Denver - Berlin

Machine Intelligence
Through Data Transformation


The fact that a wide range of sensors, machines and equipment can transmit information about status, performance and usage, and can interact with people and other devices anywhere in real time points to the increasingly complex role of data in IoT systems. This only compounds when we consider the many billions or more of networked devices that many observers are forecasting will be deployed and the scale of data they will produce.

However, after several years of hype about big data and analytics, it’s truly amazing how misunderstood and underestimated data management tools are in the development of the Internet of Things (IoT) and Smart Systems market. Aggregation, transformation and management of data from sensors, machines and equipment is the holy grail of machine learning and the IoT; a fundamental core enabler.


In the IoT arena today, most networked machine applications are limited to remote monitoring and maintenance services, including alerts, alarms, remote diagnostics as well as tracking and location services. This is due to several factors including technical complexities, business model challenges and a lack of significant embedded intelligence in machines. Existing technology has proven cumbersome and costly to apply with many conflicting protocols and incomplete componentbased solutions. The challenges of gathering machine data and integrating diverse data types have been big adoption hurdles for customers wanting to analyze the data from machines and systems.

Return from simple applications, while extremely valuable, is limited to the manufacturer’s service delivery efficiency. Contrary to what current market offerings depict, however, the value of connectivity does not have to end with simple applications focused on a single class of device. Moving from “Simple” to “Compound” applications involves multiple collaborating machines and systems with significant interactions between and among devices, systems and people. No longer is the focus solely on the machine builder’s ability to deliver support for their product efficiently. Rather, value is brought to the customer through business process automation and machine optimization.

As technologies mature, particularly embedded computing and software tools, machines will continue to evolve to much higher levels of intelligence. As machines become more and more complex, so to will the challenge of extracting intelligence from the machine’s data. Because more advanced intelligent machines produce a variety of more complex “machine logs” in a relatively predictable manner, it is an ideal “staging” area for designing, building and deploying a new generation of advanced data transformation, management and analytics tools.


As Smart Systems move beyond the first base of connectivity, the service delivery story becomes critical to deriving new levels of value from gathered data. The need to manage and transform complex data for analysis becomes paramount, and the complexity of building this sort of solution in-house becomes unrealistic. And customers who opt to go with OEM service contracts are spending huge dollars and getting little value in return. This is why an increasing number of companies who are looking for new levels of data value from their advanced machines are turning to Glassbeam; they realize that other offerings cannot address complex machine log data in the manner that Glassbeam is able to.

Glassbeam’s key differentiators include:

» Ability to ingest, parse and analyze multi-structured, complex log data from advanced assets; the solution goes beyond the simple value provided by analyzing sensor or historical data.

» Much faster time to deployment, which also leads to significantly reduced cost (rapid deployment and reduced man hours). Overall, Glassbeam can deliver 10x the functionality in 1/10th the time at half the cost of other solutions.

» Much more granular development of Rules & Alerts when compared to competitors. Complex log data management uncovers many more variables for analysis than traditional solutions can.

» Integration of multiple data types to enable increasingly complex applications.

» Enablement of machine learning and predictive analytics on complex log data.

» Ease of use for multiple user personas with data different needs.

Glassbeam’s key capabilities that drive differentiation:

» SCALAR Platform and Semiotic Parsing Language (SPL): SCALAR is a purpose-built machine data management and analytics solution; the platform leverages Glassbeam’s SPL to combine data parsing, ETL and Rules/Actions into a single processing element (ability to address both unstructured and structured data in one single development step).

» Rules & Alerts Engine: Complex event processing technologies that model and capture threshold and anomalous conditions, then send alerts when pre-defined conditions are met


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