IoT and Classical Business Models

Many companies, especially in the Information Technology (IT) section are aware of the IoT explosion, one of the biggest challenges facing any company is how they prepare for the change that will result from the increased business impact that IoT will present.

With figures in the trillions in terms of the market for IoT, how do companies ensure they can get a slice of the pie? If they currently do not sit within the relevant market segment, analysis will be required to determine if it can be an opportunity or a threat to their business as a whole.

IDC in 20143 predicted that IoT will actually overtake the Information Communication Technology (ICT) over time. It predicts IoT will grow 12% year on year, whilst classical ICT will grow just 4%. Figure 3 below illustrates this.

Figure 3: IDC Prediction of IoT vs ICT [3]

Considering that most business is consistently monitoring the bottom line, it is not only the opportunities that it will present, but how it will impact how we work. With limitless numbers of sensors monitoring processes, improving business energy efficiency, enabling new ways of working in teams, business will need to be more open to change, and more dauntingly, open to the elements of a “big brother” type scenario.

There are trends that are ensuring an evolution of business practice as we know it. Normally, new technology platforms impact on a single strand, with the exception of the impact of the internet. But IoT has the potential to become an entire business ecosystem, where creating and capturing business value will be paramount. However, this is not a straightforward suggestion. Barriers to this include the current early position of IoT in its lifecycle, and the sheer volume and types of devices to be considered. From an ecosystem perspective, by nature it would indicate a seamless quantity of micro-systems working together in a self-sustaining fashion. Trying to estimate what this will mean for IoT is still not clear.

Consider the classical technology adoption lifecycle. There are five types of innovation adopters, the first being the innovators themselves. The list is completed, in sequence by early adopters, early majority, late majority and laggards. With the current immaturity in IoT, and the lack of clarity in the various emerging technologies, the challenge for business is to try to advance the early adopters to early majority, so the business needs to be able to scale. The early adopters are less fussy when it comes to product design, but once the number of adopter’s increases later in the life cycle, the early majority will want polished product offerings, with appropriate services.

With the IoT still in its relevant infancy, it is appropriate to compare it to the early stages of the Internet. When we look at the recent business ecosystems that have been spun out of the Internet for EMC, such as Pivotal Cloud Foundry, one would postulate about future ecosystems opportunities for EMC from the IoT spectrum.

Another important consideration for companies is to consider the skill-sets and people that are required to drive their Big Data strategy as a result of their growing IoT ecosystem. A key tenant for this will be the data itself, and in the February IT@Cork Tech Talk by my EMC colleague Steve Todd, and even more recently in his blog on data value (value was something I had never associated to data until this talk), Steve spoke to the importance for major companies to begin to consider a more structured approach to their employees that are involved in data set discovery, identification and migration (Data Architect) and also a Chief Data Officer to represent the company from a data perspective. Interestingly, my role in EMC changed last year, to the role of a Data Architect. So I could first hand relate to this. When faced with a business challenge in big data, 5 steps that can be critical to success are as follows.

1: Demystify and then map the current devices, tools, processes and trajectory of data across the business unit or company (AS-IS Diagram)

2: Scour the company and external sources for any technologies that can enable a more scalable and clearer approach

3: Look to centralize data storage, to allow the company to focus on being agile and scalable, and also remove duplicate data (concept of a Business Data Lake)

4: Develop an ingestion framework to ensure the data lake has a sufficient landing platform for data.

5: Build the Analytic’s platform that is pointed at the centralized “Business Data Lake” to meet existing and future needs of the business.

When we apply this to IoT, we start to that every company, no matter how small, will begin to generate huge data-sets, and there will be a new skillet needed at companies that never had previously to ensure they can gain as much insight from the data sets. Sure, there are companies that can provide these solutions, but realistically, the future state will surely be to have these as core skills, just as “internet skills” once appeared on resumes?!

It is proposed here that key stakeholders across multinationals can overcome these challenges and design practical IoT business models if they consider an ecosystem style approach, instead of looking at modular needs of individual business units. This will allow the business to get a high level perspective of where IoT can bring value to their business offerings.

Reference:

3: Digital Universe Article

http://www.emc.com/leadership/digital-universe/2014iview/internet-of-things.htm

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