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#4 Consuming products or services?

Products in data world

Let's start with what is a product? A product is an item that can even be sold or used. Usually, a product refers to something physical or virtual that you can give or own. If this universal definition is translated into the data world, then it could mean an Excel file that contains contact information for customers, which you can buy from a vendor. Or it could even mean database copies where there is location information about potential customers that you can upload to your own CRM system for marketing.

There are pros and cons to product thinking. Today, as more and more data is being collected, globalization is accelerating and the boundaries of countries and industries are blurring, data is becoming obsolete quickly, so it may not be worth collecting for yourself as a product. Outdated or poor quality data is problematic throughout when quality services or decisions based on the data should be made.

Services in data world

So what about the definition of service? Everything in the world is being servitized. A service is often defined as a thing that is produced and consumed at the same time, based on need. So only when something is really needed is that service procured. Examples could be barber services, modern forms of mobility and digital entertainment (streaming) services. These are usually paid a monthly fee or simply according to actual consumption, for example. In the data world, this definition would mean that, as a customer, you would be able to get the data you need, when you need it, in the right format and at the right time, for which you pay according to usage. On top of that, a product is usually something you buy and then own when you only consume the service and enjoy value when you need it.

So there is a big difference between data products and data services in their fundamental logic and this will change the way we all use, acquire and value data in the future. The megatrend of service deployment is also spreading to the data economy. As you can read from our other blogs, outdated and poor quality data is an expensive price for a company. In addition, when data is used, or when it is needed, it does not need to be stored for itself or passed on from other life-cycle costs, it is clear that real-time data flows and Data-as-a-Service actors are raising their heads around the world.

In future blogs we will discuss data products and data services more in depth but for now, you need to think thought how you should be prepared for such a shift. Are you taking this into account in your data strategy and data infrastructure?


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