While researching and writing this topic, keep in mind the Logical Model to differentiate between input, activities, output, outcome, and impact.
The analysis should focus on the voluntary sharing of customer information, what kind of data is being collected and shared, and how this impacts the customers.
The introduction should show how data privacy has become a human right (UN - OHCHR), and it must highlight the importance of data privacy, as well as the impact that data sharing has on society.
If relevant, provide more information about how privacy breaches/abuse contribute negatively to society and their impact. This will help the reader make an educated assumption about the impact the company has.
It should assess the amount of personal data sharing that happens, along with the consequences of it.
The focus should be on how this impacts customers - not how it may impact business or profit, although financial/business growth incentives can be included as a motive for sharing sensitive information.
Try to tailor the introduction to the specific industry in which the company is.
Read more on how to build a strong introduction in this article.
The core analysis needs to address how much data is collected from customers (and sometimes employees), whether it is sold to third parties, or if third parties have access to it.
The number of customers should be stated, and whether this is a systematic process they do with all customers, or whether specific ventures with portions of their customer base’s data are compromising privacy (or both).
It is important to address what kind of information is being collected and distributed - whether it is financial data, data on activities, etc.
You should also explain what kind of third parties are getting access to the information, what they might do with it, and how this might affect the customers involved.
Focus: voluntary selling and sharing of users' data
The core of the analysis needs to address the type of data collected, how much data is collected from customers (and sometimes employees), with whom it shares the data, and why.
Also, it should show strong links with the social consequences of collecting, storing, and/or sharing personal data. You can look for fines given by the GDPR, which will show that these companies haven’t protected users’ data privacy rights.
Note that an infringement or breach of contract/Terms & Conditions is not always necessary to analyse the impact of the company’s actions, products, services, or policies. You can still measure the impact of Facebook and the likes, who sometimes abuse their users' privacy by using the information they held on them without their full understanding of its use.
In your analysis, try to go beyond reporting the voluntary selling of data and measure the social impact it has had on individuals. You can use studies as proxies. Learn more in the article Step 5: Assess scale and value.
Remember: only half of the consumers are aware of settings that control data collection!
What do companies do with our personal data once collected? And, what is the impact?
1. The data is grouped and analyzed to personalize advertisements for customers → Possible impacts: spam, frustration, feelings of intrusion and of being watched from targeted advertisements, data tracking which can also incentivise users to spend more money
2. The data are listed and analyzed for R&D purposes → Possible impact: using data for “social experiments” for R&D purposes (i.e., Facebook using data to experiment with users’ emotions), and going beyond academic research, can cause more widespread harm, such as global manipulation (i.e., Cambridge Analytica scandal).
3. The data is sold to a data brokerage → Possible impact: from buying and selling data, third parties can misuse sensitive information.
There’s a difference between misuse and loss. How do companies keep data misuse under the radar?
- Loss: someone with bad intention “steals” personal data without permission.
- Misuse: someone’s data is legitimately collected (by a company) but used beyond its original purpose.
Companies keep data misuse under the radar by means of:
1) commingling: data reused for another purpose than initially intended,
2) personal benefit: data abuse without bad intentions (e.g. an employee saving data on the PC),
3) ambiguity: the company is unclear about what it does with personal data so that interpretation is vague.
Overall, there’s an increase in concern about how companies handle customer data. People worry about confidentiality and violation of their private interests.
In your analysis, try to go beyond reporting the voluntary selling of data and measure the impact it has had on individuals (social - a sense of intrusion; political - manipulation and polarisation; economic - push to consumption).
What kind of information is being collected and distributed? Why does the company do this? Look at the policies on privacy.
How many customers are affected? Is this a systematic process, or does it happen to only a specific part of the customer base (or both)?
Is the company sharing the data? What kind of third parties are accessing the information? What are they doing with it? How might this affect the customers involved?
You can measure the impact by answering the following questions: How much money was made from targeted advertisements? Was the company fined for the misuse of customer data?
Useful tip: check out companies the GDPR has fined, as well as other fines and scandals on the matter, to find out those who have significantly abused of their customers’ data.
Caution: For the social impact, think of companies selling customers’ data to other companies for targeted advertisements and other commercial purposes. Don’t consider companies using customer data to optimize their own processes and service.
Secondary: Significant data breaches
If significant, discuss data breaches and similar cybersecurity issues as a secondary point.
Beyond negatively impacting the company, data breaches and cybersecurity attacks have short and long-term consequences on employees/clients/consumers’ lives. They can result in identity theft, fraudulent credit card activity, and emotional challenges such as stress on a more interpersonal level.
An analysis of one specific breach (data breach or privacy breach) takes the risk of being anecdotal. We do not want to report every single breach that a company has faced. This kind of event is only useful if it serves to illustrate a broader issue, e.g., a history of repeated dishonest behaviour, neglect, and lack of regulation from the company to many of its clients/employees/users.
Unless the breach and underlying wrongdoing are so significant that it would in itself justify a dedicated analysis, you should broaden the impact analysis to the larger issue, not just the specific breach. Read more here on finding the right granularity level.
The exception to this rule is if the company is in the financial sector (Commercial Banks, Consumer Finance), and there is direct impact data available about monetary loss and/or identity theft.
The ideal situation (after discussing the voluntary selling/sharing/use of data):
The analysis shows that there is a history of breaches (showing negligence and oversight from the company).
A significant number of users' data was breached (i.e. 10,000+ people getting their data leaked).
The analysis includes direct data showing customers’ monetary losses or other impacts such as identity theft.
The acceptable situation:
If there is no direct data available, you can use proxies to show a monetary loss or identity theft.
We can accept a case where less than 10,000 people were affected, but only if direct data exists (i.e. X persons got Y amount of money stolen).
For both topics, make sure to describe the scale of the impact by taking into account:
The impact is not just the number of people affected, but how they were impacted. It is not because the data of, for instance, one million people have been exposed that the impact is necessarily significant. An analysis can discuss a case concerning a few hundred/thousand people if there is a tangible outcome. The impact is considered significant when this number of people (few thousand) have been victims of monetary losses, identity theft, etc., and this has been quantified.
1/ The breadth of the impact
Is the impact local, national, or global?
How many people are affected? Thousands? Millions? Billions?
2/ The depth of the impact
Is the life of people concerned deeply affected, or does the issue just marginally impact them?
Are the changes brought by the issue profoundly changing society?
3/ The persistence of the impact
You can also use studies on the impact of data privacy as proxies. Learn more in the article Step 5: Assess scale and value.
Key points to remember:
It is important to remain critical and nuanced. In your analysis, make sure you are not repeating the company’s CSR report.
The analysis should be as comprehensive as possible and include all data sharing cases and data breaches. Show the company’s track record (persistence).