Dr. William L. Bain founded ScaleOut Software in 2003.
We are in the midst of a data revolution – deriving business intelligence (BI) from data has graduated from a nice-to-have feature to a business-critical function. As connected devices, sensors, and machines become ubiquitous, organizations increasingly require a solution that can keep up with the real-time data delivered by the Internet of Things (IoT).
Traditional techniques for business intelligence cannot handle the demands of IoT initiatives; their ever-growing streams of data require real-time tracking and analysis to be effective. Businesses are looking toward the next-generation of analytics technology – operational intelligence (OI) – to handle live, fast-changing data and provide immediate feedback.
Operational intelligence for IoT requires a computing platform that can store, update and continuously analyze data sets representing dynamic real-world entities or business assets. In-memory computing, which can perform these functions with scalability and extremely low latency, provides the computing power required for OI. For example, it can analyze a terabyte of continuously changing data in a few seconds and can ingest and analyze events from millions of sources within milliseconds. Tracking and analyzing events from a huge collection of dynamic assets and quickly generating feedback opens up new revenue streams and business opportunities that were previously impossible.
Here are some scenarios in which OI can introduce disruptive changes and add significant business value for IoT.
The financial industry has been one of the fastest to integrate high-performance computing technology into its day-to-day business functions, moving trading off NASDAQ’s floor and onto the Internet. Investment firms now rely on this technology to drive financial decision making with real-time analysis and forecasting. Using in-memory computing, investment firms can analyze several trading algorithms simultaneously, comparing historical stock prices, market fluctuations and equity positions to determine whether a trade should be initiated. A hedge fund can track the effect of market fluctuations on its portfolios, allowing long and short equity positions to be quickly evaluated based on proprietary strategies for rebalancing. Through in-memory computing, financial institutions can obtain the OI they need to make trading decisions faster and more successfully.
Outfitting “smart” machines on factory floors with real-time telemetry enables OI that can monitor performance and identify early indicators of problems, preventing costly failure scenarios. The losses associated with unexpected outages build up quickly; the cost of repair, replacement of expensive equipment, and reduced productivity reverberate throughout the business. Depending on what a factory produces, a minor mechanical failure that is not immediately recognized and addressed has the potential to endanger consumers and even risk non-compliance with industry regulations. Rather than relying solely on periodic inspections and component replacements, in-memory computing continuously analyzes live data and cross-references it with historical patterns to proactively avoid impending failures.
The proliferation of e-commerce has created intense competitive pressure on traditional brick- and-mortar stores, necessitating the integration of IoT technologies to add OI. E-commerce sites have long used in-memory computing to evaluate shopping behavior, purchase history, and spending patterns, providing real-time customized offers to prospective customers while they browse online. Brick-and-mortar retailers have started to apply the same techniques to create a personalized shopping experience within their stores. Shoppers can opt-in to a personalized shopping experience, which uses OI to combine demographics, brand preferences, shopping history, and current offers to generate immediate, personalized recommendations that assist sales associates. By employing RFID tags attached to merchandise, OI also allows retailers to precisely track inventory changes and lower costs.
Similarly, cable TV providers can stream telemetry from a vast network of set-top boxes to an OI platform, enrich the data with relevant historical information, and make timely, personalized recommendations to viewers as they watch TV. Data from customer history, demographic patterns, and other sources all can feed into a new way for brands to reach and engage viewers in the moment. By using in-memory computing, cable providers can perform this analysis in real-time and provide tailored offers, not just for entertainment recommendations that compete with streaming powerhouses like Netflix and Hulu, but also to immediately identify and correct technical issues.
Summing Up: IoT and In-Memory Computing
By using in-memory computing to continuously ingest, correlate, and analyze real-time data enriched with historical information, OI detects patterns and trends on a second-by-second basis. This powerful technology can provide immediate feedback that steers behavior, optimizes performance, avoids downtime, and captures important new business opportunities. Many industries have been quick to implement OI in order to maximize the value of their IoT deployments. Because of this technology’s impressive power and flexibility, countless new use cases will undoubtedly emerge.
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