对于从事数字化转型的工业公司，分析是将大量数据变成业务价值以增强运营和改善客户体验的关键。面临着巨大的财务压力和迅速变化的全球市场的竞争，公司需要非常仔细地思考该数据的位置以及如何最好地利用数据。在某些情况下，需要集中处理数据和分析（例如在云中）来推动战略决策。在其他情况下，需要立即做出运营决策，这意味着集中解决方案无法提供分析。
分散分析（否则称为边缘分析或边缘计算）在操作网络的边缘或附近。这在一些面向消费者的行业中很普遍。但是，直到最近，由于成本，复杂性，安全性和技术障碍的混合，无法在工业优势处进行分析。
That is changing. Digitization is occurring in all industrial environments. In brownfield infrastructure, intelligence is being added via devices such as sensors and gateways. In new infrastructure, we’re seeing digitization through embedded software and preconfigured intelligent equipment.
As this change has taken place, ARC has observed the market focus swinging away from centralized Big Data and analytics toward edge data management and analytics. This makes sense to some degree, as the growth of edge devices for the Internet of Things (IoT) and their related data has skyrocketed, and will continue to do so.
However, edge analytics that rely too heavily on data generated only by equipment and devices overlook some of the most valuable data and insights available to industrial companies: operational data, a portion of which is also generated at or near the operational edge, plus process knowledge.
Cloud and edge redefine analytics
在工业环境中，传统上采用了层次结构来捕获，访问和传达整个组织的数据。运营人员，无论是在现场环境还是在工厂的地面上，都可以证明旨在捕获，共享和使用数据的过程和技术。但是，对数据使用的限制是相当大的，有时会受到业务孤岛和技术的严格限制。
This data structure precedes the Internet. As the Internet becomes a ubiquitous part of business and operating environments, this traditional data structure is being replaced.
Organizations are now beginning to see the value of a more comprehensive view of data and analysis. This improved view includes centralized processing, such as in the cloud (or even on premises on a server), and extends seamlessly to and from the operational edge.
As business leaders wrestle with the data explosion, they see cloud computing as the solution for associated volume, speed and complexity issues.
云可以带来巨大的计算能力来解决问题，因为它为结合复杂和大型数据集（包括结构化和非结构化）提供了可行的解决方案，并提供了高级分析技术。
示例包括将机器学习应用于声学数据以预测资产故障，整合文本分析以进行过程优化或使用图像分析进行产品保证。
为了应对云的增长，组织边缘的概念被定义为企业运营环境中最远的扩展，无论是物理基础架构，分布式操作点还是客户交往。
Edge Analytics将数据处理和计算靠近或在数据源（包括设备和设备）的数据来源扩展。在工业运营中，在边缘执行的分析通常支持战术用例，以效率，可靠性，计划外的停机时间，安全性和客户体验。
经常被忽视的IIOT元素
When thinking about the data for edge analytics, a common misperception is that they only consist of streaming data, time stamped based on the input source. They are often referred to as Industrial Internet of Things (IIoT) data. The thinking here is that a combination of connection, automation, edge analysis and workflow automation are key to getting value from the data.
尽管是真的，但这仅在IIT策略的背景下画出一部分图片。缺少的是了解操作过程及其相关数据的价值，其中一些可能是在边缘生成的。由于这些数据通常是由主题专家（SME）生成和捕获的，因此它们通常包含高价值信息。
操作数据，尤其是在边缘生成的数据，如果使用的话通常不足。除非存在正式过程，否则这些数据很少被系统地系统地系统地系统地作为整体操作数据池的一部分提供的来源。
In addition to operational data, SMEs understand (and often design) operational processes and best practices. These high-value workers have specific knowledge of how to operate equipment, execute maintenance and ensure safety procedures. For example, crude oil engineers have expertise around impact of crude types on equipment failure during the refining process. This intellectual property is invaluable, of course, and organizations are fearful it will leave the business as workers retire or move on.
Technologies are now available that can mathematically model and capture that expertise as part of the analytics. In doing so, this process knowledge can be augmented with operational and IIoT data. This blending of knowledge and data can be used to drive the optimized decision flows and equipment performance necessary for maximizing IIoT strategies.
>>Michael Guilfoyle,mguilfoyle@arcweb.com, is director of research at ARC Advisory Group. His expertise is in analysis, positioning and strategy development for companies facing transformational market drivers. At ARC, he applies his expertise to developments related to IIoT and advanced analytics, including machine learning.
