Big Data

Big Data Ethics – The Dos and Don’ts of Big Data Analytics.

Big Data Analytics enables, organizations and individuals  to understand their business well and to reap the huge benefit that follows it. It brings personalized services, efficient use of resources, prevention of failures, and detection of abuse and fraud. Laws and policies guide organizations, especially around the use of data and privacy. However, the emergence of the Big Data concept has widened what is legally allowed and what is possible. Within this gap, there are new risks and opportunities. It is within this gap where people are raising ethical questions about what is acceptable and what is not. As a Fintech Business, be ready to follow these Big data ethics strictly since money and personal private information are often involved , unless you want to attract legal actions.

Examples of bad and good use of Big Data will determine the type of legislation that will be imposed in this industry. For us, Big Data ethics should relate to several principles that can lead to the formation of Big Data Norms.

Privacy should be respected

Ensuring data privacy is a matter of defining and implementing information rules. People should be allowed to manage their private information in huge, 3rd party analytical systems with security.

Confidentiality

 Shared private information should remain confidential. Some good data is generated by the help of services we trust (e.g. pictures, address books, Wi-Fi location tracking of cell phones and cell tower). But just because we generate and share information, it doesn’t mean we should not keep private information confidential.

Transparency

Transparency is required in Big Data. When data sets produce new inferences and predictions, it leads to data being a business. Some people such as data brokers gather huge volume of data about people, often without their consent and share in a way they don’t want. For Big data to operate in ethical terms, people need to see a transparent way of how their data is being used.

Identity

 By permitting institutional surveillance to determine who we are, big data analytics do compromise people’s identity. Therefore, we need to think about the type of Big Data inferences and predictions that should be allowed.

Organizations and individuals should be careful in their use of Big Data. They should consult widely and create policies that record the conclusions they have come to. For people who want to develop Big Data ethical policies, they should consider the following facets:

  1. Reasonable – Is the data used reasonably?
  2. Consent and Choice – Are there choices that are given to affected parties? Do they understand the implication of their consent? Do they have any opportunity to decline? Are there alternatives?
  3. Context – What was the purpose of data originally? What is the purpose of the data now? In its new use, how far has it been removed from its original context? Is the new use appropriate?
  4. Substantiated – Are data sources appropriate, complete, timely and complete for the application?
  5. Owned – Who is the owner of resulting insights, their duties towards it in term of the obligation to act and its protection?
  6. Fair– Is the use of data fair? Are all parties affected properly compensated? How equitable is data use?
  7. Considered – Are there consequences of data use?
  8. Accountable – How are unintended consequences and mistakes detected and repaired? Is it possible for interested parties to check any results that affect them?

In conclusion, there is a significant potential for Big Data to have positive impacts on us. However, this will happen if organizations are willing to set standards, guidelines, and rules. It is time for consumers and firms to engage in an open debate about data ethics, in order to set expectations and boundaries.

READ THIS :  The Impact of AI in Big data testing
Comments
To Top

Pin It on Pinterest

Share This