As the IoT and data grows, human driven investigation of data becomes less and less effective. Errors and missed opportunities become more prevalent.
More-affordable computing power and improvements in the way we manage huge datasets have opened the way for artificial intelligence and machine-driven methods to investigate large and disparate data sets.
Organisations that make the devices that make up the Internet of Things and the organisations using those devices both have much to gain by implementing analysis and machine learning into their devices and operations.
Machine Learning can improve the way we interrogate our data and the way we use the insights derived from that data.
The data available from IoT devices is often highly specialised and geographically dispersed. This creates problems at the very first hurdle: organisations face challenges with data gathering, formatting normalising before the process of analysis can begin.
The volume of devices within an IoT system also typically presents challenges because of the sheer volume of data being produced. It is also produced rapidly and continually, usually much faster than many organisations are used to working with data.
These pressures mean that mining value from the data being produced is beyond human comprehension alone.
Most Business Intelligence tools fall short too; they have simply not been created to deal with these challenges. Most do not provide data points beyond those discovered by the user.
The Thingworx platform has been designed to address all of these core challenges and ThingWorx Machine Learning moves on analysis beyond the personal limitations of the user.
ThingWorx Machine Learning uses multiple analytical and machine learning techniques against a given data set to determine the best approach to creating a model. Once the best model is identified and put into production, ThingWorx Machine Learning begins to score records for prediction.
ThingWorx Machine Learning is highly flexible: it can be embedded into ThingWorx or run as a standalone solution. You can choose to run it on-premise or in a number of virtual environments. It scales as your needs grow.
This new approach to analysing data using Machine Learning technology can help to optimise products and processes now and deliver automated predictions, patterns and signals detection.
Implementing it doesn’t have to be daunting: InVMA can guide you through every step of the process.
One of the most powerful features of the ThingWorx Machine Learning is its ability to organically learn from changes in data – something that is highly prevalent in data being generated from IoT.
For example, data within the IoT is constantly changing, either on its own, or as new sensors, devices or data sources are added. This can potentially cause the deterioration of the quality of a data model.
The learning process within ThingWorx Machine Learning automatically adjusts to the new data, learns from it, and creates new insights as a result of continuously adapting to all changes. This creates continual, automatic improvements with minimal human interaction.