It’s easy to write-off the Internet Of Things (IoT) as a great technology solution looking for a problem; yet another acronym clogging-up the hype cycle.

High performance organizations, however, see IoT very differently. For them, IoT is already on the frontline, where data and machine learning combine to power them exponentially ahead. When these organizations look at IoT, they don’t see a new technology to make things connected.

Instead, they see a business decision – and a better way to inform it.

A Nervous System For Your Business

The rate of technology innovation continues to dramatically increase the power and miniaturization of mobile phones, computers and sensors – putting ever smarter, cheaper and faster services at our fingertips.

For smart organizations, this opens up new opportunities to gather more data, more granularly in real-time: putting it in the hands of decision-makers at all levels of the business.

These organizations know IoT is not just about connecting devices: it’s a tool to connect up intelligence. This tool can be your business’ eyes, ears and memory – one that never sleeps, and never stops. Combining IoT devices and machine learning capabilities creates a nervous system for your organization: learning how it operates, how customers interact with your products, and feeding in higher fidelity insights that allow you to make better data-informed business decisions.

These decisions range from very local, tactical decisions – like when to restock shop shelves – to global strategic decisions, such as continuing with a brand supplier or category. Businesses based around this sort of data-informed decision-making outperform, and out-earn, the competition.

In short, if you’re not learning, then you’re missing out on earnings. Or worse still, you are investing valuable resources without sufficient evidence or clear understanding of the outcome it achieves.

A healthy IoT nervous system fuels this learning, and enables software to better fit with your business reality. In turn, this leads you to better business decisions, a more responsive organization, and higher performance.

The Connected Learning Loop

We developed a simple, three stage model to help people think through how they might apply IoT to their own business – meet the Connected Learning Loop.

Connected Learning Loop

Target Value Hypothesis

Before diving straight into the technology solution, any new initiative needs to target a business hypothesis that these new tools could help address. Key questions to address include:

What questions do we struggle to address as a business?

Which indicators would help guide our thinking?

Where is the richest source of insight?

Who needs to know more, or is best positioned to act in response to this information if we had it?

Pinning down and prioritizing these questions, with the strengths of connected devices and machine learning capabilities in mind, is the first step to a more responsive organization.

Connect And Collect

Once we have defined a question to answer, it’s time to get started collecting data by connecting devices.

This shouldn’t mean providing a mountain of raw data streams from exciting new sensors. Maintain your focus on the business questions you’ve identified, and ensure the data being ingested and analyzed is directed at the hypothesis we’re aiming to gather evidence for.

Machine learning is the key enabler here, taking what might have been a previously unmanageable volume of data, and processing it to be decision-ready. For example, these algorithms could turn a raw camera feed of a retail store – manually analyzed by people after the fact – into a responsive feed that includes, at any given moment, live counting, path tracking and group size monitoring of customers in a service environment.

Actionable Insight

Once data is collected and processed as appropriate, it’s time to trial how well it can inform responses to our targeted value hypothesis.

The most effective applications of collected data are uncovered by putting new information in the hands of those most able to act upon it. This means those on the front line of the business, active on the outer edges, can preempt issues, delight customers or optimize daily operations in real-time based on real evidence. This closes the Connected Learning Loop and feeds insight into additional value hypotheses to target.

How Your Organization Can Start Learning Today

The most exciting thing about this approach is that it doesn’t take a multi-year, multi-million dollar programme to start realizing the benefits of IoT. Anyone, in any organization, can get started today.

Together, Transport for London (TfL) and TAB sought to improve how efficiently the Underground network answered the question: ‘Is there a more efficient way to test the brakes on a Tube train?’ Existing technology was cumbersome, expensive and required a Tube train to be removed from service for testing – resulting in costly disruption to the network.

Over the course of just five days, a small team conducted a technical proof-of-concept that demonstrably proved an iPad could be used to test the brakes of a Tube train as accurately, and much more cost effectively, than existing brake testing technology. Over the subsequent months, the product was developed further and robustly tested alongside the existing solution. The new tool, known as TfL Decelerator, is in pilot phase. Across three lines alone, Decelerator is projected to save almost $500,000 per year – scale that up across the network, and the savings are considerable indeed.


Don’t Wait To Experiment With Connected Learning Loops

You don’t need a mountain of time or money to realize the benefits IoT has to offer. As projects like TfL Decelerator show, smartphones and tablets offer connectivity, light, audio and motion sensors that can provide the minimum viable infrastructure for new insights, right out of the box.

All you need is a small and empowered cross-functional team. Give this team a clear question to tackle, and get them to work through the Connected Learning Loop. Ensure they are feeding back lessons learnt as they go, and use it to inform future actions. This lightweight, small scale approach to real-world business challenges enables you to trial new innovations and gain the essential evidence you need to see if it’s worth scaling it across your entire business.

It’s time to get learning, and start earning.

This article with co-authored with Emily Maginess, Brett Thornton, and George Proudfoot from TAB