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Springpath Gains Real-Time Intelligence and Improves Operational Efficiencies

As the HCI market has grown increasingly crowded, Springpath sought to differentiate itself by providing enhanced serviceability to its customers. But it was lacking in its ability to provide fast customer issue resolution for both hardware and software components and in its ability to get meaningful, real-time intelligence from data. Springpath needed to create better operational efficiencies for its support organization. To better service its customers, Springpath would need a solution to resolve customer issues faster while collecting real-time customer data from systems to extract better insights for both internal support teams and end customers alike. Building a solution would require quick ingest and parsing of data – which was at the time often sent in the form of complex log files – and the ability to present it as actionable information. Further, a solution would need to enable support engineers to provide quick issue resolution to minimize downtime. Ideally, Springpath wanted customers to get full visibility into their own systems – and thereby be able to perform timely maintenance tasks – with the goal of maximizing uptime and ensuring optimal system performance. In addition, a solution should integrate with their issue management system, Service Portal, for easy access for both the in-house support team and end-customers. “Offering world-class Customer Service in the IoT era requires a comprehensive and scalable Analytics platform that is well-integrated with an existing Support workflow,” says Brett Flinchum, VP of Global Customer Success at Springpath.

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  • Equipment & Machinery
  • Product Development
  • Springpath is the pioneer in the Hyper Converged Infrastructure (HCI) software industry. Its HCI software turns standard servers of choice into a single pool of compute and storage resources. The Springpath Data Platform eliminates the need for network storage and intuitively integrates into existing management tools to maximize operational efficiency. Using adaptive scaling capabilities, customers can grow compute, caching or capacity resources independently, depending on their changing business needs.

  • Springpath selected the Glassbeam Analytics platform as a foundation to build a robust solution that would enable speedy issue resolution and enable real-time intelligence that would transform the operations of the support group. Glassbeam’s unique data transformation and preparation engine can easily convert even the most complex machine data into structured and meaningful formats. This structured data can then be consumed inside Dashboard Reports to get visibility into every operational parameter of the HCI system stack. It can also be used to create and monitor threshold conditions through Glassbeam’s Rules and Alerts application to ensure that Support groups can preempt incidences of downtime by proactively addressing potential causes of malfunction. “Glassbeam is the first Complex Machine Logs Analytics platform that has true credentials in transforming unstructured data” notes Brett. “These is a treasure trove of intelligence deeply embedded inside log files and Glassbeam is central to our efforts in converting raw data to actionable information” The Glassbeam solution leverages its partnership with PTC to incorporate powerful scoring models at Springpath via ThingWorx Analytics. ThingWorx Analytics enables Springpath to embed powerful, automated analytics capabilities, including adaptive learning technology, real-time pattern and anomaly detection, simulative and prescriptive analytics, and automated predictive analytics into its solution – without requiring the expertise of data scientists or mathematicians. It is able to quickly and accurately predict important operational events that affect customers, such as disk failure or inflection points for performance degradation. The joint solution from Glassbeam and ThingWorx gives Springpath the ability to get better insight into product failures – the conditions under which they occur and potential causes for failure – as well as a better understanding of customer behavior patterns around these issues, all in real time. With this kind of real-time data, ThingWorx Analytics is able to better predict future events, providing Springpath with unprecedented insights to plan maintenance more effectively, minimize or avoid downtime, and better manage customer expectations and satisfaction levels. To get a free demo of ThingWorx 8:

  • Carbon Dioxide Emissions, Connectivity Status, Network Performance (bits/s)
  • Emerging (technology has been on the market for > 2 years)
  • Impact #1
    [Efficiency Improvement - Operation]
    Springpath is able to get a deeper, detailed understanding of issues surrounding product failure and able to use that insight to improve operational efficiencies, provide better customer service, enhance customer interaction, and improve overall customer outcomes and satisfaction.
    Impact #2
    [Efficiency Improvement - Issue Response]
    Analytics dashboards are used by the support team and end-customer to carefully monitor anomalies and quickly react to potentially adverse situations. The Single-Sign-On implementation with ensures the right context for each end user, thereby greatly increasing the dashboards’ utilization at Springpath.
    Impact #3
    [Process Optimization - Predictive Maintenance]
    The combination of predictive scoring models and rules and alerts has better-equipped engineers to pre-empt and avoid incidents of failure and/or system malfunction. Engineers get notifications via email or text messages in real time and are able to quickly take remedial steps to minimize or eliminate downtime events.
  • Factory Operations Visibility & Intelligence
    Visualizing factory operations data is a challenge for many manufacturers today. One of the IIoT initiatives some manufacturers are pursuing today is providing real-time visibility in factory operations and the health of machines. The goal is to improve manufacturing efficiency. The challenge is in combining and correlating diverse data sources that greatly vary in nature, origin, and life cycle. Factory Operations Visibility and Intelligence (FOVI) is designed to collect sensor data generated on the factory floor, production-equipment logs, production plans and statistics, operator information, and to integrate all this and other related information in the cloud. In this way, it can be used to bring visibility to production facilities, analyze and predict outcomes, and support better decisions for improvements.
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