With 2016 coming to a close, we’re seeing a proliferation of “big data prediction” reports coming out nearly every day. You’re probably wondering which trends will actually come to a fruition and have an impact on your industry. As a GPU-accelerated database company, we’ve seen first hand the trends driving fast “Data in Motion” (hence the name of our blog—Kinetica in Motion). With the number of connected objects expected to reach 50 billion in 2020, the Internet of Things (IoT) will continue to be a huge driver behind data in motion in the coming years. IoT sensor data is already becoming a treasure trove for data scientists and business analysts to explore, analyze and visualize–creating new use cases and enabling real-time business decisions.
The breakout success story for 2017 will be IoT uses cases with GPU Databases that “push real-time streaming use cases to the front burner,” according to Ovum’s 2017 Trends to Watch. As the use of smart sensor technology continues to spread, proactive data science and business analyst teams will need a solution that enables them to simultaneously ingest, explore, analyze, and visualize this streaming data. In fact, Computer World’s recent article Powering the IoT with In-stream Analytics proclaims that “Organizations will need to re-tune existing data management and analytics processes for the IoT age.”
The proliferation of sensor-driven technology has opened up impressive use cases across a broad cross section of industries. Organizations are taking advantage of Kinetica’s GPU-accelerated database in order to tackle these new IoT use cases, which include supply chain optimization, fraud and risk modeling for financial services firms, location-based marketing, cyber security (the ability to detect when hackers begin probing devices), predictive maintenance, and energy grid management, among others.
Discovering new value from high volumes of streaming IoT data is one of the biggest opportunities in energy today. Energy and public utility companies are utilizing “smart meters” to measure how energy resources such as electricity and natural gas are using in a homes and commercial buildings. These metering systems help utilities meet the demand for energy conservation, while also making billing easier for consumers to understand. Homeowners who use smart meters have real-time visibility into their energy consumption and can adjust accordingly, while utilities are better able to meet consumer demand and balance production.
Depending on demand and time of year, smart meters also enable utilities to change their pricing so that consumers get the best pricing available. In addition, smart meters can continuously monitor energy use, so utilities can react quickly to broken equipment or service interruptions. Finally, by understanding usage patterns across energy networks, utilities can better plan for future smart grid development.
GPU-accelerated databases can be used to analyze the massive data sets coming from smart grids and turn that data into actionable information and increased business value. Utilities can use Kinetica for geospatially visualizing smart grids, while ingesting real-time streams of smart meter, electrical and gas transmission/distribution data, and weather data to optimize energy generation and uptime based on fluctuating usage patterns and unpredictable natural disasters.
Moving into 2017, it will be exciting to see how data-driven organizations will leverage GPU-accelerated solutions to deliver truly real-time insights on data in motion.
Want to learn more? Here are some IoT use case cases where streaming ingest, analytics, and visualization are paramount:
- GPU-accelerated Use Cases For Manufacturing IoT
- Retail Use Cases: Real-time Inventory Management, Fleet Management, Customer 360, Logistics Optimization
- USPS Fleet Management & Route Optimization With Carrier BreadCrumb Data
- Mike Perez, Simplifying Streaming Analytics Using A GPU-Accelerated In-Memory Database