If you’re a data scientist, you know the pressure you’re under to help the business understand the signals hidden in the vast and diverse stream of IoT data. Businesses need to decipher these signals, so they can deliver critical outcomes that enhance the customer or patient experience, improve equipment effectiveness and drive operational excellence. But, if you’re using batch scoring and various techniques to analyze data at rest, you’re hamstrung by the need to stream, store, and then score the data. It’s not only very time consuming for you, but it also delays your ability to make decisions in real time, which hampers the business’ ability to accelerate performance.
What steps can you take to rapidly convert IoT data into valuable insights for your business?
You must be able to capture more precise insights at the edge and make real-time intelligent decisions in the Cloud. And you want to be able to use the environment of your choice (Jupyter lab, Python) to quickly and precisely ingest, understand and act on massive volumes of diverse IoT data in real time. But it is difficult without streaming analytics and machine learning capabilities.
Here are some thoughts to consider along the journey toward helping your business extract the most value from its IoT data. Read further as I outline how to accomplish the three steps to deal with data. Also, make sure to see the bottom of the article for an easy way to get started with a free trial from SAS.
When you think about ingest, consider IoT is about getting access to high speed, has various forms, and emits from various sources. To do so, you need flexible ways to connect to these sources and support the speed and volume of IoT data. SAS Event Stream Processing (ESP) connectors and adaptors are pre-packaged with ESP and support various data formats, protocols, and are optimized for high speed data ingestion. Examples include connectors and adaptors for streaming data as well as static data. Streaming data sources include IoT devices like machines in a factory, connected vehicles, wearables, and customer behavior when interacting with products on your website or when they’re on your network in your stores. Often overlooked, static data sources represent a treasure trove of information you already have. And, this static data enriches events originating from streaming sources to provide a richer set of data to analyze.
This brings me to the second point: Understand. Understanding data means you need to apply a series of transformations and analysis on the data, so you can obtain some insight from the vast amounts of streaming data. This requires analytical techniques adapted to the streaming problem space, and different problems require different analytical procedures.
Often IoT data is high frequency and a vast number of dimensions to the data exist. Techniques that can help reduce the number of dimensions to those that are most relevant are critical. Additionally, you need techniques to understand and analyze both unstructured and structured data. So, having techniques to process video, audio, and text is necessary to gain the insights needed to make sound decisions.
Using many different approaches to understand the information and having a way to apply these different ways is important. ESP delivers a set of prepackaged algorithms applied on streaming data. It also provides integration with machine learning and artificial intelligence (AI) techniques to train models offline and then deploy for in-stream scoring. This is a powerful combination of capabilities combined with real-time analysis to discover events of interest.
And once you’ve discovered an interesting event, you need to act. It’s not enough to simply identify events and log them somewhere. The point of ingesting these events and applying real-time analysis is to react faster -- react faster so healthcare providers can enhance patient outcomes, retailers can deliver a differentiated customer experience, energy companies can predict machine failures before they occur, and manufacturers can detect objects and classify them immediately.
No matter the use case, detection is the just first step. The true value is in the ability to act. Reaction can be in the form of an alert generated to an operator to investigate a problem, or maybe to dispatch a technician to resolve a potential problem before it becomes a catastrophic failure. This means support is needed for decision making so you can apply business rules and create workflow - enabling case creation, routing, resolution, and dispatching. Action can be human actions, or they can be an automated feedback loop to control machines for optimized operations, or to reduce wear and prolong machine life.
To hear how the powerful combination of “ingest, understand and act” can help you make real-time decisions on your IoT data, check out this video below from the 2019 SAS Global Forum. Daniel Wilkins describes how SAS uses IoT to eliminate vibration that can cause premature aging or accelerated wear of the equipment in any kind of Manufacturing or Industrial environment.
Want to quickly and easily get your hands on the software that can help you be an IoT data super hero? Try ESP in the SAS Analytics Cloud today – start your free trial now.
Finally, view Samantha's story to learn about a data scientist who found a better way to make faster, more informed decisions using her IoT data.
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