Accelerate business outcomes using the cloud
Users need to be able to capture more precise insights at the edge and make real-time intelligent decisions in the cloud to make the Internet of Things (IoT) valuable.
Data scientists know the pressure to help businesses 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 to enhance the customer experience, improve equipment effectiveness, and drive operational excellence.
But if users are using batch scoring and various techniques to analyze data at rest, they are hamstrung by the need to stream, store, and then score the data. Not only is it very time consuming, but it also delays the ability to make decisions in real time which hampers the business’ ability to accelerate performance.
What steps can users take to rapidly convert IoT data into valuable insights for their business?
Users need to be able to capture more precise insights at the edge and make real-time intelligent decisions in the cloud. They also want to be able to use the system of their choice to quickly and precisely ingest, understand, and act on the massive and diverse volumes of IoT data in real time. However, it can’t be done without streaming analytics and machine learning capabilities.
Here are some thoughts to consider along the journey toward helping their business extract the most value from its IoT data:
When users think about “ingest,” consider the IoT is about getting access to data that is high-speed, has various forms, and is emitted from various sources. To do so, users need flexible ways to connect to these sources that support the speed and volume of IoT data. Users need tools that support various data formats and protocols, and are optimized for high speed data ingestion. Solutions need to include connectors and adaptors for streaming data as well as static data.
Streaming data sources typically include IoT devices like machines in a factory, connected vehicles, wearables, and customer browsing, interacting, and purchasing behavior. Static data sources are often overlooked but represent a treasure trove of information they already have but most likely have not fully tapped into. This static data can be used to enhance the events that originate from streaming sources to provide a richer set of data to analyze.
Activate your treasure trove of data through understanding
Understanding data means they need to apply a series of transformations and analyses on their data so users can obtain some insight from the vast amounts of available data. This requires analytical techniques that are adapted to the streaming problem space knowing that different problems require different analytical techniques. Often IoT data is high frequency and there’s usually a vast number of dimensions to that data. It’s critical to develop techniques that can help reduce the number of dimensions to those that are most relevant and can help understand and analyze both unstructured and structured data.
For example, processing video, audio, and text are all necessary to gain the insights needed to make sound decisions and need techniques which can support those processes and data. Many different techniques can be used to understand the information and having a way to apply these different techniques is important. Tools need to have a wide range of capabilities including algorithms that can be applied on the streaming data, integration with machine learning, and AI techniques that allow models to be trained offline and then deployed for in-stream scoring. These powerful capabilities can be combined for real-time analyses to discover events of interest.
The purpose is to act
Users need to act once an interesting event has been discovered. It’s not good enough to simply identify events and log them somewhere. The point of ingesting these events and applying real-time analyses 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 just the 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 resolution so users can apply business rules and create workflow — enabling cases to be created, routed, resolved, and dispatched. Action can be human actions, or they can be automated feedback loops to control machines for optimized operations, or to reduce wear and prolong machine life.
The objective is to quickly and precisely ingest, understand and act on massive and diverse volumes of IoT data in real time. This has a big impact on the business and allows people to take the actions necessary to make big improvements.