Our relationship with data is ever more complex. We people create a lot of data with our activities, we can also be identified from data which something we would like to deny or at least have some control of it.
The current global digital social media-driven economy is not based on buying products or services with euros, dollars, or even bitcoin. Instead, we pay with data about us, personal data.
Companies wish to have that data in order to pinpoint marketing more accurately, use our data in building AI-driven algorithms and feed that same AI with our data in order to discover new benefits from it. Scholars use the term gentrified in this context. They say that individuals’ personal data is gentrified; they are unaware of what information is gathered and marketed, and they can not use it for their own benefit.
individuals’ personal data is gentrified; they are unaware of what information is gathered and marketed, and they can not use it for their own benefit.
The intentions of all operators are not always benevolent nor using the data to offer us better services. Often the discussion dwells around the juxtaposition between personal data and industrial data. That is a rather limited approach. If we take even a slightly more open approach we can see that our personal data are not “challenged” or “threatened” by companies alone or that opposite of personal data would be industrial data.
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What is forgotten often in debates is the fact that not only companies should be challenged regarding the use of personal data. Governmental organizations, as well as 3rd sector organizations, use data in services they provide for us or use data about us to provide services for others. Not all data about you created or possed by the government are public domain or accessible by you.
Industrial data
Let’s have a look at the industrial and personal data first. Industrial data often refers to a large amount of diversified time series generated at a high speed by industrial equipment. It is also often used as a synonym for big data. Big data refers to data generated in high volume, high variety, and high velocity that require new technologies of processing to enable better decision making, knowledge discovery, and process optimization.
Personal data
What about the claimed opposite, personal data? All data related to an identified or identifiable person are personal data. We all have become familiar with GDPR which sets the personal data ground so that as long as the data can be used to identify persons directly or restored to an identifiable format, they constitute personal data and are subject to the GDPR.
Culture of fear in the data economy
One of the questions in the Data Product Toolkit process is “Does the data contain GDPR data?” The situation leads to development in which lawyers in companies are consulted more and more by CIOs, CEOs, and anyone making decisions on data. The lawyers often play safe and that results in saying “yes, that can be considered personal data in some situations”. That in turn results in not using the data since getting permissions from millions might be difficult and slow. In short, the fuzziness of boundaries enables a culture of fear in the data economy.
Now think of the world between the two extremes industrial data and personal data. We have no difficulties knowing that for example Telephone numbers, Identity card number, Car registration numbers, Positioning data, patient records all are personal data. Few people understand that for example, IP addresses and pet's veterinary records are personal data as well as data on the hereditary diseases of the person's great-great-grandparents. The further we slide from the most obvious boundaries of what is personal data and what is not, the more reluctant we are to utilize the data in anything. If the situation is blurry, it is considered a financial and brand-related risk since GDPR contains sanctions that are not small. This fuzziness of what is personal data and not is working against the growth of the data economy.
Long journey has begun
The data spaces situation resembles the evolution of code as well. The situation and fuzzines of what is open and free regarding source code and relation to proprietary code took decades to clarify. The analogy is not 100% fit, but those familiar with it can easily understand that this is another bigger long-termThe long phase in the economy and societies.
Incorporate personal data management in the framework
An ongoing debate exists over whether the distinction between personal and non-personal data is enduringly useful. Some argue against the distinction because de-intentification is not truly possible, while others maintain that it is. More and more of the data in the data economy is and will be of the kind that can reasonably be used to identify a natural person. The fuzziness in what is personal data and what is not is a threat to your business if not handled properly. For these reasons, personal data use and management is one that ought to be incorporated into any regulation or governance framework intended for the broader data economy. Manage or at least prepare to manage personal data as a maximizedbuilt-in function now and you’re future risks are minimized and business opportunities maximised.
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