It’s no surprise that exciting new technologies are generating startling insights from big data, but very few come from the B2B sector.
It can be difficult for industrial executives whose performance is measured in dollars and cents to become excited about bits and bytes, especially when they have been fairly dismissive of big data to date, labelling it a B2C phenomenon.
That mindset, however, changes with the Internet of Things (IoT), where companies that cannot deliver optimal performance through analytics will not be able to compete.
The new reality is that ubiquitous sensor data means companies must disrupt or be disrupted, and the challenge to business leaders is to answer two fundamental questions. Firstly, what new business models must I deploy in order to compete? And secondly, how can I build organisational structures that can execute those models, bridging internal divisions between corporate functions and business units?
>See also: 5 predictions for the Internet of Things
You will note that neither of these questions relate to technology. Why aren’t we talking about how many billion devices will be connected in the coming years, how much storage we will need to contain all that sensor data, or how quickly we can hire the smartest data scientists?
Consider the three major transformations in our industrial history. They’re certainly stories of technological advances, but more than that they are stories of enablement.
In the early 19th century, the steam engine enabled the Industrial Revolution, spurring new manufacturing processes that created industrial giants selling affordable products at scale.
In the mid-20th century, computers enabled the Information Age, creating software giants selling affordable services at scale. And at the turn of the 21st century, the internet enabled ecommerce and created the web and social media giants who dominate today’s business landscape and sell data products at scale.
If – as many claim – IoT is to be the fourth revolution, what will it enable, and what will be the new class of giant to emerge?
IoT represents the constantly-growing universe of sensors and devices that create a flood of granular data about our world. The “things” include everything from environmental sensors monitoring weather or energy usage, to “smart” household appliances, telemetry from vehicles and production lines. Devices of all types are moving online and connecting.
The giants of the internet (of people) have thrived because of their ability to gather every possible bit of data about human behavior and interaction, and use it to understand and predict that behaviour.
Armed with this information, internet companies can take actions that create profits for themselves by delivering great experiences for users.
IoT extends the same principle to devices: manufacturers can gather data about how their products behave and interact, and use it to understand and predict future behaviours.
Using that data, companies can optimise performance and drive profitable outcomes for themselves through great user experiences.
Increasingly, those experiences are delivered as services, and users pay for the outcome, not the physical object delivering it. This pivotal change to the delivery of product as a service underpins many IoT projects.
The concept is not entirely new, of course. The most notable example is jet engine Power by the Hour, pioneered in the 1960s by Rolls-Royce. This isn’t renting or leasing; it’s a performance-based contract that creates a powerful alignment of incentives for both the operator and manufacturer.
These incentives include reliability of service, reduced capital expense and accurate cost projection for the operator, mitigation of after-market competition risk, stable revenue projection, and reduced risk of product commoditisation for the manufacturer.
The IoT-enabled conversation with products and devices makes this proposition compelling for companies selling equipment much cheaper than jet engines. Whether the customer needs compressed air, forklift capacity, office printing or a host of other services that are delivered today as products, tomorrow’s giants will be the physical/digital engineers who are able to use data to optimise their performance.
The new giant
The implications go far beyond how products are sold. In the words of GE’s Jeff Immelt, “Industrial companies are in the information business whether they want to be or not.”
The capability that manufacturers now have to use data from sensors enables them to change the way that they design, upgrade, and maintain devices in the field. The result will not just be greater efficiency, but entirely new functionality and levels of service.
Immelt was right to refer to information, but IoT sensors churn out data – lots of data that must be analysed to create useful information. Analytics is the critical link between monitoring operations and optimising performance.
Smart, connected devices can only be as intelligent as the instructions they are given. The intelligent operation of things is dependent on effective ‘Analytics of Things’.
Product development in most industrial companies takes place in operational business units. Enterprise data management and analysis is the domain of a central IT function.
In far too many companies, there is a sizable gulf between the two. Trust is lacking and efforts to merge the functions frequently descend into turf wars.
Because of the necessity to link Analytics of Things with operations, any organisation looking to harvest the potential of IoT must find a way to bridge this gulf.
Vested interests and desire for control must be set aside to pursue the greater prize. That is why, more than anything, IoT represents a business leadership challenge – flexibility, collaboration, agility and invention will win the day.
This is a challenge that some will embrace and some will dismiss. Like any revolution, there is a risk of failure. There will be winners and losers, and some won’t know which they are until it is too late.
What does this all mean?
To monetise IoT data, companies should clearly identify IoT opportunities and a strategy – with participation and sponsorship by business leadership.
Keep in mind that the data and information ecosystem complexity may call for collaboration with an experienced analytics services team to support new go-to-market models.
For example, strategies may be geared to the application of machine learning and advanced analytics techniques to deliver automation, or deep understanding of usage patterns that influence design or shift to DevOps deployment, or predictive part failure analysis to facilitate lean processes.
Regardless of the details, organisations should consider continuous monitoring of assets to enable new revenue opportunities and pricing strategies based on performance and pay-per-use models instead of purchases.
Early warning detection systems that use predictive analytics can find and correct issues with machines and devices sooner. And real-time monitoring and analysis of physical assets allows companies to understand and act on a variety of real-time insights.
These recommendations are derived from real-world use cases. Companies that have made investments in IoT analytics are realising significant business benefits as a result.
Sourced from Steve Matthews, IoT practice director, business consulting, Teradata