Root cause analysis and the time exploration updates to the Azure Time Series Insights
Time Series Insights is a fully managed analytics, visualization, and storage service that makes it simple to explore and to analyze billions of IoT events simultaneously. Additionally, it will allow you to visualize and to explore time series data streaming into Azure in minutes, all that is without having to write a single line of code. For more information, regarding the product, pricing, and that is getting started in the below blog.
Faster root cause for analysis and investigations
We have heard a lot of feedback from our manufacturing, and the oil and gas customers that they are using Time Series Insights to help them to conduct root cause analysis and investigations. But it is been difficult for them to quickly pinpoint it statistically for significant patterns in their data. To make this process to be more efficient, we have added a feature that is proactive surfaces the most statistically significant patterns in a selected data region. This relieves the users from having to look at thousands of events to understand what patterns are the most warrant their time and energy. Further, we have made it be easy to then jump directly into these statistically significant patterns to continue by conducting an analysis.
This new feature is also helpful for the post-mortem investigations into a historical data. Most of our customers have their existing alerting mechanisms in the place (for example, Azure Stream Analytics jobs) and the use of Time Series Insights as a complementary investigative tool to understand the context of an alert. These customers are using the Time Series Insights to look back during a postmortem for additional clues to help mitigate and to prevent similar issues from occurring in the future.
Greater control for the time for data exploration
Additionally, we have heard from the customers across many verticals that they are using Time Series Insights to help them triage and to diagnose the issues involving sensor data from their key assets, but they have been asking for the finer control over their ability to navigate time in our visualizations. To give these customers to be more control, we have provided several new usabilities for the improvements to time navigation to make triage and to make it diagnosing easier.
First, we’ve added a time interval slider for the more precise control of movement between the large slices of time that show smooth trends down to the slices as small as the millisecond, allowing the customers to see granular, high-resolution cuts for their data. Further, we have set the slider’s default starting point to be the most optimal view of the data from their selection; balancing resolution, query speed, and the granularity.
Secondly, we heard from the customers that they would like an easier way to move between the time ranges when conducting the diagnostics on their sensor data. Previously, a user needed to leave to be their search and to reselect the period they wanted to explore their environment all over again to complete this task. To make their workflow more seamless, we have added a time to brush and make it easier to navigate from the one-time span to the another, putting intuitive to the UX front and to center for an easy movement between the time ranges.