Industrial IoT platform capabilities grid

The Industrial IoT Platform Buyer’s Guide: Capabilities Grid 2021

Industrial IoT platforms hold the promise of full factory visibility and a complete picture of all manufacturing data. Well within 2021, use cases are beginning to show even greater, more granular data-driven insights into operations on the shop floor, and industrial IoT platform adoption is becoming the norm for most enterprises. 

The industrial IoT platform consolidates a variety of strengths in providing a solid basis for data-driven decision-making. Bringing together IT, OT, and data science teams in one place, it greatly facilitates the path towards greater process efficiency. You start with connecting your industrial assets to reliably and consistently tracking, optimizing, and innovating within your evolving IoT landscape. 

And getting there starts with choosing right. In this week’s edition of the Industrial IoT Platform Buyer’s Guide, we are focusing on the critical capabilities of industrial IoT platforms and provide a blueprint for creating your own capabilities grid to guide you through the selection process. 

industrial IoT platform capabilities

Get the checklist and create a capabilities grid for your IIoT platform

Read this article to learn more about the latest trends and insights around industrial IoT platforms, covering Gartner’s Magic Quadrant for Industrial IoT Platforms released in October 2020. 

Getting started with IIoT

IIoT begins with setting up connections with your data sources. And then, the crowning achievement of your IIoT initiative is a fully connected factory with a consolidated data strategy that extends from the edge to the cloud. 

Typical IIoT use cases include predictive and preventive maintenance, asset condition monitoring, asset tracking and quality management, traceability analyses and overall equipment effectiveness cases. Edge-to-cloud scenarios additionally unfold towards artificial intelligence and machine learning solutions with bi-directional data flows moving from the edge to the cloud and vice versa. 

To gain insights into the complete picture painted by data, you need a hardware-independent platform that allows you to connect almost anything and make any industrial asset available to the internet of things.

Full data visibility on the shop floor and beyond entails using both local and cloud analytics, enterprise and cloud systems, app development capabilities in the cloud, and ML running at the edge. 

Enabling organization-wide data availability

Next, the right data has to reach the right experts within the industrial enterprise without getting siloed in isolated departments. Once all machines are connected and the data has made its way to the right recipients, it is time to uncover the value of the data. Doing things with the collected data is what eventually leads to more transparency and efficiency. And doing things with data means making the data tangible and accessible to various experts at various levels of know-how. 

An increasingly sought after capability is the possibility to run on-premises use cases but also move the data to advanced cloud systems to build machine learning models. The Record Evolution Platform offers just that. It can be deployed on-premises only, within a closed-off enterprise environment, but it also allows for hybrid and cloud-only IoT deployment.

Further, you can integrate with most of the common cloud systems such as AWS, Microsoft Azure, and Google Cloud Platform. The platform allows organizations of any size to connect their existing assets, analyze data at the IoT edge, and move the data along the value chain across both local and cloud systems. 

This is how organizations gain the upper hand in a crowded space as analytics at the edge means accelerating insight, which translates into faster decision-making. Edge computing enables organizations to take action instantly, and move quickly towards improving just about any aspect of their operations. 

Settling on a data strategy

After onboarding, the real challenge is selecting a data strategy that will best serve the organization. This means using the right kind of machine data where and when it matters. To address this challenge, the IIoT platform needs to be equipped with a predefined set of capabilities to achieve full transparency and efficiency, starting from the edge and all the way to the cloud. This way, organizations have full access to each stage of the process. You collect IoT data at the edge, use the data to optimize IoT apps in the cloud. Then you roll app updates out over the air under constant consideration of the incoming IoT edge data. 

In a fast-moving environment, industrial manufacturers are best served with an open IIoT platform. It ensures efficiency while remaining flexible and capable of change. Especially in the case of hardware-independent platforms, manufacturers can give their operations a lift without the necessity of replacing existing equipment. 

The ultimate achievement facilitated by this edge-to-cloud cycle is the ability to make better business decisions at a greater pace.

The ideal is a fully formed IoT development cycle that covers all functions at the IoT edge, handles multiple heterogeneous devices on the shop floor from within a single venue, performs both IoT edge analytics and data analytics in the cloud, and is able to seamlessly integrate with any other local platform as well as with the leading cloud players such as AWS, Microsoft Azure, or Google Cloud.

So here is our industrial IoT platform capabilities checklist:

Connecting to IoT data sources

  • Data collection from any connected device
  • Common data layer
  • Data accessibility and data storage

Data Integration

  • Built-in data source connectors
  • Multi-source raw data import
  • Big Data capabilities and implementation
  • Enables integrations with leading cloud providers

IoT analytics

  • Data pre-processing and transformation in data pipes
  • Custom visualizations in dashboards
  • Instant reporting with extraction of standard KPIs
  • Statistical and analytical queries
  • Building and running machine learning models at the IoT edge

IoT Application Development and Deployment

  • Full application management 
  • Container-based app development
  • Public and private IoT app store with app templates and custom industrial applications
  • Live IoT development in a cloud IDE 
  • OTA deployment to multiple connected devices
  • Any device and any protocol 

Device Management

  • IoT device setup and configuration with just a few clicks, no programming needed 
  • Location tracking with a unified map view of all connected devices
  • Device status monitoring and instant maintenance options, including lifecycle management
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About Record Evolution

We are a Data Science & IoT team based in Frankfurt am Main, committed to helping companies of all sizes to innovate at scale. So we’ve built an easy-to-use development platform enabling everyone to benefit from the powers of IoT and AI.