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Hortonworks 是领导者。阅读 Forrester Wave。
Few sectors have the scale and complexity as the energy and utilities industry and Hortonworks’ energy management solution leverages internet of things (IoT) big data for energy efficiency. Our 100% open-source connected data platforms enable energy data management and analytics for utilities with a single view of assets (we enhance grid visibility by orders of magnitude), operations and customers, predictive maintenance to prevent blackouts and leaks, and smart meter data analytics to detect outages and energy theft, and customer analytics to optimize customer service.
现代的公共事业公司需要捕获、传输、分析和存储智能仪表数据，以满足于安装先进计量基础设施相关的业务目标。但是当按月计量成为典范时，他们的数据架构是在更短时间内设计。智能仪表每一个小时收集多次信息，从而产生了海量、持续且丰富的数据流，公共事业公司技术不完善，无法在旧数据库平台上高效率处理和存储这些数据。
With 100% open-source Connected Data Platforms from Hortonworks, utilities enhance their grid visibility by orders of magnitude. Hortonworks’ energy big data management solution helps them monitor their data-in-motion from operated assets in real-time and compare that to deep historical analysis on past trends. That data discovery powers actionable intelligence for remote operations support, and also delivers real-time insights to: increase grid reliability, balance loads, reduce outages, and detect fraud.
历史上，电力和公共事业公司的运营技术 (OT) 和信息技术 (IT) 系统一直以来在组织中都是由孤立的人员开发、维护和使用。这一事实已经阻碍了公司间协作以及业务单位之间的数据可视性，导致运营和能源成本提高，中断时间延长、运营效率低下以及客户服务质量不佳。
From the beginning, Apache™ Hadoop® was architected to combine data from many different sources with highly variable formats. Apache NiFi identifies all of those sources and moves them to a central location for storage and analysis (both in real-time and batch). Now Hortonworks offers both of those technologies in an integrated set of solutions for utilities data management and analytics. Connected Data Platforms integrate data from siloed operational, IT, and external systems, enabling OT/IT convergence to create new dashboards for a single view of assets. This cross-company visibility reduces downtime and optimizes grid operations, potentially saving millions of dollars.
传统上，运营商通过物理检查来收集有关生成、传输、分发和计量设备的状况的数据。这意味着，检查数据稀疏并且难以访问，特别是考虑到出现问题的硬件的高价值以及设备故障所造成的潜在健康、安全和便利性影响。
Predictive maintenance helps utilities determine the condition of in-service equipment and then predict when maintenance should be performed. Rather than sending a maintenance truck based on the time of year, utilities now send them based on the actual need for repair. Hortonworks enables operators achieve energy efficiency from internet of things data by reducing failures and lower costs associated with routine or time-based preventive maintenance.
Utility companies built legacy data systems with a one-to-one relationship between the end application and storage platform. For example, the billing team manages a payment system with its database, the customer care team stores call logs in a CRM system, and the field service team stores data on service trucks and work orders.
Hortonworks’ data management and analytics solutions for utilities has helped some of the largest companies create a single view of their data and uncover value that might have been within reach, but scattered across multiple interactions, channels, groups and platforms. With that single view, they create customer personas, rank them by usage, optimize service calls, reduce churn and adjust target marketing for offers on value-added services like budget billing.
随着为降低风险而需要收集、处理、存储、集成分析的数据源的集合和数量日益增加，批发能源市场参与者与公共事业运营商一样面临类似的数据挑战。来源包括来自运营资产的传感器数据、市场和交换数据、ERP 数据、来自交易和风险管理平台的数据以及其他内部和外部来源的数据。
Hortonworks 支持的实时交易解决方案使能源交易商可以即时回应市场机遇，不会让其组织面临不正当的法律或财务风险。例如，一位客户利用我们的互联数据平台来提取、处理和分析来自商品交易数据服务的实时电力市场数据，以在其现有交易平台中丰富数据。这提高了预测能力，让客户可以识别市场不法行为，并帮助检测欺诈交易行为。
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