We have all heard that data is the new oil or the new gold.
The data economy is growing at a staggering pace and everyone wants to get a piece of the action. The amount of data created in the world is surging, From 5 zettabytes in 2013 to an expected 180 zettabytes in 2025. 90% of today's data was created during the last two years only…
New technologies like AI and machine learning require massive amounts of data in order to work properly. When products, software and services are interconnected, data becomes the cornerstone of our digital world. In that sense, it is more a lubricant than a fuel. Now, the application of GDPR (General Data Protection Regulation) provides a legal framework for how data can be used and protected. Defining the rules will protect the rights of citizens, as well as help unlock data’s enormous value.
Data is not like oil. It is not that easy to turn it into money. You cannot just collect it and then distribute it. The analogy is misleading. This conveys the idea that you just need to build up your data lake and then distribute through APIs as if they were simple pipes.
Data has characteristics that makes it potentially more valuable than oil. Unlike oil, value of data is contextual because of freshness, quality, uniqueness, benefits it provides to the data buyers. Value of data needs first to be understood. Data equals to knowledge or know-how. Consequently, determining its value is assessing the results of this applied knowledge. Try to look at data like the specific skillset a new hire would bring to your team.
For instance, why is an engineer specialized in AI better paid than an agriculture engineer? Few proficient AI engineers are available compared to the market demand. It increases therefore the retribution for their skills.
Data is know-how. You can think about acquiring data like adding a person with a specific skillset to your workforce.
Technology does not feel degrees of satisfaction and does not tire as people do. In addition, it does not get used up like raw materials. Data usage is only limited by the use of machines to treat information. In practical terms, data usage is potentially infinite in our digital world.
However, data is not created out of nowhere. It comes from machines, sensors, services owned by companies in a unique position. Indeed, only some data producers can provide precise data about weather information, data car autonomous behavior, detailed information about people, etc. Data is harvested by data producers on devices in a unique position at a specific time. Therefore, it makes data unique. The entry barrier for potential competitors is high. To compete, they would need to be the alternative device used, replacing existing device being a car, sensor or phone.
Products, software or services provide features and capabilities that are “doing something”. A buyer can understand in advance before choosing them.
For instance, a printer is going to print, and a hair dresser is providing a haircut. Even if you do not like your haircut it is still a haircut. Using software, services or products will provide you satisfaction or dissatisfaction. However, you can figure out the range of possible outcomes in advance. In other words, when you buy something what you buy is the perceived and expected satisfaction you will get using it.
With data it is all different. The wide scope of possible results implies risk for the buyer in his assessment of expected satisfaction. Asking for information, he never knows what could be the result. The very same results could lead to satisfaction or dissatisfaction from the use depending on the context (time, location, any other contextual info). Indeed, different data results can have very different value for this buyer. In addition, the very same data result might have very different perceived value for different buyers.
For instance, two persons buy weather data to decide whether they should open their shops and ask shop assistants to come the day after. One is selling ice creams and the other one pullovers. If the temperature is high on the next day, the ice cream seller would open his shop but not the pullovers seller. Hence, one is doing business the other not.
Perceived value of data varies a lot from one customer to another. If you cannot measure and understand the value you provide well, you cannot monetize it well and will not capture a fair share of that value. If the link between results, value and price is not well articulated and communicated, it creates a risk for buyers. The risk they perceive will influence the price they are willing to pay.
Data producers need to do some work to prepare, analyze and derive the customer’s insights from their datasets. Data is unique and its value very contextual. To assess this value, a convenient way is to consider your data as a working skill. Data can be monetized when it provides insights to make a valuable decision creating value. Obviously, it requires a specific practice to capture most value provided for each customer.