Visibility is the start point for driving business value

Developing visibility of events through gathering data, and knowing that something has happened is just the start point for deriving the maximum value from the internet of things.  Causality builds on visibility to provide a ‘cause and effect’ relationship;  why does something happen, what factor(s) affect my operation? Some may be within your direct control, will not but, at least if we can model and understand the major factors, it gives us a chance to identify when they are likely to occur and to mitigate their impact.

Context is all important to determining Causality

Causality is a complex topic. Some would say the whole world is a massively complex cause & effect network that no one will fully understand because there are too many ‘moving parts’.  We’re all familiar with the concept of the butterfly flapping its wings in one part of the world causing a hurricane in another, but for most businesses the causes and effects are more directly connected and more identifiable. Once you have a view on the cause you have the ability to introduce ‘some’ control.

At its heart, causality applies meaning to the data generated from your sensory network, in the context of your specific business. Knowing the temperature of your lorry is 5C has a very different meaning if you’re carrying ice cream or fresh fish.

Additional data points provide context to understanding causality

On its own, data from your sensory network is limited in effect, and to really understand the major factors affecting your operations requires additional data points.  For example, in a logistics business, time of year, time of day, traffic conditions, age of driver, driver health, driver alertness, type of goods, weather conditions, type of vehicle, condition of vehicle and route all have an effect on your ability to deliver?  Certain combinations of these may affect your delivery schedule and that some increase the risk of an accident, potentially fatally.

For a retailer, understanding of inventory location, volumes, stock outs, misplaced stock, shrinkage, staff movements, customer movements and customer demographics within the store, linked to sales by item and overall store profitability might be quite useful, preferably in real time, to effect an immediate response to improve sales and customer service. There is no retailer that can do this now but none of them would deny some cause & effect across these elements. After all, you can’t sell what you don’t have on your shelves.

Many manufacturers have limited real-time visibility of their production process and hence limited understanding of their Work In Progress. They are unable to see how production flows through their factory (i.e. the visibility bit), the effect of the workforce on productivity, the external factors affecting output. For example, the product moved to ‘the side’ for further work which then gets lost.

Agriculture is ripe for the adoption of sensor-based farming to understand how to improve yield based on weather, soil consistency, seed type, animal fertility patterns and many other factors.

Data volume can overwhelm our ability to digest it

Put simply, the expected amount of data generated from connected devices AND the influencing factors will overwhelm the ability of people to digest and understand, model it and keep the model up to date. Luckily there is a capability that can help.  Data Scientists are in short supply but just as once people used to weave cloth by hand, the time is now here where a single person can’t ingest and understand the volume of data. It is here that emerging  technologies in Machine Learning and Artificial Intelligence provide assistance.

Machine Learning is not ‘magic’ by any means, it is not the silver bullet, but it can offer a way to derive meaning in the high volume, data-led world. It doesn’t look at a data set and ‘suddenly’ infer that your factory is or isn’t operating at maximum efficiency.   Human inputs are required to determine what is ‘right’ and what is ‘wrong’ and the shades of grey in between.  It needs time to build up enough data to be able to spot trends and understand patterns. It also takes a lot of computing power.

There are no turnkey solutions

Do not expect your partner or supplier to turn up with a turnkey solution that has been proven 50 times with a clear business case and value proposition. Where we have been successful and the customer has seen tangible benefit, it is through co-creation and collaboration. This is not necessarily a natural process for many System Integrators nor for many customers, but approaches such as Design Led Thinking and a consultative, business benefit realisation exercises can be effective.

In summary

Finding business value can become mired in a ‘technology’ based rather than business case focused approach or a lack of understanding around change management and technology adoption. Internet of Things projects are not like a ‘traditional’ SAP or Oracle ERP, or a desktop support services, data centre or even cloud migration project. It is new, exciting, dangerous and risky.

The Internet of Things is something of a Brave New World, full of contradictions. It is risky, it is rewarding, it is uncertain, it is valuable, it can easily be a waste of time but it is coming and the time to get on-board is now.

This article was originally published on IOT Advisory Consulting and is reproduced with the kind permission of the author.

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