So what do you look for when selecting an IIoT platform vendor? It comes down to asking the right questions when you are choosing the right platform to make your Industrial IoT projects a reality. In this blog, we’ll cover the best practices for when you evaluate the right vendor for your business. You can also watch the accompanying webcast of the results from our IDG Quick Pulse Survey ‘CIO Survey: IIoT Adoption – The Real Barriers & Opportunities Ahead.’
With the relative immaturity of the Industrial IoT and many enterprises still struggling with data integration it is important to choose a vendor that can accommodate your needs and grow as your projects mature.
Interestingly, the recent IDG Quick Pulse CIO Survey revealed that the majority of senior IT executives are not prepared to handle the volume, velocity and variety of data being generated by the IIoT. 70% have not implemented an IIoT strategy because they are either not prepared, and are currently struggling to effectively manage their data. They are still in the “consideration” or “early discussions” or “planning” phase.
Through the survey we also found that as IIoT maturity goes up, senior IT executives’ confidence in current data integration tools declines by half. CIOs are not confident that their current systems can accommodate IIoT as it matures. So in the early stages of Industrial IoT project planning it is important to outline requirements and capabilities that are critical to the success of your projects. Here are some best practices we recommend for Industrial organizations planning to implement an IoT strategy:
Best Practice #1 - Integrates data from siloed systems in wrist-watch time
In today’s market there are not a lot of experts and solutions that are capable of tackling the magnitude of the data integration challenge. It’s critical that project time and budget should not be spent stitching together data from disparate sources. Look for a solution that applies machine intelligence to intuitively create a semantic model, with the ability to dynamically adapt to any source, to fully automate data integration and offload the heavy lift for IT.
In the CIO Survey, senior IT executives resoundingly chose the ability to correlate data from any source into a common data model as the top priority when evaluating IIoT platforms. 70% say that having a proven model for data mapping ranked higher than everything else.
Best Practice #2 – Supports data sources & processes specific to your company
We know that every industrial organization and each industrial vertical has their own unique data types, communication protocols and internal processes, which can make selecting a vendor challenging. Don’t look for a solution that perfectly fits every use case you’ve outlined but a platform that is data type and industry agnostic, with the resources or ecosystem to support customization.
Best Practice #3 – Delivers predictive & actionable intelligence based on your data
Data means nothing if it isn’t providing real business value. What differentiates a next generation analytics solution from the data warehousing tools and BI solutions of the past is the ability to deliver actionable data intelligence in real-time to improve operational efficiency through asset performance optimization, and more. A solution with a federated architecture to provide analysis in real-time on data in motion from edge devices to the cloud provides a significant advantage in the industrial market.
Best Practice #4 – Ensures time-to-value expectations are actually met
Avoid the pitfalls of lengthy projects that are slow to show value. Choose a vendor that takes an agile approach that prioritizes automation by leveraging machine intelligence and complex decision processing through asymmetric assertion logic.
Best Practice #5 – Extends beyond out-of-the box functionality of the platform
As your Industrial IoT project matures and as new data becomes more accessible, new use cases, business models, and innovations will emerge putting pressure on your tools. It is critical to select a vendor that enables an accelerated development lifecycle by providing the tools to rapidly iterate beyond the scope of your initial project. Look for a vendor solution that provides tools to support the full software development lifecycle. Including an Integrated Development Environment (IDE), a Software Development Kit (SDK), extensive documentation, a catalog of pre-existing analytic plugins, overall an extensible and scalable framework
Best Practice #6 – Easily exports learnings to other analytic tools
We know that from experience that large industrial enterprises there are different departments that have already invested in tools they like to use. A solution that lives within its own silo is no better than the point solutions currently in the market. Choose a solution that lends itself to the wider Industrial IoT ecosystem and can seamlessly export learnings to third party analytics tools. Capabilities to look out for: data services that support common APIs, dynamic protocol translation, and a pluggable architecture for third party analytics.
Best Practice #7 – Supports flexible & secure deployment
It is important to ensure than an Industrial IoT platform Is both available in the cloud, data centers, and at the edge. Look for a vendor that can offer a highly available, flexible and secure framework for deployment. Ask the vendor critical questions about flexibility in deployment either on premise, in the cloud, or a hybrid model. Ensure that you have the option to control your own network topology and infrastructure components.
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IDG Quick Pulse Survey Resources