In any platform of sufficient complexity, multiple anomalies are likely to occur. For many organizations, NOC operators triage multiple anomalies based on their severity. There are internal, non-customer-facing issues that might affect only a small part of your workforce and one-time issues that affect only a small number of customers. Both of the issues get ticketed and sent to low-level support.
Let’s start with simple definitions. Time series data is largely what it sounds like – a stream of numerical data representing events that happen in sequence. One can analyze this data for any number of use cases, but here we will be focusing on two: forecasting and anomaly detection. First, you can use time series data to extrapolate the future.
Many companies today try to feed business metrics into APM or IT monitoring systems. Splunk, Datadog and others track your business in real time, based on log or application data – something that would seem to make sense. In practice, however, it fails to produce accurate and effective monitoring or reduce time to detection of revenue-impactful issues. Why? Because monitoring machines and monitoring business KPIs are completely different tasks.
It’s no secret that machine learning (ML) has experienced tremendous growth and adoption over the last few years. And why not? This exciting technology has enabled us to utilize the power of machines for a wide variety of applications and industries. From image processing to predicting to medical diagnosis, ML has begun to reshape the way we live.
Every successful company plans for sustainability and growth. Forecasting the growth path helps companies set their short- and long-term business objectives and make important decisions to help them reach their goals. Short-term forecasts are important in quarterly and annual budget planning and for ensuring that daily business operations help achieve long-term goals.
Customers today are faced with a wide variety of industry terminology: APM, IOTA, BPM, OI, BAM and AIOps, just to name a few. Using different terminology like this might help large vendors expand their market size with different positioning offerings, but it certainly doesn’t help their customers understand what they’re getting. Companies spend tens of millions of dollars to solve their problems with the wrong solutions and struggle to get value from it.
Demand forecasting is the process of predicting future demand for a company’s products or services. Understanding how many customers will want to purchase or use something is critical for acquiring inventory, planning capacity, scheduling resources, producing products and managing the supply chain. How many people should you schedule for a work shift? How many widgets should you produce this quarter? When should you build additional capacity into your systems?
It may be back-to-school month, but the glitches never went on summer vacation! Check out the glitches that occurred since our last report…
On July 22, 2019, Slack was in the middle of deploying an update to their desktop app. The update was supposed to decrease memory consumption and increase load time, but instead the company suffered a significant, widespread outage on a global scale. After approximately 40 minutes of downtime, the service was back up. But in the meantime, the company whose motto is ‘where work happens’ essentially stopped working.
