Like many who are unfamiliar, I define it as getting out of the realms of low, medium, high, critical and calculating risk and enumerating risk, specifically in an empirical numeric way because then you can actually do an essential roll-up view of what your overall portfolio of risk is.
When we add data risk quantification to the equation, quantifying risk is infinitely more difficult especially with unstructured data. Why? With many data sources on premise and in the cloud, there are too many variables to manage. However, with a common framework such as Data Mesh, it provides a common framework to enable consistent data risk quantification.
Driving Risk Up Or Down, Not Minimizing Risk
Once there’s a common structure, you can begin to quantify the asset with a points-based system that identifies the following:
- how much an asset is worth
- how mission critical is it
- how much regulatory scrutiny it is subject to
Then, you can rank them and consider actionable risk.
How that applies to data products in a Data Mesh is that you can start optimizing the risk and value of data products because when users see the overall risk portfolio, data product owners can decide whether to drive the risk up or down. It’s a little counterintuitive because, when we think about risk, we might immediately think that our objective should be to reduce risk and that’s not a practical approach through the prism of risk management.
Now, with a common language and point system, it’ll impact the organization in two very critical areas: data democratization and data monetization.
CDOs and data managers often struggle with data democratization, particularly with the fear that they don’t have all the information to make decisions. For instance, you want different departments to share data, except there are critical questions that also impede decision making such as: What should I share? What can I share? What happens if I share?
If you can quantify the risk and even better, quantify the value in a way that they can understand, it can immensely impact data democratization.
Next and perhaps one of the most important impact: data monetization. As we know, there are many drivers to monetize data: increase customer retention, create a new revenue stream, enter new markets and so much more. But before organizations can monetize data, the organization needs to enable the quantification of the value and risk of data.
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