IoT Analytics

With Volery IoT Analytics solutions we enable your organization to execute and automate data analytics to leverage your IoT Data, which is a very challenging task if one looks at the mass of data to be processed. Generating insights is essential to gain hard facts and to make the right decisions at the right time.

In general, IoT-Data have a complex nature, are often not reliable, only make sense with additional data from external sources, and usually occur in huge volumes. This makes it impossible to derive satisfying results using conventional data analytics methods and tools. Volery IoT Analytics solutions provide flexible tools and methods to (a) enrich your IoT-Data with external data sources (b) transform your IoT-Data into an analyzable format (c) realize your analytical tasks e.g., making predictions, use machine learning models, and (d) deploy and report the results to an easy-to-use application.

Your company is already involved in IoT Analytics, integrates Analytics-Services, or has its customized Analytics-Infrastructure? No problem, our IoT Analytics solutions provide flexible and easy-to-use methods to access and transform the underlying IoT-Data and results such that your analytical tasks can be integrated into almost any Analytics-Platform and Infrastructure.

We utilize a large set of tools and methods such as Visual Analytics, in an effective way to convey your decision makers and domain experts the bits of information they need to act.

Technical Details

Volery IoT Analytics solutions enables you to use the most suitable algorithms for your analytical tasks e.g., the forecasting of future sales trends or the clustering of Wireless Sensor Networks to optimize its efficiency, as well as the most efficient toolset to report results in an appropriate manner.

Volery allows flexible data access via REST-APIs and APIs in many different programming languages, for instance Python, R, JavaScript, Java, C, C++. Thus, there is no limitation when it comes to choose the most suitable machine learning library for your analytical tasks.

The choice of the right machine learning library depends on multiple factors such as the underlying data to train and test the model, the use-case and characteristics of the analytical target, the application and analytical needs, as well as the level of comfort and previous experiences. Common ML libraries that harmonize well with Volery and our IoT Analytics services include, Tensorflow[.js], Keras, PyTorch, R, and Java-ML just to mention a few of them.

Reporting results to a decision maker or a data analyst in an appropriate manner is one of the most important steps in an Analytics Pipeline. Our IoT Analytics services are able to utilize Analytics-Platforms (e.g., Tableau, PowerBI, Qlik, and many more) to report results and monitor important processes.