Moore’s law refers to an observation made by Gordon Moore that the number of transistors per square inch on an integrated circuit board (aka IC chip) had doubled every year since its invention and he predicted that that trend would only continue.
That is certainly the case with compute capabilities getting faster, smaller, and less expensive. Couple that with readily available high-speed Internet, and you have the recipe for an exponential rate of the Internet of things (IoT).
The business value however is not in the collection, package, transport, or storage of data - but rather the answers and insights we gain from analyzing it. And there is a major difference between analytics for IoT and traditional analytics. Where traditional analytics is focused on a centralized data set, IoT analytics is performed anywhere from edge-to-insight.
Organizations now have an opportunity to monetize the insights gained from analyzing IoT data at different levels within the IoT ecosystem. As a result, there are opportunities to increase process efficiency, deliver better customer experiences, and generate new revenue streams. If you do not have an IoT strategy you can be assured your competitors do.
Edge devices form an intelligent network whereby being interconnected with unique identities allows them to sense their environment and communicate to each other about the state of the environment. Analyzing the data in gaining insights generated by such networks can help organizations in true digital transformation by making more informed business decisions.
To harness new capabilities of IoT analysis, analysts need to think differently about their tools, methodologies, and techniques. A plan needs to be developed by which analytics will be performed throughout the IoT ecosystem in addition to the centralized data repository.
IoT analytics presents a unique ability to generate value at the point of data Tweet this
In some cases, edge devices will collect all the data, about everything, all the time. In other instances, edge devices may be trained through machine learning to only collect the best data, and the best way, at the best time while still processing some level of transaction history of information that is “ignored”.
A central consideration to how one should architect their analytics design is through definition of the outcomes their wanting to achieve. Edge devices may process analytics in addition to communicating the outcome either to other devices, gateways, or the cloud.
The key outcome to an IoT solution is to significantly improve the time-to-value of data. Tweet this
Edge analytics however could create a potential security vulnerability if not implemented properly. Organizations should ensure that edge analytics is part of their overall security strategy to ensure that the data is accessible and integrated but not viable.
Read more about Securing IoT
There is a huge potential of monetizing IoT data that extends beyond traditional customers in extends to stakeholders, partners, and vendors. This monetization potential comes in the form of being able to archive localized analytics in addition to overall insights. Consider the example of a vehicle tire: if you’re able to create a Smart tire in collect data on its performance under different conditions that information could be utilize as an advisory service on overall tire life enhancement for vehicle owners in addition to tire manufacturers who want to improve its design.
Monetizing analytics starts with a group of directly connected organizations of your smart product such as telecommunication, hardware, OEMs and end customers. This can extend to additional groups who may provide support in some direct or indirect fashion such as insurance companies, finance providers, and retailers.
The ability to have insight at various levels in the hierarchy can be adopted to serve the requirements of these various groups throughout the ecosystem thereby saving money for some while improving opportunities to generate additional revenue for others.
There are two potential sources for monetizing IoT analytics:
Accommodation business operational analytics can help organizations enhance their performance metrics and ultimately drive financial improvements. This is because predictive analytics can significantly optimize costs organizations which is trending at 2 to 4% from a 50% penetration of IoT in manufacturing. If we consider that the global cost of manufacturing is $25 trillion cost savings of $500 billion could be realized.
The IoT analytics market is expected to grow at 27.5% to $16.35 billion by the year 2020. Tweet this
According to IDC 55% of manufacturers have undertaken some type of IoT initiative in the form of research, pilot programs. The IoT market is comprised more than 25% of manufacturing with oil and gas and energy organizations leading the wave of adoption.
Manufacturers capturing large volumes of data due to this high-rise IoT data capture. Microsoft estimates that 371 billion in additional revenue can be generated by taking advantage of this opportunity and McKinsey pegs the value created by IoT applications at approximately $1.2 trillion by the year 2025. McKinsey states that the majority of this will be seen in improvements in inventory optimization, operations management in addition to predictive maintenance.
While many organizations see IoT analytics as something to pursue there are a number of things that need consideration in order for the true potential of IoT analytics to be realized.
Now more than ever customers demand a high level of service, they’re looking for personalized products that give them a type of experience they desire. Organizations are now having to overhaul their business model to these new drivers. In the case of manufacturing where a customer makes a custom order,
IoT has the potential to reduce production time from weeks down to hours. Tweet this
IoT has the potential to generate even new types of customers to your business. In addition to traditional and customers that an organization may have there are new customers that are relevant because of what IoT affords.
Production activity in most organizations is extremely complex; there are programs made of the projects, projects made up of work streams, work streams made up of activities, and activities with specific tasks, that may themselves have a policy or procedure. All production activity drives the efficiency of the resulting yield. IoT enhances the ability to monitor at every level of the production process. This is where that edge analytics really shines in its ability to deliver value at the point of data.
IoT and manufacturing has some unique challenges:
Organizations must understand their customer needs and identify opportunities where IoT data can help provide the answers and insights to meet them. Customers have different needs and wants, some real others perceived. By understanding the specific needs of different customer types organizations will have a better understanding of the type of information data required to perform analytics.
Organizations should also perform discovery and analysis of various stakeholders throughout their ecosystem in the insights they might require from analyzing your IoT data.
As a result, organizations must have a strong data strategy. Often the approach is to focus on internal business problem and as a result will discover new ways of improving their data capabilities resulting in higher levels of value from those that do not. Realizing the full potential of IoT data will require organizational change management to be carefully thought through. Here are things that organizations should be doing today and adopting IoT: