SAG_Twitter_MEME_IOT_ Oilfield_Oct16.jpgData is the new oil, as the popular saying goes.  The idea behind this is that just as the discovery of oil sparked the first Industrial Revolution, the “discovery” of data is sparking the fourth Industrial Revolution.

“Wait!” I hear you saying, “Data has been around for a few decades, data is nothing new. Why is it sparking the revolution now?”

Well, like the first Industrial Revolution, it was not so much the discovery of oil itself that drove it. Rather it was the discovery of how to refine the oil into usable products - that could power cars and factories and heat homes -that really got the industry going.

The same logic applies to the computer industry. Although Internet Technology and Operational Technology have been around for some decades, industries only just started to truly understand the value that can be derived from the data coming from their products.

Business Intelligence and Big Data analytics have moved on to advanced analytics, which not only enables insight but also allows for the creation of completely new business models and revenue streams.

But what kind of insights can be derived from the data generated by the products? It might strike some people as odd, but many companies don’t actually know what data they are generating, or could generate, and what they could do with that data.

The thing is, if you find data from which you can derive value it could be so profitable that it pays for the physical devices generating the data. This is the crux of what companies are seeking on a digital transformation journey. Some of new business models based on data often have the following characteristics:

  • Pay as you use. Pay as you use is possible as the producer (and owner) will monitor usage of the product and bill the consumer accordingly.
  • Pay for advice. This is about using the insights that can be derived from the data. Think of medical devices; the data generated could be used to give you advice on your health. Your toothbrush could send the data to your dentist, who can analyze your brushing behaviour and tell you how to brush better for a fee.
  • Pay for unlocking features. The final one that is really interesting is unlocking features or upgrades, whereby you ship one basic version of a product (simplifying production dramatically) and sell it for a low price, then allow people to unlock features or upgrade for new features – for a fee.

So why are companies not embracing these business models? There are two major hurdles to overcome one on a business level and another on a technical level. The business level has all to do with the switch in revenue model, especially the pay per use which has a steep adoption curve. The issue is that from a financial viewpoint you switch from a pay upfront to a pay-as-you-go model, which will have a serious impact on every aspect of business.

Although pay as you go might give a much more robust income stream over a number of years, there is no doubt that it will cause a dip in revenue in the short term. So how will companies compensate for that loss in direct income? Will shareholders accept that? The current understanding is no, not really.  

From a technical angle there are some serious issues as well.  IoT is very much about the infrastructure of hardware (Things, gateways, routers, network, servers) and software (firmware, device management, analytics, integration, APIS and applications) that needs to be put in place before benefits can be really reaped.  The issue with this is that it is hard for the responsible team to get a proper business case on a single proposal. It is like asking people to buy a fire truck without having the road to drive it on.

It is my opinion that if these challenges are not properly addressed companies that are on the quest to explore an oilfield could well find themselves lost in a minefield.




Digital Transformation


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