what-is-iot-analytics

The Detailed Guide To IOT Analytics

Byadmin

Day by day, the way and the purpose of using the data is constantly evolving. The tech world has come to the point saying “data is a treasure” rather than golds and diamonds. Internet of Things (IoT), the giant tech offers more meaningful and plethora of data. The way of utilizing that data to its full potential, with latest tools and technologies is referred as IoT Analytics. 

What is IoT Analytics? 

IoT Analytics is a process. simply put, it’s a verb. Nowadays, data analysis tools are indispensable for any business. IoT data analysis is one of the application of data analytics tool used to scrutinize bulk volume of data generated by IoT devices. The gathered data is organized and analyzed to give a fruitful insight. IoT Analytics is as same as Data Analytics, the only difference is that the data is fetch by IoT devices. 

  • Let’s you obtain resourceful insights including MATLAB and Octave. 
  • Generate data in time-series format for quick analysis and to filter, transform and store information. 
  • To create and train models without managing infrastructure, for analytics and machine learning (ML) inference. 
  • With Built-in SQL query, visualize trends and analyze the performance of device. 

Use Cases of IoT Analytics 

 Predictive machine maintenance: IoT analytics are capable of predicting machine breakdown. Businesses make use of such predictions to prevent their activities from halt. 

Quick updates to product software: Its alert businesses with malfunctioning with the consumer products.  

Tracking Inventory: IOT analytics helps to track and monitor inventories in real-time so that the retailer gets instant updates regarding stockouts 

Smart agriculture: It helps you analyze factors like time, location, altitude, temperature, humilities, with that data, one can write models with recommended outputs to take actions in the field. 

Features of IOT Analytics 

There would be no much difference in features in Azure, AWS and cloud. To understand better, let’s have a look at the features of AWS IOT Analytics. We can categorize key features into 5 primary sections like  

  • Collect 
  • Process 
  • Store 
  • Analyze 
  • Visualize 

 Data Collection 

Data ingestion from multiple sources: Take data directly from AWS IOT core to IOT Analytics. or, ingest data from Amazon Kinesis or Amazon S3 to IOT Analytics with the use of API. 

 Data Collection and Analyzation: It uses Analytics console to configure AWS console to receive messages from multiple devices under various formats and frequencies. AWS IoT Analytics establishes channels and verifies that the data complies with the criteria you specify. The service then directs the channels to the respective pipelines for proper message processing and transformation. 

Data Processing 

Filtering and Cleaning: You can define the AWS Lambda functions. which triggers on when IOT analytics misses data. To eliminate outliers from your data, you may also provide max/min filters and percentile criteria. 

Transforming: It transforms messages using mathematical calculations or conditional logic. You can perform any kind of common calculations  

Enriching: The data can then be forwarded to the AWS IoT Analytics data repository after being enhanced by external data sources like weather forecast information. 

Reprocessing: The raw data from the Channel connected to the Pipeline can be reprocessed by AWS IoT Analytics. Reprocessing your raw data allows you to acquire new and historical data, make modifications to your pipeline, or just process your data differently. You can establish a new pipeline or go back to a previous pipeline. To get deeper understanding or test a concept, this skill is frequently required. To reprocess, simply link the Pipeline to the relevant Channel. 

Data Storing 

Time-Series Data Storage: In an IoT optimized time-series data repository, AWS IoT Analytics stores the device data for analysis. You may control access rights, put data retention guidelines into place, and export your data to other access points. 

Raw Data Processing and storing: In addition to automatically storing the raw ingested data so you can process it later, AWS IoT Analytics also archives the processed data. 

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Data Analyzing 

Scheduled SQL Queries: You may execute ad hoc or planned queries with the built-in SQL query engine provided by AWS IoT Analytics and receive results promptly. For instance, you could want to quickly execute a query to determine the number of monthly active users for each device in your fleet. 

Time-Series Analysis: Time-series analysis is supported by AWS IoT Analytics, allowing you to examine device performance over time to comprehend how and where they are being utilized, continually track device data to anticipate maintenance needs, and track sensors to anticipate and respond to environmental conditions. 

Machine Learning and Sophisticated Analytics: For statistical analysis and machine learning, hosted Jupyter Notebooks are supported by AWS IoT Analytics. To get you started with IoT use cases related to device failure profiling, forecasting events like low usage that might signal the customer will abandon the product, or segmenting devices by customer usage levels (for example, heavy users, weekend users), or device health, the service includes a set of pre-built notebook templates. These templates contain machine learning models and visualizations created by AWS. 

Custom Container: Your custom-authored code containers, whether they were created in AWS IoT Analytics or by a third party like Matlab, Octave, etc., will be imported by AWS IoT Analytics, freeing you your time to concentrate on what makes you stand out from the competition. There’s no need to redo the analyses you’ve already done using third-party software. You may easily perform your analytics container on AWS IoT Analytics by importing it. 

Container Execution Automation: You can automate the execution of containers housing specially written analytical code or Jupyter Notebooks to carry out continuous analysis using AWS IoT Analytics. Your business’s needs can be met by scheduling the execution of your custom analysis on a recurrent timetable. 

Data Capture with customization: Users using AWS IoT Analytics can do analyses on additional incremental data that has been collected since the previous analysis. By accurately scanning only your fresh data, you may increase analysis productivity and cut expenditures. Customizable time periods will capture the fresh data for you since your last analysis, regardless of when you did your last analysis. 

Visualization: You can view your data sets in a QuickSight dashboard thanks to a connector offered by AWS IoT Analytics. The embedded Jupyter Notebooks in the AWS IoT Analytics interface allow you to view the outcomes of your ad hoc analysis as well. 

Benefits of IoT analytics 

  •  Transparency across the entire IoT network
  • Instant identification and trouble shooting 
  • Efficient asset utilization 
  • Cost optimization 
  • Identification of new market trends 
  • No latency in product development 
  • Enhanced customer experience 

Who We Are? 

We Continuuminnovations, offers managed cloud services from startups to enterprise. Our experienced cloud engineers recognize your needs and come up with the perfect solutions. We constantly update ourself with the latest trends and technologies to bring innovative solutions.