Question

What are some of the ethical considerations associated with data analytics?

Answer 1

Data analytics is the process of inspecting, cleansing, transforming, and modeling data in order to draw useful information, informing conclusions, and supporting decision-making. It refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.This technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

The ethical considerations associated with data analytics are as follows.

1. The types and volumes of data describing individuals and organizations expand continueously, and this trend will continue as sensors make their way into more of the day to day items we use. There are very thin lines that separate the use and misuse of data, and some industry associations are addressing the issue head-on with ethical guidelines. While organizations usually have stated privacy policies.

2. Laws and regulations guide organizations, particularly around privacy and the use of data, defining the current “nogo” areas for an organization. However, recent advancements in analytics a technology has widened the gap between what is possible and what is legally allowed, changing the balance of power between individuals and the data collectors.   Within this gap are new opportunities alongside the risks of public relations disasters and unintended consequences. And it is within this gap where the ethical questions around what is acceptable are raised.

3. The offenses such as fraud are clearly unethical, there is a lot of gray area when it comes to the collection, use, and analysis of data. Ethical guidelines, laws, statutes, and regulations may draw many lines. Even so, questionable situations can arise at various stages of the data life cycle that can confound reasonable people and expose their organizations to risks. The Data Science Association has established the Data Science Code of Professional Conduct to help data scientists navigate tricky situations.

4. Examples of good and bad practices are emerging in the industry and in time they will guide regulation and legislation. The choices we make, as practitioners will ultimately determine the level of legislation imposed around the technology and our subsequent freedom to pioneer in this exciting emerging technical area