How do you evaluate data analysis risks for different groups?

Evaluating data analysis risks requires a multi-dimensional approach. For objectives and assumptions, alignment with business goals is crucial to avoid strategic errors. Data sources and methods' credibility and ethical integrity can't be compromised. Constant vigilance for biases or errors in output is needed, and validation methods should be in place. Reproducibility and interpretability are vital for transparent and actionable findings. Communicate results effectively to stakeholders and use them for data-driven decision-making. This holistic scrutiny helps mitigate risks for all involved parties—data owners, analysts, subjects, and users.

It is of vital importance to have encrypted data: when doing financial risk modelling, you are always working with particularly sensitive data. That is why the first job is to encrypt customer data to maximise protection while achieving the expected quality.

Evaluating data analysis risks for different groups requires a comprehensive understanding of potential biases, privacy concerns, and interpretational challenges. By conducting thorough assessments tailored to each group's context, considering ethical implications, and implementing robust data governance practices, one can mitigate risks effectively. Regularly reviewing and adapting risk evaluation strategies ensures a nuanced and responsible approach to data analysis across diverse user groups.

I always work on a data table, which contains information about the use, development, extent and limits of the data. I work with different roles and permissions so that the team is aware of who and what can be accessed. It is also relevant to have a calendar with a timing on the use of the data, in order to keep the use of the data to a minimum. Data bias is avoided in the first transformation by removing the nominative character.

The General Data Protection Regulation introduced the explicit consent of consumers on the destination of data. This made it a challenge to analyse data, which incorporated detailed information on the destination of the data. If ARCO rights were a conquest in the first generation, explicit consent in the second, today we have to face the challenge of Artificial Intelligence and the vast amount of data it provides. The question arises as to the ownership, responsibility and storage of this data.

Implementing a DLP system adapted to the size of the company is the main way to avoid a data leakage. In the risk dashboard, this should be included in the Operational category because of its importance. Access must be restricted, creating profiles with different hierarchies. Training is another fundamental aspect that must be emphasised for the whole team, regardless of their role.

Risk in data analysis can be built on 3 pillars: - Team: All trained and with different roles. - Information: Included in the operational risk dashboard, in a unified way for the whole team. -Action: Implementing a DLP is the best tool to mitigate any risk giving security and certainty to the team.

In my experience, data analytics has been helpful, as stated previously, there are minimal risk, but more importantly, the information that gets exchanged that increases consumer knowledge is greatly needed, and when used properly is detrimental to advance the consumer experience.