Updated: September 3rd, 2018

Analytics is a loaded word in 2017 for manufacturers in industrial, commercial and consumer goods industries. Can analytics really improve decision-making, enhance operational efficiency, and predict future behavior?

Yes, but only if you’re collecting the right data, leveraging the right algorithms to analyze the data, and making actionable insights available in a timely manner. And that’s a tall order.

IoT Analytics – for business operations

If you google ‘IoT Analytics Platform’ or ‘Analytics of Things”, you’ll come up with dozens of companies who offer solutions that analyze sensor data produced by IoT systems.

These predictive maintenance analytics systems are built for asset-intensive sites and business processes – such as smart plants, factories, buildings and cities, fleets, and supply chain operations. The goal? Supporting strategic objectives like predictive maintenance, route optimization, fleet optimization, remote asset monitoring, and production optimization.

However, these IoT analytics platforms don’t address the needs of the OEM manufacturer or product team – the team that ideated, designed, and built the IoT-enabled industrial machine or commercial equipment. So what is available for their needs? 

IoT Behavior Analytics – for the product manufacturer

IoT behavior analytics in the Seebo Platform
IoT Behavior Analytics in the Seebo Platform

A new breed of analytics is emerging for IoT – Behavior Analytics. Behavior analytics is complementary to existing analytics platforms and offers entirely new value for product teams.

IoT Behavior Analytics directly gauges how the smart machine, product, or system is functioning in ‘real life’, while comparing the actual behavior to the system spec. In so doing, product teams gain immediate visibility into which features are being used and how – and as importantly, which features are not being used and why.

Product teams can then go back to their specs, design modifications that increase user adoption, eliminate useless costly features, and add new value to the customer.

Of course, all these improvements depend on the basis for the product or system: The IoT Model.

Why? Read on.

A model-driven approach to Behavioral Analytics

In order to understand what is happening with your system – including if there are delays or errors – you’d have to recreate the rule structure on whichever analytics platform you choose.

But recreating a model of your IoT system – or in some cases, creating a full model for the first time – is time-consuming, and can lead to errors in data collection and analysis.

The Seebo Industrial IoT platform is unique, in that your analytics dashboard is automatically generated from your IoT model: a visual blueprint that defines how a product or system will work, look, and act, as well as the analytics insights it will deliver.

IoT Behavior Analytics and the IoT Model | Seebo IoT
An IoT model on the Seebo platform

The moment a system of units – product or products, app, cloud and others – is modeled, the platform automatically generates the data structures and analytics dashboards to describe the system’s behavior when in market.

Once your system is live, the ‘analytics’ mode is already set up with a dashboard listing every rule and every possible use case that you created in your IoT Model. Since you don’t have to reverse-engineer the rules, components, relationship between units, and data structures, the opportunity to err is much smaller and the time-to-insights minimized.

Behavior Analytics – What’s in it for you and your customers

Learn, optimize, improve

Behavior Analytics makes it easy to see how your users are engaging with your product, so you can make informed decisions about product improvements and gain valuable data on your customer’s wants and needs.

Analytics help you pinpoint which smart capabilities are crucial and worth improving – and which aren’t being utilized, and can be discarded to save time and costs.

For example, if your IoT system has a specific web app dashboard that users more or less ignore, then enhancing that app would be a waste of valuable resources. 

Perform Root Cause Analysis

Behavioral analytics also provides product teams with tools to perform root cause analysis for system faults. You can analyze data coming from a system, and if you see misuse, understand the root cause and fix the issue early on – even before a user becomes aware there was an issue.

For example, if a high temperature in a factory or work environment is supposed to trigger an alarm on a mobile or web app, but it doesn’t, an analytics dashboard would show root cause of the error (a faulty sensor, connectivity, error, etc.). The manufacturer then has early warning that there is a bug in the system to fix, together with precise information to determine the cause of the fault.

Plan your next release, smarter

Besides using root cause analysis for products on the market, you can use that and all other in-market product usage data to plan your next product iteration. Remember that cool ‘web app’ which your customers were ignoring? Cutting it from the next release will save production costs without reducing customer satisfaction.

Turn Products into Services- create long revenue streams

Many companies sell products, and only see revenue after they sell another product. With behavior analytics, you can build a subscription-model, offer analytics-driven services to customers, and continue to see revenue from a customer during the lifetime of a single product.

The use of behavior analytics is already quid pro quo for companies who implemented IoT systems, and for good reason: it reduces risk, improves the offering, saves on production costs, and creates new value for the client or end user.

Looking for more info? Learn more about the benefits of behavioral analytics for IoT.