Operational Intelligence

Companies are continually looking for ways to maximize productivity and profitability. Even when operations have been analyzed and optimized, subtle changes in environments make room for further, significant improvement. Taking a wide variety of granular measurements from sensors – on vehicles, equipment, consumer products, smart meters, etc. – lets businesses track patterns in operations to find new optimization opportunities. MapR solutions help companies to cost-effectively analyze massive volumes of higher resolution data to streamline production and distribution, reduce costly downtime, and pinpoint operational weaknesses.

Architecture for Operational Intelligence

Specific use cases include:

  • Supply Chain and Logistics: Track the movement of vehicles and products to identify the “costs” of various transportation and process options. By analyzing large volumes of historical, time-stamped location data, businesses can calculate optimal delivery routes and enable dynamic rerouting to minimize the impact of arbitrary obstacles like traffic and weather. Businesses can also leverage the optimal delivery system as a revenue-generating basis for premium/expedited delivery services to consumers.
  • Assembly Line Quality Assurance: Take measurements of work-in-progress products to find manufacturing defects as early as possible, while also identifying any potential process or design flaws. Since defects are typically the result of many factors, analyzing long histories of assembly line sensor data can find subtle anomalies that signify product flaws.
  • Preventive Maintenance: Monitor equipment or product utilization in a live environment to identify patterns that indicate imminent failure. For revenue-generating operations equipment, downtime results in lost revenue as well as costly repairs. Ongoing analysis of an entire system lets businesses predict when failure might occur, so preventive maintenance can avoid the failure. For consumer products, failures or need for replacement will depend highly on usage patterns, and tracking those patterns help manufacturers to alert customers when their products need specific maintenance.
  • Exploration and Production Optimization: Analyze data on existing operations and past exploration/production efforts to make decisions on ongoing and future operations. Tracking histories of successes and failures enable the development of models that provide guidance on where to pursue future projects, as well as when to halt existing projects due to reduced return on investment calculations.
  • Smart Meter Analysis: Get granular information from smart meters on energy utilization at a per-site basis to identify better pricing and utilization recommendations. Data from thousands or millions of remote sites give energy companies the complete picture of consumption, letting them better plan for energy purchases and allocation. Energy companies can also analyze usage patterns to identify inefficient appliances at customer homes to make new product recommendations.

Operational Intelligence with MapR

Deploying operational intelligence systems with MapR leverages the high performance, massively scalable, and reliable MapR Converged Data Platform to give businesses a powerful, enterprise-grade, distributed computing platform. Some important features of MapR include:

  • Easy data access : Loading data into MapR is as simple as copying data to a standard file system. Direct Access NFS™ lets analysts access data without special tools, so they can read and write files in MapR with their existing file system-based analytical applications. The integrated security in MapR ensures that users can access only the data they are authorized to access.
  • Business continuity: For environments that depend on ongoing analysis of sensor data, MapR ensures continuity with its proven track record of reliable production deployments. MapR provides integrated high availability (HA), data protection, and disaster recovery (DR) capabilities to protect against both hardware failure as well as site-wide failure.
  • Optimized small file support: Most distributions of Apache Hadoop are intended only for large files, while the MapR Converged Data Platform is ideal for handling files of all sizes, including many small files. This is critical in an operational intelligence system in which sensor data can be stored and analyzed as many independent small files coming from many different sources.
  • High performance and scalability: MapR was designed for high performance, with respect to both high throughput and low latency. This ensures MapR can keep up with the high volume and high velocity of incoming sensor data from thousands or potentially millions of sources. In addition, a fraction of servers are required for running the MapR versus other Hadoop distributions, leading to architectural simplicity and lower capital and operational expenses. Massive scalability ensures that growing data volumes can be accommodated simply by adding new nodes to the cluster.
Learn More

Climate Corporation Uses Big Data to Insulate Farmers


HP leverages the power of MapR in its Big Data infrastructure