With so much data at our fingerprints, ensuring it is safe and secure is a major component for any organization. Research shows, 57% of consumers will stop doing business with a company that has used their personal data irresponsibly.
This article features key takeaways from the Data Chats podcast episode featuring Kirsten Martin on the ethical use of data in organizations. Kirsten Martin is a professor of IT, Analytics and Operations in the Mendoza College of Business at the University of Notre Dame, and author of Ethics of Data and Analytics: Concepts and Cases.
Evolution of Data Privacy
Data privacy has been around for a while, and has evolved overtime. Initially, if someone was putting a huge data set in a system, it was expensive and organizations had to hire system consultants to look at the business requirements. Today, that same type of computing power and data analytics can be done almost off the shelf pretty quickly.
However, there are some things that differ. There is some disconnect between the data used and the person that is actually the subject in the data. That type of distancing from the collection of data puts a strain on thinking about new things. Especially around privacy and around not knowing who’s in the data and the problems that might be introduced.
Market Pressure with Data
The generation thirty and below are very aware of the power dynamics between a corporation and a user. When they see the impact within a business on disadvantaged people, they don’t like it nor approve of it. This is a place where companies need to look into as employees are seeing the use of their data analytics programs and would walk out if things are not ethical.
The employees of enterprises are also putting market pressure on their own organizations. It’s a generation that really understands the importance of ethics.
We also see the market pressures of organizations where third parties need to come in with regulations in order to make businesses internalize the harms they may be creating. At times, regulations can be clunky and it might be more beneficial to find a solution that makes them see the wisdom of creating better results for the issue.
Discuss Business Ethics
Organizations need to be open and transparent while encouraging employees to ask questions and spend time discussing ethics within the business. Additionally, knowing who the stakeholders driving data-driven decisions are and how ethics impacts them is key to ensuring alignment.
There is a book written by Ari Waldman called Industry Unbound, and it’s mainly about the fact that many times we put data ethics and privacy questions in the general counsel’s office, which can be inappropriate at times as they are not the decision makers, but can provide valuable advice. It’s important to keep in mind the people most impacted by the decisions made.
Ethical vs. Legal
Oftentimes, people confuse legal with ethical, but there is a huge difference between the two. Legal can explain the minimum and usually understands the spirit of the law to explain where you are in the gray zone. It can be useful in explaining why organizations may also get into ethical problems. However, there are times when organizations don’t want to go by just the bare minimum of the law.
Legal is interesting but around business decisions, oftentimes it’s not interesting enough as it doesn’t tell businesses what to do, it just says what not to do. There are many directions an organization can move forward strategically and it’s more of an ethical question of “what’s in the best interest of the company?”
Engage with Ethical Data
Knowing there are answers, asking and engaging in conversations is key for staff. This will help employees get more educated on why certain decisions were made. It’s also important for people to start thinking “what data shouldn’t we use and why?”
Staff will understand why certain data was not used or was left behind and the reasons behind specific decisions. Additionally, it gets people to start thinking through other questions that might help based on the data at hand.
Here are a few sample questions to get your started:
- What data hasn’t been used?
- What features of the data haven’t been used?
- Why was some data left behind?
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