As businesses begin to embrace the opportunities offered by the IoT, digitilisation and analytics, many are unsure what they are going to do with all that information.

Key Messages

Five Patterns to look for:

  1. Add new value to existing products
  2. Combine data within and across industry
  3. Digitalising assets
  4. Trading Data
  5. Codifying Capability


Within product related service businesses, most of the thinking is centred on driving out cost through remote diagnostics and services. But this is just the tip of the iceberg. The digitalisation of products provides an even bigger revenue opportunity, often in ways not initially foreseen. As companies increase their maturity in the use of data, so the opportunities for them to innovate their business model increase. They are able to provide increasingly sophisticated services that differentiate them from competitors. However as implied by the connected services maturity curve below (as well as many commentators), managers should understand that it is not necessarily appropriate for all businesses to move to the right.

Connected Services Maturity Curve

Remote Services Maturity Curve

As many companies try to figure out how to use the data created by their products and processes, many are turning to Service Thinking to identify the profit pools that they can exploit within their customer and industry value chains. This is the process by which companies look deeply into the customer’s value chain, or the industry chain, and figure out how they use their knowhow and technology to make a difference to their customers. For example in the haulage industry, the truck itself only represents perhaps 8% of the running costs. 50% is the fuel bill and 25% the driver. Truck manufacturers such as MAN have developed servitized business models based on Telematics technologies that improve fuel consumption and turn the Truck into an operating expense. This data driven business model has enabled early adopters such as MAN UK to grow their business by a factor 10 over the past 20 years against a declining market.

Although ‘Service Thinking’ will help identify the areas of priority, how do companies go on to figure out how to develop data driven solutions. For example take SAVortex, a UK SME who have developed a SMART connected hand dryer with remote diagnostics. The idea was to use connectivity to dramatically reduce maintenance costs for hand dryers in large office complexes. This they achieved, but in addition found that the data they had on the usage of the toilets was even more valuable to their customers. Nearly everyone who goes uses a toilet also washes and dries their hands. By monitoring the usage of the hand dryers, large facility managers could infer the footfall in different areas of the building, so optimising heating, light and cleaning costs. These savings could in certain cases far outweigh those achieved within the original business model.

The question is how can we help companies make this type of leap in imagination. A framework originally developed by IBM and reported in the Harvard Business review, can help companies explore the value of their digitalised assets. The first step that a business can take is to be aware that there are typically five patterns of innovation through which data is typically monetized data. Frequently successful companies will combine 2 or 3 of these patterns in developing their propositions. Building an awareness of these opportunities, already starts to make the executive sensitive to the potential value their data has within the business.

1. Add new value to existing Products

This comes from understanding the data can be produced or potentially produced by a product and whether it is possible to generate insights from it. If we look at the potential for data, then with the reduction of sensing hardware and the opportunity of IoT, the cost and technology barriers have dramatically reduced in recent years.


But for most products that have been digitalised, there is already an immense amount of data being generated but not necessarily captured and certainly not analysed. The trick is to understand whether the insights that can be gained could add new value to us, our customers, our suppliers, our competitors or players in another industry.

The Savortex hand dryer is a good example of this. This company set out to use data that it could generate from its hairdryer, to create a value proposition that was not only for drying hands efficiently, it was aimed at the Facility management companies who could save energy and cleaning costs in their toilets through feedback on the performance of the dryer.
Other equipment manufacturers use the data they can generate in their equipment, to bring new value to their customers. Elekta, the leading Radiography Solution provider for cancer treatment has all its equipment remotely connected to a central customer support centre. Not only can they make their existing maintenance products more effective, they also can see the efficiency with which a Hospital Unit is using their equipment. This allows them to make recommendation to improve throughput based on their experiences across a wide number of customers.

Other manufacturers such as Rolls Royce Aerospace have taken this even further with their ‘Power by the Hour’ offer, where a new outcome based business model often called Servitization, has been developed. The basis of the business model is knowing exactly how an engine is performing and being able to predict its future performance. In this way they offer a service where they guarantee the engine will be available on wing. But more importantly they use the data to design the service into their future products, so that they can be even more effective. You can watch a video interview with Dave Gordon from Rolls Royce’s Defence business talking about how he has used data to develop new Service Revenue and value through this link.
The creation of data can lead to the developing completely new business models.. Wincor Nixdorf developed remote services to support availability contracts on their cash dispensing machines with great effect. What they began to realise was that the main cost of running a cash distributor is the management of the cash itself. To little and dissatisfied customers increase the operational costs. To much and working capital is unnecessarily tied up. As Wincor Nixdorf knew exactly how much money was in each ATM, they could combine this knowledge, with the locations of the ATMs and Bank’s own processes to manage the cash in the ATMs. This they did, creating a brand new business segment.

And its not only complex mechanical equipment to which this approach applies. SKF , the bearing company is well recognised for introducing miniaturised self powered sensors that can continuously monitor their operating conditions. This enables SKF to offer a whole raft of new services that enable their customers to understand the performance of their bearings and some make their equipment more reliable and less vulnerable to unscheduled down time.

