As sensor tech and IoT grows, firms need to enhance their capacity to collect data and derive business-critical information via analytics. Find out how in our Q&A with Savi SVP Andy Souders.
Sensor technology drives 30% of all data from Internet of Things (IoT) devices, said Andy Souders, SVP of Products & Strategy at Savi Technology, in a recent email Q&A. For him, IoT sensor technology is about "using the data to uncover new information and correlations that were previously unknown."
There's a "data tsunami" on its way, he explained, and "companies will therefore need to look for new ways to manage it by innovating their capabilities in collecting, storing, and analyzing business-critical information," adding that "it will not be long before sensor data surpasses enterprise data in volume."
Savi is a sensor-based analytics solutions provider founded in 1989, with a long history of working with the U.S. Department of Defense. In this Q&A, Souders discusses IoT sensor tech trends, Savi's mission and solutions (including the release of Savi Tracking 3.0), innovating for IoT, and big data pitfalls.
TechRepublic: What trends do you see in your competitive space over the next several years?
Andy Souders: As the IoT continues to grow, Savi believes so too will the use of sensor technology. Sensor technology drives a significant portion -- roughly 30% -- of IoT data. Sensor technology isn't just about gathering data though; it's about using the data to uncover new information and correlations that were previously unknown. To face the coming data tsunami, companies will therefore need to look for new ways to manage it by innovating their capabilities in collecting, storing, and analyzing business-critical information. To that end, we expect that sensor technology and the analytics will continue to become increasingly important given this anticipated growth and that it will not be long before sensor data surpasses enterprise data in volume. We expect this shift to place Savi in a prime position to manage and gain actionable intelligence from sensor data, given our history and expertise interpreting the data. Those that can figure out how to harness that information to make strategic decisions will have a competitive advantage in 2015 and beyond as first movers and early adopters in sensor data analytics.
TechRepublic: How would you define both your competitive space and Savi's corporate mission?
Andy Souders: We're a sensor analytics company. Our mission is to provide the most scalable and complete sensor analytics solutions for commercial and government organizations that have high-consequence assets which are critical for their operations.
We are often asked if we're competitive with companies in the traditional supply chain space or a fleet company, and the answer is that we aren't. We can take the data from a fleet management system and plug it into our solution to provide the analytical "secret sauce" to enable customers to execute intelligence route planning or accurately predict multi-stop ETA based on machine learning. It's not just about what's happening; it's about preventing problems in the first place and using data to describe the ideal course of action. Although fleet management systems can provide location information, we cover the white space left by these solutions from a granular, analytical, and predictive perspective.
TechRepublic: It is probably a safe guess, since you were founded in 1989, that the IoT is a game-changer for Savi. How is your company changing and innovating for IoT?
Andy Souders: Savi built its reputation providing sensor technology to government organizations to increase visibility and minimize risk across the global supply chain. The rise of the IoT over the last few years has provided an opportunity for Savi to build on its reputation for sensor technology, analyze sensor data, and deliver actionable intelligence to drive business impact. The whole purpose of the IoT is to turn innate objects into smart, communicative devices that talk to one another across a network, whether it's the ability to unlock a door from an iPhone, to manage the industrial supply chain, or to reroute a shipment if a hazard is detected. None of this would be possible without sensors to transmit and receive information. Sensors are the IoT's eyes and ears and therefore, as more "smart" devices are enabled, the volume of sensor data will only continue to grow. The next evolution of the IoT will depend on organizations understanding the value of their sensor data to optimize "smart" experiences into "intelligent" experiences through data analysis. The IoT is still in its infancy and its impact has been small so far, although with IDC predicting that the market will hit $7.1 trillion by 2020, it has a lot of potential to become massive in the near future.
TechRepublic: According to your website, Savi got its start supporting military supply chains in combat. What led you to your current product mix and corporate goals?
Andy Souders: Based on the work Savi has done for the DoD and NATO, we recognized that beyond the public sector there was a lot of great data being collected but not processed in supply chains that had a vast potential for applications beyond the public sector. Savi conceptualized the application of an analytical algorithm to process the data already being collected, leading to the genesis of Savi Insight, a SaaS-based analytics platform that captures, correlates, and analyzes sensor data into actionable intelligence. The award-winning Savi Insight uses new predictive and prescriptive analytics-based scenarios to uncover previously undetected operational and supply chain patterns. These capabilities empower organizations to measure and monitor Key Performance Indicators (KPIs), enhance operations with predictive analytics that spot opportunities and avoid problems and benefit from prescriptive analytics that recommend actions to improve future outcomes. By ingesting, correlating, and analyzing vast amounts of historical and real-time machine-generated data, Savi Insight quantifies and forecasts risk, performance, and efficiency, helping clients improve supply chain visibility, optimize operations, and prevent loss of high-value assets.
TechRepublic: Since launching Savi Tracking 3.0 in Nov. 2014, what kind of traction and feedback are you seeing?
Andy Souders: Since adding new features to Savi Tracking, we've continued to see impressive growth and usage of the solution, especially among our commercial customers. Since November, an additional one hundred commercial organizations began benefiting from the solution's new mobile capabilities, which allow users to access, view, and enter critical information about their in-transit and high-consequence assets in real-time on most smartphones and tablets. With Savi Tracking 3.0, we've enhanced the solution's performance to allow our growing base of customers to collect more sensor data and process it in less time, ultimately making it easier and faster to obtain critical supply chain information that can in turn minimize the impact of high-risk conditions and locations, better secure high-value assets, and improve supply chain management through operational intelligence.
TechRepublic: Please describe the features and benefits of Savi Insights as a predictive analytics solution.
Andy Souders: Savi Insight is a SaaS analytics solution that captures data from sensors and other sources, correlates multiple variables including time, temperature, and location, and applies logic that turns data into actionable intelligence. Savi Insight is "tag agnostic," and therefore able to ingest and correlate data from nearly any source, whether it's weather, an RFID tag, barcode, ERP system, or even Twitter.
By ingesting, correlating, and analyzing vast amounts of historical and real-time machine-generated data, Savi Insight quantifies and forecasts risk, performance, and efficiency, helping clients improve supply chain visibility, optimize operations, and prevent loss of high-value assets.
Its pre-packaged scenarios bring organizations the predictive and prescriptive analytics required to harness existing sensor data to drive operational efficiency and strategic actions for measured impact against KRIs and KPIs. These capabilities are the cornerstone of a growing market trend in supply chain and global industrial applications.
It allows our clients to quickly measure, assess, and predict performance based on their own data and to rapidly benefit from a growing list of pre-packaged scenarios that address their top business challenges. We are now working with nearly 600 commercial clients to answer questions such as how well assets are being utilized, how to optimize fleet performance, when goods will arrive, and the safest (or riskiest) routes.
TechRepublic: What are some of the pitfalls that you have seen among enterprises adopting big data analytics solutions?
Andy Souders: Often companies push the envelope and tackle big, hairy, audacious challenges or maybe do big data for big data's sake, just because technology has gotten more powerful. However, unless there's a business need or specific problem to tackle within an enterprise, big data initiatives won't gain traction. Enterprises that find the most value in utilizing big data analytics solutions are those trying to solve an inefficiency or make sure they can implement and maintain a streamlined business. As analytic capabilities struggle to keep up with the pace of big data and industry debates rage on related to common data communication standards for the IoT, the full potential of the "smart supply chain" is still in its early days though, so enterprises still have a lot to learn.
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