IoT Data Analytics


Condition Based Monitoring


Predictive Maintenance


Reduce Maintenance Cost


IoT Enabled Platform ATHENA


Slowly but surely, IoTs are becoming part and parcel of the Industries as well as personal life. As per the various Industry leading surveys, it is estimated that there will be around 50 Billion IoT devices in operations. Not only volume is mind blowing, what is more interesting is what we do of this huge data. We will surely need Machine Learning, Artificial Intelligence and other statistical methods to make sense of this data.

The Use Cases will vary for each type of applications for IoT. In case of Industrial Automations, the most important use case will be Predictive Maintenance rather than Break-Down or Preventive Maintenance to ensure maintenance costs are reduced significantly. For Urban Infrastructure, the use case can be of optimal usage of power across IoT networks or for optimal power utilization for smart homes

ATHENA, is IoT Enabled Open Source Software based Integrated Analytics Platform, drastically reduces your IoT Implementation to Insights Journey. It’s configurable components allow you to adapt the platform to your specific machine sensor data. Once configured, the various in-built algorithms learn for each machine for better predictions. It can also smoothly integrate with your Analytics IT landscape. Know more about ATHENA.

IoT Analytics

Descriptive Analytics

The set of Algorithms used are primarily used to understanding the Machine Behaviour and look for patterns which are indicative of any Anomalous behaviour. The simple example can be Clustering

Machine Learning

These algorithms are typically employed for complex business problems. Examples are classification using Artificial Neural Network

Predictive Analytics

Algorithms in these categories use Engineering Basics as well probability calculations to predict the outcomes. These algorithms in Industrial Automations will be a two stage process. Applying Engineering Principles in Stage 1 and Machine Learning Algorithms in Stage 2. Examples can be predictions of a failure of a machine or possible break down of a service

Specialized Analytics

Integrating the Machine Data with the Analytics output from specialized areas such as GIS, Image Processing etc

Prescriptive Analytics

Algorithms in this class provide various recommendations for certain types of business problems. The examples in case of Industrial Condition Based Monitoring can be recommendations about Part Replacement



Significant Reduction in Deployment time using IoT Enabled Integrated Analytics Platform ATHENA

Ready Made Models

Domain Specific Ready made model skeletons available for faster Deployment

Data Spooling Connectors

Easy to configure readily available Data Connectors for pulling various machines

Domain Exposure




BIG Data Implementation

Experience of algorithm implementation in Big Data Environments

Strong Analytics

Experience working with variety of Domains with wide spectrum of Statistical Models

Easy Deployment

Flexible deployment options. Implementations can be Cloud based as well as on Premise