Customers turn to Viviota and to NI technology to better measure and analyze machine data from sensors. It turns out, sensor data does not always work well with traditional IT software, and there is a gap of software tools for this purpose.
The many Global 1,000 companies and leading research centers I’ve worked with grapple with managing the volume of Analog Data acquired from sensors and other sources. And the amount of that data is only going to increase. The growth of sensor data is outpacing the ability to manage and analyze it.
A common problem is companies have relied on a patchwork of third-party tools and custom in-house solutions to find the right data for analysis. Over time, a company can have thousands of applications, each one with its associated data saved in all types of formats. This creates “silos of data” in proprietary formats with no way to identify what it all means. In other words: it’s digital clutter.
The real value of data is to extract meaningful knowledge to make a product or service better. At Viviota, we refer to the cycle time it takes to design, build, and test any product as “the time to innovation.” Every company’s goal is to decrease this window of time to continually increase the value of its offerings.
A company should treat its data as a strategic asset, and someone should harbor the data and create an environment where data has a seat at the R&D table.
Viviota Time-to-Insight software has been built from the sensor up to attack these problems. In other words, Viviota manages the enormous amount of data coming from sensors and all data sources feeding backend big data analytics. We are unique in this way. We build on NI’s new Data Management Software Suite, which includes an enterprise solution for finding, analyzing, and reporting measurement data. Time-consuming tasks in test that once took days to execute, can now happen in seconds. Better yet, engineers can focus on engineering, instead of managing data.
Data management shouldn't take time away from innovation
The location may be different—Japan, Germany, United Kingdom, United States—but the story is the same: engineers at these companies spend valuable hours searching for the data they need. If they do find what they’re looking for, it’s often in the wrong format and takes a huge effort to make sense of it.
These companies are primarily in the business of producing trains, planes, automobiles, and other things —not managing data. They share the same frustration, longing to spend their time innovating with timely information rather than acquiring and managing data. Engineers need better ways to interpret the data so they can make decisions faster—sometimes extremely fast.
The common thread to this problem is multiple third-party tools and custom in-house solutions being used to find the right data for analysis. Over time, a company can have thousands of applications, each one with its associated data saved in all types of formats. This creates “silos of data” in proprietary formats with no way to identify what it all means. In other words: it’s digital clutter.
The real value of data is to extract meaningful knowledge to make a product or service better. At Viviota, we refer to the cycle time it takes to design, build, and test any product as “the time to innovation.” Every company’s goal is to decrease this window of time to continually increase the value of its offerings.
A company should treat its data as a strategic asset, and someone should harbor the data and create an environment where data has a seat at the R&D table.
Viviota Time-to-Insight software has been built from the sensor up to attack these problems. In other words, Viviota manages the enormous amount of data coming from sensors and all data sources feeding backend big data analytics. We are unique in this way. We extend NI SystemLink Data Management Module, which includes an enterprise solution for finding, analyzing, and reporting measurement data. Time-consuming tasks in test that once took days to execute, can now happen in seconds. Better yet, engineers can focus on engineering, instead of managing data.