2. Combining Data within and across industries

Is it possible to combine the product data with another data set to create new value? In the truck example the driving habits of the driver could be analysed by MAN through the telematics. When combined with the drivers names held by the haulage company, training could be recommended to improve the capability of drivers to optimise fuel efficiency enabling profitability to be doubled!

Another very simple example comes from Listo, a small Northern Irish SME who enables IOT solutions for their customers. They were working on a solution for the maintenance of submersible pumps in Landfill sites to prevent contamination of the water table. Remote monitoring enable the sites operations team monitor performance to ensure expensive regulatory fines are avoided. Add to this data a weather forecast on rain, and alerts could be taken to take preventive action where pump performance was known to be marginal.

These are very simple industrial examples to illustrate the point. As the knowledge of big data and in particular the mathematical technologies that underpin this form of analysis such as machine learning, become better understood, it will become easier to combine datasets. Machine learning is a technology that is particularly interesting for industry, as these are self learning algorithms that become very good at identifying anomalies in large data sets and comparing large data sets to draw conclusions. Increasingly used in fraud Management in the finance industry, these technologies are also more and more being used for predictive maintenance across different industries

3. Digitalising Assets

The key to digitalising assets is to understand:

  1. Which assets are digital in nature and how can this feature be used to increase their value?
  2. When is it possible to turn physical assets into digital assets?

An example from the industrial service world is that the time is approaching when some spare parts are not held as physical stock, but as a digital drawing. When the part is required, the drawing is down loaded to a 3D printer at the point of need for the part to be produced. This has significant implications on the business model for spare parts and where value is created.

Digital assets present an opportunity to the group that manages the assets, but it is also a threat for companies. Digitalisation typically slashes the distribution and logistics costs enabling new business models

4. Trading Data

Can data be structured and analysed to yield higher value information? Again the SAVortex example is a good example where the usage information of the dryer is extremely valuable to the facilities company and in effect is sold as a separate service.

Another recent example is the tie up between Tom Tom and Vodafone. The mobile network of Vodafone, able it to know the speed, location and direction of their subscribers. They can sell it, because it is extremely useful to Tom Tom to pinpoint jams and provide premium services to its users.

5. Codifying a Capability:

The question is does a company have a significant capability that can be digitalised and which others value. Many industrial companies have a huge amount of intellectual property which if put on a digital platform can yield immense value to various stakeholders. For example the bearing manufacturer SKF has many industrial apps which their customers and channel partners. Customers can directly use the apps to improve their equipment and business. Channel partners use the technical data to make it easier for SKF products to be specified into industrial products. SKF in monitoring this activity has another tool with which to improve its customer intimacy with clients and partners.

Key to success is to embark on this process with a cross functional team, adequate resources and top management support. The latter is important as combining these patterns of innovation can challenge existing business models and lead to new ways of defining the business.

With these in place, the next step is to know what data you have from your products and operations. What data can you access but are not capturing? Do others have data that would be helpful to you and how might you collaborate with them. Then by examining each of the five patterns, ideas begin to emerge and develop. Often an idea will aply to multiple patterns. Here are some questions developed by IBM, that companies can ask themselves as they go through the process.

  1. Add value to products
  • Which of the data relate to our products and their use?
  • Which do we now keep and which could we start keeping?
  • What insights could be developed from the data?
  • How could those insights provide new value to us, our customers, our suppliers, our competitors, or players in another industry?
  1. Combining Data
  • How might our data be combined with data held by others to create new value?
  • Could we act as the catalyst for value creation by integrating data held by other players?
  • Who would benefit from this integration and what business model would make it attractive to us and our collaborators?
  1. Digitising Assets
  • Which of our assets are either wholly or essentially digital?
  • How can we use their digital nature to improve or augment their value?
  • Do we have physical assets that could be turned into digital assets?
  1. Trading Data
  • How could our data be structured and analyzed to yield higher-value information?
  • Is there value in this data to us internally, to our current customers, to potential new customers, or to another industry?
  1. Codifying Capability
  • Do we possess a distinctive capability that others would value?
  • Is there a way to standardize this capability so that it could be broadly useful?
  • Can we deliver this capability as a digital service?
  • Who in our industry or other industries would find this attractive?
  • How could the gathering, management, and analysis of our data help us develop a capability that we could codify?

Having identified opportunities, they are collated and prioritised. One or two are selected to be investigated in more detail and an action plan agreed for theur development.

This whole creative process is greatly facilitated by two further actions;

  1. Having a strong technology presence within the team who can understand how data can be extracted, exchanged and mashed up.
  2. Having input from external parties who can bring an Out-Side in perspective to the technology and business challenges

What is clear is that opportunities are growing for product companies to find new value from the data they generate. With an open mind-set, some determination and a structured approach, this provides industrial companies with a significant opportunity to grow through embracing the digital economy.

Written by Nick Frank, Managing Partner at Si2

This content is typical of the Management Insights that reside in our Knowledge Centre. You have unlimited access to this content when you take out an Si2 On-Demand subscription