Big data refers to the massive amount of information that a company collects pertaining to its operations. As more business owners use big data to become familiar with the needs of their customers, more issues surrounding ethics and privacy with big data arise. Fortunately, there are some steps that managers can take to ensure that they handle their data in an ethical manner. Here are four ways to handle big data ethically.
1. Allow Customers To Own Their Data
When possible, it is a good idea to let customers own the data that they give to a company. For example, if a business has a survey on its website, the website should make it clear what they are using with the collected information and give customers an opportunity to opt out of its collection.
If the data collected can be tied back to a customer’s personal information, provide customers with instructions for removing their data from the database, if they wish. Companies that collect DNA to provide customers with ancestry and health information, for instance, sometimes permit customers to contact the company and have them remove their DNA information from their system. Companies can permit this even after the customer previously allowed the company to collect and store their DNA.
2. Always Treat Data Confidentially
In cases where someone could tie any of the data in a dataset back to a particular person, it is extremely important to treat that data as confidential material. For instance, most businesses do not consider email to be a secure and confidential way to send data back and forth because a bad actor can intercept an email message on its way to the rightful recipient. The use of a secure file sharing platform is a safe alternative to email that many companies are starting to use as they deal more with big data. These platforms give users greater control over their files and even allow them to encrypt the data that they send. Encryption refers to the altering of data files to an unreadable state while the files are in transit to the recipient.
Encryption is just one of many methods that can help data owners maintain confidentiality. Other best practices include:
- Locking up the physical servers where the company stores data
- Ensuring that only those who have a legitimate interest in the data can access data files
- Destroying the data files prior to disposal
- Routinely training employees on how to handle confidential information
Companies can get into big trouble and lose the trust of the public if they allow private data to leak. While accidents and mistakes happen, employees should do everything in their power to ensure that large datasets remain secure.
3. Dispose of the Data When No Longer Needed
Deciding what to use the data for is one of the first things that managers should do before beginning data collection. By making this determination, the manager can have an idea of how long they need to keep the data they collect. Another guideline for how long a company should keep data is the amount of time that passes between the last contact that a client or customer made with the company. For example, a financial institution might get rid of a former customer’s data a certain number of years after the customer switches to another bank, except in cases where federal regulations state otherwise.
It is also important for managers properly dispose of the data. For instance, data owners generally consider shredding a hard drive containing large files to be a more secure method of disposal than simply deleting files from the computer. People who are technologically savvy can often retrieve deleted files that ordinary users might think are gone forever.
4. Aggregate Data When Possible
Even with all the proper measures in place, it is still possible for a data breach to occur. Because of this, it is a good idea to aggregate a large dataset, if possible. When someone aggregates data, they end up removing any information that can personally identify a customer.
Using big data can be extremely helpful to companies. However, with the great privilege of using this information comes the great responsibility of handling the data ethically.
