The tools we use to get things done are generating more data than ever. But this data is often inconsistent and highly invariable. This makes it hard or merely impossible for an organization to gain value from it. This is where IoT Analytics comes in. IoT Analytics is a service that processes, contributes, stores, analyzes, and reflects IoT data for manufacturing and other enterprises. A product of AWS (Amazon Web Service) IoT Analytics helps collect, clean, and analyze IoT data, and this allows businesses and device manufacturers to quickly and easily gain operational insights.
To better understand how IoT Analytics helps, its good to understand who it helps the data engineer and data analyst. They are the core users of IoT analytics. The data engineer collects the data from devices, making sure its properly prepared and stored for analyses. The data analyst productizes and communicates insights by exploring individualizing data. Keeping these two users in mind IoT analytics has been designed with three different areas:
a) Collect: This area is all about collecting data for analyses. Channels are how analytics listens to devices using a protocol such as MQTT Topic Filters. Channels pull messages into IoT Analytics from all manner from devices.
b) Process: Once messages are flowing pipelines are used to process messages, turning them into full data. Pipelines use activities like transforms, filters and even enrichment services and many more. When the pipeline is done with its work data stores, provide a resting place for data which is now ready for analyses which are perfect for the third step of IoT, i.e., analytics.
c) Analyze: This is where IoT Analytics helps the user to get insight into their data. To explore and refine data sets are created simply by hearing data into the stores. Data sets are refreshed automatically to provide up-to-date data insights. To gain even deeper insights, Jupiter Notebooks empower people to get creative with their data, and the notebooks templates make it easy to use common IoT analytics functions and Machine Learning.
Businesses rely on three types of IoT analytics from their selected IoT platforms. By testing various IoT platforms, an organization can identify the best analytics capabilities.
Descriptive analytics: Descriptive analytics is the standard and essential basic form of analytic insight that allows users to define and aggregate incoming IoT data. Descriptive analytics can be used to make sense of the collected data quickly.
Predictive analytics: Predictive analytics seek to model future data and behaviours by analyzing actual old data. Regression analysis, such as linear regression, is an example of predictive analytics.
Prescriptive analytics: Prescriptive analytics are analyses to help organizations optimize for the future path to be taken. Image processing, machine learning (ML), and natural language processing (NLP) are some of the methods used to build prescriptive analytics.
At AceTek Solutions which is an IoT Analytics service provider, IoT Analytics ties up nicely with AWS to visualize data, sharing insights. From start to finish, we leverage IoT Analytics to make it faster and even easier to collect and analyze even the most sophisticated IoT data. Our IoT Analytics can accommodate different skills and objects and also affords stronger colouration and communication between roles. IoT Analytics by AWS through AceTek Solutions delivers custom IoT analytics at better and faster scalability.
As one of the best IoT services provider, we do our best to have hands-on experience with all or most of the IoT analytics tools available. Some of the IoT analytics tools we work with, and that can help you discover the signal in the noise and take your company to a new level of innovation: Power BI, Tableau, SQL dashboard, Thingsboard, Arduino, PlatformIO, IBM Watson, Raspbian, OpenSCADA, Node-RED, etc. Reach out to AceTek Solutions, you go-to IoT analytics service provider to know more about our IoT services and to enquire how we can help you implement these services in your organisation.
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