How many customers are in our stores right now, and what are they most likely to buy?
Which vehicles in our fleet are running with unacceptably low fuel economy and why?
What machinery on the factory floor could fail in the next five business days, and what parts are needed to keep them running?
Which rooms on our campus are empty but wasting energy due to unnecessary lights and air conditioning?
Questions like these and countless others could be answered by sensors and devices that are connected to the Internet of Things (IoT) and by a powerful processing engine that can apply advanced models to large, in-motion data streams—an approach to business intelligence (BI) called streaming analytics.
When you can answer these types of questions, you can solve business problems in new ways and seize new revenue opportunities. You can reduce costs and increase efficiency while improving the safety of employees and the security of assets and intellectual property.
What Is Streaming Analytics and How Does it Work?
These benefits are possible with SAS Event Stream Processing—streaming analytics technology that analyzes event data in real time while the data is in motion. Unlike traditional analytics, SAS Event Stream Processing technology lets business analysts and leaders take action based on current evidence.
It captures data that streams from systems or connected devices anywhere in the world and processes it while the data is hottest and most relevant. This processing includes data manipulation, normalization, cleansing and pattern-of-interest detection.
You can deploy the SAS real-time processing engine with advanced analytics models in new or existing applications. The engine then executes the models against one or more data streams to provide continuous business insight.
Let's take a look at how an SAS streaming analytics solution could impact the daily work of a fictional data analyst, one we’ll call John.
How Streaming Analytics Solves Business Problems
John is an IT analyst at a large enterprise with more than 10,000 employees and four large data centers. Every day, John and his team of data scientists query large data volumes and analyze output in order to extract insights that are used to make business decisions.
His organization has analytics and BI tools, but their limitations are becoming obstacles. For example, when John detects a pattern that looks significant, he can't easily drill down to investigate it. He must work with a data scientist to choose data sets for further analysis, clean the data of noise, and code a query that will return the answers he needs.
Furthermore, executives and line-of-business managers have begun to ask questions that the BI solution isn't able to answer. What are customers saying about the new product on Facebook and Twitter? How do supply chain delays impact production and shipping? John and his colleagues can give historical answers, but those don't help solve problems that are occurring now.
Now suppose that his organization has SAS Event Stream Processing technology. The company’s CTO wants help assessing the impact of a new product lifecycle management solution. Specifically, she wants to know where and why delays occur from end to end—in the supply chain, in production, and in shipping.
The SAS solution already analyzes event streams from manufacturing and shipping. John knows the solution can scale easily to hundreds of millions of events per second, including data manipulation and normalization. So he simply connects the stream from the company's supply chain management system to the SAS Event Stream Processing server. Using the SAS visual interface, John or his colleagues can embed the SAS processing engine into the supply chain management software and begin almost immediately to execute advanced models against the new stream.
In a matter of hours, SAS streaming analytics has detected interesting patterns that John's team can easily investigate further. They have uncovered delays and their causes and can help the CTO make a data-driven decision about the new software she is evaluating.
Uncover Hidden BI in Your Streaming Data
What could streaming analytics do for your business? To answer, first think about the event streams your business activity already generates, and then imagine what questions those streams could help you answer. Then click here to learn more about SAS Event Stream Processing and to see it in action.
