Ethical Use of Big Data
Big Data, a market predicted to grow exponentially in the next few years, is playing an important, albeit it behind-the-scenes role, in many of our most exciting and revolutionary technologies. Artificial intelligence and machine learning rely on big data analytics, while the Internet of Things both provides and utilizes troves of Big Data, and organizations of every kind are recognizing the value of Big Data and finding new ways to put it to work for stronger marketing, better customer engagement, superior product development, and much, much more. But a less glamorous side of Big Data still weighs heavily on the field, that of the ethics around Big Data and Big Data analytics. One important factor for consideration is the trust we put in the information we’re collecting, along with the faith in its final analytics; another issue revolves around personal data privacy issues and just how comfortable we should be with the collection and analysis of such detailed data about private, corporate, and government interactions, often to the minuscule scale.
Reliable Analysis: Quality versus Quantity
Researchers and analysts are finding the necessity of quality data of particular importance, highlighting the need for avoiding quantity over quality. Though it’s possible to collect and access vast amounts of data, more and more analysts are finding that the results attained from the analysis of such across-the-board data are not, in fact, reliable or insightful. For significant acumen, it’s increasingly important that the data used is precisely honed to key classes, but as important is the fidelity of the data in use. Delivering knowledge based on too-broad, inaccurate, or poorly defined data leads to ‘bad’ information that is unreliable and likely to produce weak, or possibly even negative, results when employed; it’s also likely to introduce incorrect beliefs that have the potential to spiral into further blunders.
Strictly Ethical Use of Big Data
Thanks in large part to the prodigious expansion of the Internet of Things, data generation, collection, and access too have skyrocketed. Currently, such data is being used for a range of services from delivering more relevant customer marketing at critical purchase times to public benefits such as traffic reports to social communications. However, the ethics of such data use is being deliberated more judiciously today as many privacy and security issues come to light, as well as the debate as to just how much personal information one actually wants corporate and governmental organizations holding about each one of us. Those organizations collecting data should be held accountable for the safety of these compilations, but as relevant, it’s necessary that each of us understand the implications of data collection and knowingly provide consent for the gathering, storage and use of the data. Too often none of these things are happening.
(Infographic Source: Eventsforce)
Privacy policies tend simply to be agreed to without any serious inspection, and regulators haven’t yet caught up with the expansion of Big Data technology to properly address and discipline poor data accountability on the part of collection agencies.
For the time being, a few fundamental principles of ethical data usage have been suggested, and it’s necessary that both those supplying the data and those collecting it put some sort of strategy in place to ensure adequate protection. Data suppliers, more and more, need to be aware of what they’re agreeing to, and data collectors have a responsibility to make this transparent. Furthermore, we can ensure ethical data use by determining if the collection and usage of data is, in fact, of benefit; collecting and using only that which is necessary is further advantageous; and maintaining an attitude or respect for those who provide the data as well as a fair use policy go further to ensuring ethical data usage. Big Data and the analytics thereof is a complex and progressing field, but the safer we make it now, the stronger its reliability and future employment will prove.
By Jennifer Klostermann
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