How Businesses Can Navigate the Ethics of Big Data
The growth of big data during the last decade has opened the door to a lot of opportunities and threats alike. Big data is not just big and powerful, it is also prone to errors. We can currently process more than terabytes of data at a lightning and superficial speed. This presents a lot of opportunities but also means that we are at risk of making bad decisions in a short period of time, with an impact greater than what mankind had ever imagined in the past.
Besides the threat of bad decisions and their impact, people have started placing too much faith and trust in technology, something which we might end up ruing if a conundrum arises.
The Framework That Should Be Considered
The use of big data has brought with it myriad opportunities that offer ease of access for both researchers and organizations looking for reliable data to feed the technology of Artificial Intelligence (AI). Despite these benefits, the ethical aspects of big data should not be compromised. Businesses and organizations using big data and other forms of technology must adhere to the ethical principles that come bound with them. Though the ethical limitations and complications are quite broad, the following principles should be top priority:
- Beneficial: Does the data you use benefit all consumers, as much as it benefits you? This is an important question and should be comprehended before initiating a big data campaign. The first and foremost principle for the ethical use of data is to ensure that the whole process is done with the intention and expectation of providing tangible benefit to both the users and the researchers. In an ideal situation, all concerned parties that are involved in the process should benefit from it.
- Progressive: Is there a pattern present that enforces a culture of data minimization and continuous improvement in this regard? Progress over time is a principle that should not be undermined. The value of progressiveness in this regard can be summarized in the following ways: Continuous improvement and innovation or the ability to deliver better results over time and minimizing the use of data. Businesses should aim for a stage where they can reach their desired objectives by using a minimal amount of data. This can only be achieved by understanding that minimizing data usage is extremely necessary for less risky and sustainable forms of analysis. Following progressive principles highlighted above will help eliminate hidden correlations such as the disenfranchising of certain individuals based on their demographics.
- Sustainable: Another important question that should be comprehended and duly answered is whether the insights being gathered by the data are considered sustainable over the passage of time. Insights garnered through big data should provide value for a large period of time. Data analysis that fails to be sustainable may be useful and relevant during this day and age but will be useless as time passes by. The sustainable nature of the articles is an important ethical principle that eventually sets the basis of how effective the data is over time and how efficient it is with respect to its value in the future.
- Respectful: Have all organizations considered it binding to be inclusive and transparent during the data collection stage? All stages pertaining to the organization of big data should replicate this concept. From collection to processing and analytics, transparency should be a must. The advent of device generated data that is captured in real time actually decimates the current norms for big data. The individual who actually was the originator of the data is affected the most, as even seemingly simple decisions can have massive implications.
- Fair: Has the organization considered the impact their data collections will have on all concerned or interested parties? Privacy protections may have increased, but we know that they may not do a lot in protecting your information. Big data analytics often end up compromising the identity of an individual to determine who they are before they can even make up their own minds. It is time that major stakeholders in the big data market shared some insights and discussed the kind of predictions and inferences that should be allowed and the kind that should best be refrained from.
The Intricate Details of Data Privacy
The laws regarding data protection and privacy differ from country to country all across the world. The EU has an authentic set of laws pertaining to this matter, but they are visibly different than what the United States has. Privacy within the EU is often said to be stronger than what it is in the U.S. Although the myths may exaggerate the difference, the EU is miles ahead of the U.S. when it comes to stringent data and privacy protection. Privacy is considered a fundamental right for all individuals living in the EU. Details about privacy and data protection are discussed as much as gun control in the U.S. The U.S. does have privacy protection problems, but the crux of the matter is that these laws are separate for both the governing bodies.
The diversity in laws concerned with data protection in numerous countries puts forward the notion that there is a need for globally-accepted norms that govern how privacy and protection are provided to users and their data. The globally accepted norms will set the standards and a pathway for others to follow when it comes to data protection.
Here are three important principles that can eventually lead to the development of big data norms in the future:
- Shared Information can remain confidential: Just because consumers share trusted information over numerous platforms does not mean that it is utilized for the sake of analytics. Shared information such as financial data, medical data, and address book data is best kept confidential.
- Transparency in Big Data: For big data to be effective and within the realms of legality and ethics, it is pertinent that data owners have a transparent and fair view of how their data is being treated or sold.
- Big Data Predictions: Big data predictions should be limited. Big data inferences and predictions that compromise the identity of individuals should not be allowed.
The basic need for regulations and the ethical framework is the pragmatic approach recommended for businesses. Big Data will set the tone for how research and analytics are done in the future, but the fact of the matter is how an approach can be formulated to fulfill the criteria we mentioned in the article. You can learn more about big data and analytics and the hundreds of courses Simplilearn offers, here.
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