect, store, and analyze this data efficiently, you need tools purpose-built for the job. That’s where the TIG Stack comes in: Telegraf for data collection, InfluxDB for storage and analytics, and Grafana for visualization. Together, these tools offer a powerful solution for real-time analytics, observability, and monitoring.

Investing for growth in the data economy

Money makes the world go around and this has never been more true when it comes to the data economy as innovative start-ups seek investment to achieve their ambitions and unlock their potential. Led by insight from Atomico and Realm, this piece - the fifth in our data economy series - will look at the data economy from an investment perspective - what traits do they look for in data-driven companies? How are start-ups approaching it? These are the questions this piece will answer.